Knowledge Graphs: Opportunities and Challenges

Abstract

With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world. It has been well-recognized that knowledge graphs effectively represent complex information; hence, they rapidly gain the attention of academia and industry in recent years. Thus to develop a deeper understanding of knowledge graphs, this paper presents a systematic overview of this field. Specifically, we focus on the opportunities and challenges of knowledge graphs. We first review the opportunities of knowledge graphs in terms of two aspects: (1) AI systems built upon knowledge graphs; (2) potential application fields of knowledge graphs. Then, we thoroughly discuss severe technical challenges in this field, such as knowledge graph embeddings, knowledge acquisition, knowledge graph completion, knowledge fusion, and knowledge reasoning. We expect that this survey will shed new light on future research and the development of knowledge graphs.

Full text

Knowledge Graphs: Opportunities and
Challenges
Ciyuan Peng1, Feng Xia2*, Mehdi Naseriparsa3
and Francesco Osborne4
1Institute of Innovation, Science and Sustainability, Federation
University Australia, Ballarat, 3353, VIC, Australia.
2School of Computing Technologies, RMIT University,
Melbourne, 3000, VIC, Australia.
3Global Professional School, Federation University Australia,
Ballarat, 3353, VIC, Australia.
4Knowledge Media Institute, The Open University, Milton
Keynes, MK7 6AA, UK.
*Corresponding author(s). E-mail(s): f.xia@ieee.org;
Contributing authors: ciyuan.p@outlook.com;
m.naseriparsa@federation.edu.au;francesco.osborne@open.ac.uk;
Abstract
With the explosive growth of artificial intelligence (AI) and big data,
it has become vitally important to organize and represent the enor-
mous volume of knowledge appropriately. As graph data, knowledge
graphs accumulate and convey knowledge of the real world. It has
been well-recognized that knowledge graphs effectively represent com-
plex information; hence, they rapidly gain the attention of academia
and industry in recent years. Thus to develop a deeper understanding of
knowledge graphs, this paper presents a systematic overview of this field.
Specifically, we focus on the opportunities and challenges of knowledge
graphs. We first review the opportunities of knowledge graphs in terms
of two aspects: (1) AI systems built upon knowledge graphs; (2) poten-
tial application fields of knowledge graphs. Then, we thoroughly discuss
severe technical challenges in this field, such as knowledge graph embed-
dings, knowledge acquisition, knowledge graph completion, knowledge
fusion, and knowledge reasoning. We expect that this survey will shed
new light on future research and the development of knowledge graphs.
1
arXiv:2303.13948v1 [cs.AI] 24 Mar 2023

2Knowledge Graphs: Opportunities and Challenges
Keywords: Knowledge graphs, artificial intelligence, graph embedding,
knowledge engineering, graph learning
1 Introduction
Knowledge plays a vital role in human existence and development. Learning
and representing human knowledge are crucial tasks in artificial intelligence
(AI) research. While humans are able to understand and analyze their sur-
roundings, AI systems require additional knowledge to obtain the same abilities
and solve complex tasks in realistic scenarios (Ji et al,2021). To support these
systems, we have seen the emergence of many approaches for representing
human knowledge according to different conceptual models. In the last decade,
knowledge graphs have become a standard solution in this space, as well as a
research trend in academia and industry (Kong et al,2022).
Knowledge graphs are defined as graphs of data that accumulate and con-
vey knowledge of the real world. The nodes in the knowledge graphs represent
the entities of interest, and the edges represent the relations between the
entities (Hogan et al,2021;Cheng et al,2022b). These representations uti-
lize formal semantics, which allows computers to process them efficiently and
unambiguously. For example, the entity “Bill Gates” can be linked to the entity
“Microsoft” because Bill Gates is the founder of Microsoft; thus, they have
relationships in the real world.
Due to the great significance of knowledge graphs in processing heteroge-
neous information within a machine-readable context, a considerable amount
of research has been conducted continuously on these solutions in recent years
(Dai et al,2020b). The proposed knowledge graphs are widely employed in
various AI systems recently (Ko et al,2021;Mohamed et al,2021), such as rec-
ommender systems, question answering, and information retrieval. They are
also widely applied in many fields (e.g., education and medical care) to benefit
human life and society. (Sun et al,2020;Bounhas et al,2020).
Therefore, knowledge graphs have seized great opportunities by improving
the quality of AI systems and being applied to various areas. However, the
research on knowledge graphs still faces significant technical challenges. For
example, there are major limitations in the current technologies for acquiring
knowledge from multiple sources and integrating them into a typical knowledge
graph. Thus, knowledge graphs provide great opportunities in modern society.
However, there are technical challenges in their development. Consequently,
it is necessary to analyze the knowledge graphs with respect to their oppor-
tunities and challenges to develop a better understanding of the knowledge
graphs.
To deeply understand the development of knowledge graphs, this survey
extensively analyzes knowledge graphs in terms of their opportunities and
challenges. Firstly, we discuss the opportunities of knowledge graphs in terms
of two aspects: AI systems whose performance is significantly improved by

Knowledge Graphs: Opportunities and Challenges 3
knowledge graphs and application fields that benefit from knowledge graphs.
Then, we analyze the challenges of the knowledge graph by considering the
limitations of knowledge graph technologies. The main contributions of this
paper are as follows:
•Survey on knowledge graphs. We conduct a comprehensive survey of
existing knowledge graph studies. In particular, this work thoroughly ana-
lyzes the advancements in knowledge graphs in terms of state-of-the-art
technologies and applications.
•Knowledge graph opportunities. We investigate potential opportunities
for knowledge graphs in terms of knowledge graph-based AI systems and
application fields that utilize knowledge graphs. Firstly, we examine the ben-
efits of knowledge graphs for AI systems, including recommender systems,
question-answering systems, and information retrieval. Then, we discuss the
far-reaching impacts of knowledge graphs on human society by describing
current and potential knowledge graph applications in various fields (e.g.,
education, scientific research, social media, and medical care).
•Knowledge graph challenges. We provide deep insights into significant
technical challenges facing knowledge graphs. In particular, we elaborate
on limitations concerning five representative knowledge graph technologies,
including knowledge graph embeddings, knowledge acquisition, knowledge
graph completion, knowledge fusion, and knowledge reasoning.
The rest of the paper is organized as follows. Section 2provides an overview
of knowledge graphs, including the definitions and the categorization of exist-
ing research on knowledge graphs. To examine the opportunities of knowledge
graphs, Section 3and Section 4introduce relevant AI systems and application
fields, respectively. Section 5details the challenges of knowledge graphs based
on the technologies. Finally, we conclude this paper in Section 6.
2 Overview
In this section, the definition of knowledge graphs is provided first; then, we
categorize significant state-of-the-art research in this area.
2.1 What are Knowledge Graphs?
A knowledge base is a typical data set that represents real-world facts and
semantic relations in the form of triplets. When the triplets are represented as a
graph with edges as relations and nodes as entities, it is considered a knowledge
graph. Generally, the knowledge graph and knowledge base are regarded as
the same concept and are used interchangeably. In addition, the schema for a
knowledge graph can be defined as an ontology, which shows the properties of
a specific domain and how they are related. Therefore, one essential stage of
knowledge graph construction is ontology construction.

4Knowledge Graphs: Opportunities and Challenges
Fig. 1 An example of a knowledge graph. In this knowledge graph, (e1, r1, e2) is a triplet
that indicates e1and e2are connected by relation r1.
In 2012, Google first put forward Knowledge Graph by introducing their
knowledge base called Google Knowledge Graph (Ehrlinger and W¨oß,2016).
Afterward, many knowledge graphs are introduced and adopted such as:
•DBpedia, a knowledge graph that intends to discover semantically mean-
ingful information form Wikipedia and convert it into an effective well-
structured ontological knowledge base in DBpedia (Auer et al,2007).
•Freebase, a knowledge graph which is built upon multiple sources that
provides a structured and global resource of information (Bollacker et al,
2008).
•Facebook’s entity graph, a knowledge graph that converts the unstructured
content of the user profiles into meaningful structured data (Ugander et al,
2011).
•Wikidata, a cross-lingual document-oriented knowledge graph which sup-
ports many sites and services such as Wikipedia (Vrandeˇci´c and Kr¨otzsch,
2014).
•Yago, is a quality knowledge base that contains a huge number of entities and
their corresponding relationships. These entities are extracted from multiple
sources such as Wikipedia and WordNet (Rebele et al,2016).
•WordNet, is a lexical knowledge base to measure the semantic similar-
ity between words. The knowledge base contains a number of hierarchical
concept graphs to analyse the semantic similarity (Pedersen et al,2004).
A knowledge graph is a directed graph composed of nodes and edges, where
one node indicates an entity (a real object or abstract concept), and the edge
between the two nodes conveys the semantic relation between the two enti-
ties (Bordes et al,2011). Resource Description Framework (RDF) and Labeled
Property Graphs (LPGs) are two typical ways to represent and manage knowl-
edge graphs (F¨arber et al,2018;Baken,2020). The fundamental unit of a
knowledge graph is the triple (subject, predicate, object) (or (head, relation,
tail)), i.e., (Bill Gates, founderOf, Microsoft). Since the relation is not neces-
sarily symmetric, the direction of a link matters. Therefore, a knowledge graph
can also be seen as a directed graph in which the head entities point to the
tail entities via the relation’s edge.

Knowledge Graphs: Opportunities and Challenges 5
Fig. 2 Research on knowledge graphs.
Fig. 1depicts an example of a simple knowledge graph. As shown in Fig. 1,
nodes e1and e2darkened in color are connected by relation r1, which goes
from e1to e2. Therefore, e1,e2, and r1can form the triplet (e1, r1, e2), in
which e1and e2are the head and tail entities, respectively.
2.2 Current Research on Knowledge Graphs
In recent years, knowledge graphs have gained extensive research interest.
Plenty of studies have focused on exploring knowledge graphs. This paper
conducts a comprehensive survey on knowledge graphs and lists seven impor-
tant categories of current research on this topic. Fig. 2illustrates a schema
of the most popular research lines regarding knowledge graphs. Among them,
AI systems are services that utilize knowledge graphs for their foundation,
and application fields are domains where knowledge graphs reach. These two
research lines are listed for discussing the opportunities of knowledge graphs.
Another five research lines are five main knowledge graph technologies corre-
sponding to five tasks. In this paper, we introduce these five technologies and
emphasize their limitations to give useful insights into the major challenges of
the knowledge graphs.
Knowledge Graph Embedding: Knowledge graph embedding is one
of the central research issues. This task aims to map entities and relations
of a knowledge graph to a low-dimensional vector space so that it captures
the semantics and the structure of the knowledge graph efficiently (Dai et al,
2020b). Then, the obtained feature vector can be effectively learned by machine
learning models. Three main triplet fact-based embedding methods are as
follows: (a) tensor factorization-based, (b) translation-based, and (c) neural
network-based methods (Dai et al,2020b).
Knowledge Acquisition: Knowledge acquisition, which focuses on mod-
eling and constructing knowledge graphs, is another crucial research direction
of knowledge graph study. Typically, the knowledge is imported from struc-
tured sources by employing mapping languages, such as R2RML (Rodriguez-
Muro and Rezk,2015). Furthermore, the knowledge could be extracted from

6Knowledge Graphs: Opportunities and Challenges
unstructured documents (e.g., news, research papers, and patents) by adopt-
ing relation, entity, or attribute extraction methods (Liu et al,2020;Yu et al,
2020;Yao et al,2019).
Knowledge Graph Completion: Although there are many methods for
constructing knowledge graphs, it is still unfeasible to create comprehensive
representations of all the knowledge in a field. Most knowledge graphs still
lack a good number of entities and relationships. Thereby, significant efforts
have been made for completing knowledge graphs. Knowledge graph comple-
tion aims to improve the quality of knowledge graphs by predicting additional
relationships and entities. The first task typically adopts link prediction tech-
niques to generate triplets and then assigns the triplets plausibility scores (Ji
et al,2021). The second task employs entity prediction methods for obtaining
and integrating further information from external sources.
Knowledge Fusion: Knowledge fusion is also an important research
direction that focuses on capturing knowledge from different sources and inte-
grating it into a knowledge graph (Nguyen et al,2020). The knowledge fusion
approaches are useful for both generating and completing knowledge graphs.
Recently, entity alignment has been the primary method for implementing
knowledge fusion tasks.
Knowledge Reasoning: Tremendous research efforts have focused on rea-
soning to enrich the knowledge graphs, which aims to infer new facts based on
existing data (Minervini et al,2020). In particular, new relations between two
unconnected entities are inferred, forming new triplets. Also, by reasoning out
the false facts, knowledge reasoning has the ability to identify erroneous knowl-
edge. The main methods for knowledge reasoning include logic rule-based,
distributed representation-based, and neural network-based methods (Chen
et al,2020b).
AI Systems: Nowadays, knowledge graphs are widely utilized by AI sys-
tems (Liang et al,2022), such as recommenders, question-answering systems,
and information retrieval tools. Typically, the richness of information within
knowledge graphs enhances the performance of these solutions. Therefore,
many studies have focused on taking advantage of knowledge graphs to improve
AI systems’ performance.
Application Fields: Knowledge graphs have numerous applications in
various fields, including education, scientific research, social media, and med-
ical care (Li et al,2020b). A variety of intelligent applications are required to
improve the standard of human life.
Differing from other works, this paper focuses on surveying the opportuni-
ties and challenges of knowledge graphs. In particular, knowledge graphs meet
great opportunities by improving the quality of AI services and being applied in
various fields. On the contrary, this paper regards the limitations of knowledge
graph technologies as the challenges. Therefore, we will discuss the techni-
cal limitations regarding knowledge graph embeddings, knowledge acquisition,
knowledge graph completion, knowledge fusion, and knowledge reasoning.

Knowledge Graphs: Opportunities and Challenges 7
3 Knowledge Graphs for AI Systems
This section explains the opportunities by analyzing the advantages that
knowledge graphs bring for improving the functionalities of AI Systems.
Specifically, there are a couple of systems, including recommender systems,
question-answering systems, and information retrieval tools (Guo et al,2020;
Zou,2020), which utilize knowledge graphs for their input data and benefit
the most from knowledge graphs. In addition to these systems, other AI sys-
tems, such as image recognition systems (Chen et al,2020a), have started to
consider the characteristic of knowledge graphs. However, the application of
knowledge graphs in these systems is not widespread. Moreover, these systems
do not directly optimize performance by utilizing knowledge graphs for the
input data. Therefore, the advantages that knowledge graphs bring for recom-
mender systems, question-answering systems, and information retrieval tools
are discussed in detail to analyze the opportunities of knowledge graphs. Typ-
ically, these solutions greatly benefit from adopting knowledge graphs that
offer high-quality representations of the domain knowledge. Table 1 presents
a summary of the AI systems that we will discuss below.
3.1 Recommender Systems
With the continuous development of big data, we observe the exponential
growth of information. In the age of information explosion, it becomes chal-
lenging for people to receive valid and reliable information (Shokeen and Rana,
2020;Monti et al,2021;G´omez et al,2022). Specifically, online users may feel
confused when they want to select some items they are interested in among
thousands of choices. To tackle this issue, we saw the emergence of several
recommender systems to provide users with more accurate information. Typ-
ically, recommender systems learn the preference of target users for a set of
items (Wan et al,2020;Zheng and Wang,2022) and produce a set of suggested
items with similar characteristics. Recommender systems are fruitful solutions
to the information explosion problem and are employed in various fields for
enhancing user experience (Quijano-S´anchez et al,2020).
3.1.1 Traditional Recommender Systems
There are two traditional methods for developing recommender systems,
including content-based and collaborative filtering-based (CF-based) methods.
Shu et al. (Sun et al,2019b) and Guo et al. (Guo et al,2020) have compared
and summarised these two approaches.
Content-based Recommender Systems: The content-based recom-
mender systems first analyze the content features of items (e.g., descriptions,
documents). These items are previously scored by the target users (Guo et al,
2020;Xia et al,2014b). Then, the recommender systems learn the user inter-
ests by employing machine learning models. Thus, these systems are able to
effectively recommend trending items to the target users according to their
preferences. Some recommender systems utilize the content of the original

8Knowledge Graphs: Opportunities and Challenges
Table 1 AI systems using knowledge graphs.
AI Systems Approaches Techniques on knowledge graphs
Recommender systems KPRN (Wang et al,2019c) Entity-relation path generation based on user-item
interaction
RippleNet (Wang et al,2018b) Preference propagation
MKR (Wang et al,2019a) Laten user-item interaction
MKGAT (Sun et al,2020) Neighbor information extraction; relation reasoning
Ripp-MKR (Wang et al,2021) Preference propagation; laten user-item interaction
RKG (Shu and Huang,2021) User preferenfce lists-based knowledge graph con-
struction
Question-answering systems MHPGM (Bauer et al,2018) Multiple hop relation reasoning
PCQA (Shin et al,2019) Predicate constraints-based relation extraction
KEQA (Huang et al,2019) Simple question-based triplet construction
EmbedKGQA (Saxena et al,
2020)
Knowledge graph embedding-based multi-hop
question answering
Information retrieval EQFE (Dalton et al,2014) Query knowledge graph-based feature expansion
Knowledge graph based Infor-
mation Retrieval Technology
(Wang et al,2018a)
Query-document knowledge graph construction
CKG (Wise et al,2020) Document knowledge graph construction
EDRM(Liu et al,2018) Integration of semantics from knowledge graphs
and entities from queries and documents represen-
tations of their entities

Knowledge Graphs: Opportunities and Challenges 9
query result to discover highly-related items for the users that may interest
them (Naseriparsa et al,2019b). These systems employ machine learning tech-
niques or statistical measures such as correlation to compute the highly-similar
items to those that are visited by the users (Naseriparsa et al,2019a). Another
group of content-based recommender systems employs lexical references such
as dictionaries to utilize semantic relationships of the user query results to
recommend highly semantically-related items to the users that may directly
satisfy their information needs (Naseriparsa et al,2018;Sun et al,2017).
CF-based Recommender Systems: CF-based recommender systems
suggest items to the users based on the information of user-item interaction
(Chen et al,2020c). CF-based recommender systems infer the user prefer-
ence by clustering similar users instead of extracting the features of the items
(Wang et al,2019b). However, we face data sparsity and cold start problems
in traditional CF-based systems. In general, users can only rate a few items
among a large number of items, which leads to preventing many items from
receiving appropriate feedback. Therefore, the recommender systems do not
effectively learn user preferences accurately because of data sparsity (Bai et al,
2019;Xia et al,2014a). On the other hand, the cold start problem makes
it even more difficult to make recommendations when the items or users are
new because there is no historical data or ground truth. Moreover, because
abundant user information is required for achieving effective recommendations,
CF-based recommender systems face privacy issues. How to achieve personal-
ized recommendations while protecting the privacy of users is still an unsolved
problem.
3.1.2 Knowledge Graph-based Recommender Systems
To address inherent problems of traditional approaches, the community has
produced several hybrid recommender systems, which consider both item
features and the distribution of user scores. Most of these solutions adopt
knowledge graphs for representing and interlinking items (Palumbo et al,
2020). Specifically, Knowledge graph-based recommender systems integrate
knowledge graphs as auxiliary information and leverage users and items net-
works to learn the relationships of items-users, items-items, and users-users
(Palumbo et al,2018).
Fig 3presents an example of knowledge graph-based movie recommenda-
tion. Here we can see that the movies “Once Upon A Time in Hollywood”
and “Interstellar” are recommended to three users according to a knowledge
graph that contains the nodes of users, films, directors, actors, and genres. The
knowledge graph is thus used to infer latent relations between the user and
the recommended movies.
Recently, a great deal of research has been conducted to utilize knowl-
edge graphs for recommendation tasks. For instance, Wang et al. (Wang et al,
2019c) introduced KPRN. KPRN is a recommender system that generates
entity-relation paths according to the user-item interaction and constructs a
knowledge graph that consists of the users, items, and their interaction. It

10 Knowledge Graphs: Opportunities and Challenges
Fig. 3 An example of knowledge graph-based recommender system.
then infers the user preference based on the entity-relation path. The user-item
interaction, which is extracted from knowledge graphs, improves the quality of
the recommendations and allows the presentation of the recommended results
in a more explainable manner. Wang et al. (Wang et al,2019a) also applied
multi-task knowledge graph representation (MKR) for recommendation tasks.
MKR models knowledge graphs based on the user-item interaction. It is worth
noting that MKR focuses on the structural information of knowledge graphs for
learning the latent user-item interaction. Sun et al. (Sun et al,2020) proposed
a Multi-modal Knowledge Graph Attention Network (MKGAT) for achieving
precise recommendations. MKGAT constructs knowledge graphs based on two
aspects: (1) it enriches entity information by extracting the information of the
neighbor entities; (2) it scores the triplets to construct the reasoning relations.
Finally, they applied knowledge graphs that are enriched with structured data
to recommender systems.
Wang et al. (Wang et al,2018b) presented the RippleNet model, which
incorporates knowledge graphs into recommendation tasks by preference prop-
agation. RippleNet firstly regards users’ historical records as the basis of a
knowledge graph. Then, it predicts the user preference list among candidate
items based on the knowledge graph links. Based on both RippleNet and
MKR models, Wang et al. (Wang et al,2021) applied the Ripp-MKR model.
Ripp-MKR combines the advantages of preference propagation and user-item
interaction to dig the potential information of knowledge graphs. Shu et al.
(Shu and Huang,2021) proposed RKG, which achieves recommendation by
referring to the user preference-based knowledge graph. RKG first obtains
users’ preference lists; then, it analyzes the relations between the user’s pre-
ferred items and the items which are to be recommended. Therefore, the
model effectively learns the score of the candidate items for recommendation
according to the candidate items’ relationship with the user’s preferred items.
Many studies have utilized ontological knowledge base information to
improve retrieving results from various data sources (Farf´an et al,2009). Wu
et al. (Wu et al,2013) adopted the ontological knowledge base to extract

Knowledge Graphs: Opportunities and Challenges 11
highly semantically similar sub-graphs in graph databases. Their method effec-
tively recommends semantically relevant sub-graphs according to ontological
information. Farf et al. (Farf´an et al,2009) proposed the XOntoRank, which
adopts the ontological knowledge base to facilitate the data exploration and
recommendation on XML medical records.
Compared with the traditional recommender systems, knowledge graph-
based recommender systems have the following advantages:
•Better Representation of Data: Generally, the traditional recommender
systems suffer from data sparsity issues because users usually have experi-
ence with only a small number of items. However, the rich representation of
entities and their connections in knowledge graphs alleviate this issue.
•Alleviating Cold Start Issues: It becomes challenging for traditional
recommender systems to make recommendations when there are new users
or items in the data set. In knowledge graph-based recommender systems,
information about new items and users can be obtained through the rela-
tions between entities within knowledge graphs. For example, when a new
Science-Fiction movie such as “Tenet” is added to the data set of a movie
recommender system that employs knowledge graphs, the information about
“Tenet” can be gained by its relationship with the genre Science-Fiction
(gaining triplet (Tenet, has genre of, Sci-Fi)).
•The Explainability of Recommendation: Users and the recommended
items are connected along with the links in knowledge graphs. Thereby, the
reasoning process can be easily illustrated by the propagation of knowledge
graphs.
3.2 Question-answering Systems
Question answering is one of the most central AI services, which aims to
search for the answers to natural language questions by analyzing the semantic
meanings (Dimitrakis et al,2020;Das et al,2022). The traditional question-
answering systems match the textual questions with the answers in the
unstructured text database. In the search process, the semantic relationship
between the question and answer is analyzed; then, the system matches the
questions and answers with the maximum semantic similarity. Finally, the
system outputs the answer. However, the answers are obtained by filtrating
massive unstructured data, which deteriorates the efficiency of the traditional
question-answering systems due to analyzing an enormous search space. To
solve this issue, a lot of research focuses on employing structured data for
question answering, particularly knowledge graph-based question-answering
systems (Singh et al,2020;Qiu et al,2020).
The sophisticated representation of information in knowledge graphs is a
natural fit for question-answering systems. Knowledge graph-based question-
answering systems typically analyze the user question and retrieve the portion
of knowledge graphs for answering. The answering task is facilitated either
by using similarity measures or by producing structured queries in standard

12 Knowledge Graphs: Opportunities and Challenges
Fig. 4 The illustration of knowledge graph based question-anwsering systems.
formats (e.g., SPARQL). Fig 4presents an example of the knowledge graph-
based question-answering system. The system answer “Shakespeare” is a node
that is linked to the node “Romeo”. The node “Romeo” is extracted from the
question.
There are two main types of questions in this space: simple and multi-
hop questions, respectively. Simple questions are answered only by referring
to a single triplet, while multi-hop questions require combining multiple enti-
ties and relations. Focusing on simple questions, Huang et al. (Huang et al,
2019) proposed a knowledge graph embedding-based question-answering sys-
tem (KEQA). They translated the question and its corresponding answer into
a single triplet. For instance, the question “ Which film acted by Leonardo”
and one of its answers “Inception” can be expressed as the following triplet:
(Leonard, act, I nception). Then, the head entity, relation, and tail entity of the
triplet are represented by a vector matrix in the embedding space for learning
the question-answer information. Considering the semantic meanings of the
questions, Shin et al. (Shin et al,2019) presented a predicate constraint-based
question-answering system (PCQA). They took advantage of the predicate
constraints of knowledge graphs, which is a triplet contains a subject, pred-
icate, and an object to capture the connection between the questions and
answers. Using the triplet for question-answering integration, the processing of
the question-answering service can be simplified; therefore, the result improves.
Bauer et al. (Bauer et al,2018) focused on multi-hop questions and pro-
posed a Multi-Hop Pointer-Generator Model (MHPGM). They selected the
relation edges that are related to the questions in a knowledge graph and
injected attention to achieve multi-hop question answering. Because of the
advantages of knowledge graphs’ structure, multi-hop question answering can
extract coherent answers effectively. Saxena et al. (Saxena et al,2020) proposed
EmbedKGQA to achieve multi-hop question answering over sparse knowl-
edge graphs (such as knowledge graphs with missing edges). The main idea
of EmbedKGQ is to utilize knowledge graph embeddings to reduce knowledge

Knowledge Graphs: Opportunities and Challenges 13
graph sparsity. It first creates embeddings of all entities and then selects the
embedding of a given question. Lastly, it predicts the answer by combining
these embeddings.
Compared to the traditional question answering, the advantages of knowl-
edge graph-based question-answering systems can be summarized as follows:
•Increased Efficiency: Instead of searching for answers from massive tex-
tual data, which may contain a large volume of useless data items, knowledge
graph-based question-answering systems focus only on entities with rel-
evant properties and semantics. Therefore, they reduce the search space
significantly and extract the answers effectively and efficiently.
•Multi-hop Question Answering: The answers can be more complex and
sophisticated than the ones produced with traditional methods relying on
unstructured data since they can combine several facts and concepts from
the knowledge graph via multi-hop question answering.
3.3 Information Retrieval
Information retrieval enables retrieval systems to match end-user queries with
relevant documents, such as web pages (Liu et al,2019). Traditional infor-
mation retrieval systems index the documents according to the user queries
and return the matched documents to the users (Hersh,2021). Nevertheless,
index processing is complex and requires plenty of time because of the massive-
ness and diversity of documents. As a result, traditional information retrieval
faces the challenge of inaccurate search results and potentially low efficiency.
Also, since search engines have limitations with respect to text interpretation
ability, keyword-based text search usually outputs limited results. Thus, to
address these problems, many modern search engines take advantage of knowl-
edge graphs (Bounhas et al,2020;Zheng et al,2020). Knowledge graph-based
information retrieval introduces a new research direction that takes advantage
of knowledge graphs for improving the performance of search engines and the
explainability of the results.
Typically, these systems rely on the advanced representation of the docu-
ments based on entities and relationships from knowledge graphs. These formal
and machine-readable representations are then matched to the user query for
retrieving the more pertinent documents. For instance, Wise et al. (Wise et al,
2020) proposed a COVID-19 Knowledge Graph (CKG) to extract the rela-
tionships between the scientific articles about COVID-19. In particular, they
combined the topological information of documents with the semantic meaning
to construct document knowledge graphs. Wang et al. (Wang et al,2018a) pro-
posed a knowledge graph-based information retrieval technology that extracts
entities by mining entity information on web pages via an open-source relation
extraction method. Then, the entities with relationships are linked to construct
a knowledge graph.
Knowledge graphs can also support methods for query expansion, which is
able to enrich the user query by adding relevant concepts (e.g., synonymous).

14 Knowledge Graphs: Opportunities and Challenges
For example, Dalton et al. (Dalton et al,2014) presented an entity query fea-
ture expansion (EQFE) to enrich the queries based on the query knowledge
graph, including structured attributes and text. Liu et al. (Liu et al,2018)
proposed the Entity-Duet Neural Ranking Model (EDRM). EDRM integrates
the semantics extracted from knowledge graphs with the distributed represen-
tations of entities in queries and documents. Then, it ranks the search results
using interaction-based neural ranking networks.
Compared to traditional information retrieval, the knowledge graph-based
information retrieval has the following advantages:
•Semantic Representation of Items: Items are represented according to
a formal and interlinked model that supports semantic similarity, reason-
ing, and query expansion. This typically allows the system to retrieve more
relevant items and makes the system more interpretable.
•High Search Efficiency: Knowledge graph-based information retrieval can
use the advanced representation of the items to reduce the search space sig-
nificantly (e.g., discarding documents that use the same terms with different
meanings), resulting in improved efficiency.
•Accurate Retrieval Results: In knowledge graph-based information
retrieval, the correlation between query and documents is analyzed based on
the relations between entities in the knowledge graph. This is more accurate
than finding the similarities between queries and documents.
4 Applications and Potentials
In this section, we discuss the applications and potentials of knowledge
graphs in four domains: education, scientific research, social networks, and
health/medical care. Although some researchers try to take advantage of
knowledge graphs to develop beneficial applications in other domains such
as finance (Cheng et al,2022c), the knowledge graph-based intelligent ser-
vice in these areas is relatively obscure and still needs to be explored.
Therefore, this section mainly focuses on education, scientific research, social
networks, and medical care to summarize the opportunities of knowledge
graphs. Table 2 presents several recent applications of knowledge graphs that
make contributions to these fields.
4.1 Education
Education is of great importance to the development of human society. Many
studies have focused on deploying intelligent applications to improve the qual-
ity of education (Bai et al,2021;Wang et al,2020d). Specifically, in the
age of big data, data processing becomes a challenging task because of the
complex and unstructured educational data. Thereby, intelligent educational
systems tend to apply structured data, such as knowledge graphs. Several

Knowledge Graphs: Opportunities and Challenges 15
Table 2 Fields of applications of knowledge graphs.
Fields Applications Methods Functions
Education Knowledge Graph based
Course Management
Model (Aliyu et al,2020)
Course knowledge graphs Courses management; Generation of
course allocation schedule
KnowEdu (Chen et al,
2018)
Instructional concepts extrac-
tion; Educational relation iden-
tification
Educational knowledge graph con-
struction
Knowledge Graph-based
Tool for Online Learning
(Zablith,2022)
Integration of social media con-
tents and formal learning con-
tents
Efficient online knowledge acquisition
Scientific Research Scientific Publication
Management Model (Chi
et al,2018)
Knowledge graph based aca-
demic network
Scientific publication management
Reviewer Recommenda-
tion System Yong et al
(2021)
Knowledge graph-based rule
engine establishment
Precise matching of reviewer and
paper
Social Networks DEAP-FAKED (Mayank
et al,2021)
News-Entity knowledge graphs Fake news detection
GraphRec (Fan et al,
2019)
Information aggregation of
user-user and user-item graphs
Social Recommendation
Graph Reasoning Model
(Wang et al,2018d)
Knowledge graph propogation Social relationship extraction
Health/Medical
Care
SMR (Gong et al,2021) Medical knowledge graph
embeddings
Safe medicine recommendation
DETERRENT (Cui et al,
2020)
Knowledge guided graph atten-
tion network
Health misinformation detection
KGNN (Lin et al,2020) Mining the relationships
between drugs
Drug discovery
COVID-KG(Yuan and
Deng,2021)
Multimedia knowledge graph
construction
Drug discovery

16 Knowledge Graphs: Opportunities and Challenges
knowledge graph-based applications support the educational process, focus-
ing in particular on data processing and knowledge dissemination (Yao et al,
2020).
In education, the quality of offline school teaching is of vital importance.
Therefore, several knowledge graph-based applications focus on supporting
teaching and learning. For example, considering the importance of course
allocation tasks in university, Aliyu et al. (Aliyu et al,2020) proposed a knowl-
edge graph-based course management approach to achieve automatic course
allocation. They constructed a course knowledge graph in which the entities
are courses, lecturers, course books, and authors in order to suggest rele-
vant courses to students. Chen et al.(Chen et al,2018) presented KnowEdu,
a system for educational knowledge graph construction, which automatically
builds knowledge graphs for learning and teaching in schools. First, KnowEdu
extracts the instructional concepts of the subjects and courses as the entity
features. Then, it identifies the educational relations based on the students’
assessments and activities to make the teaching effect more remarkable.
The abovementioned knowledge graph-based intelligent applications are
dedicated to improving the quality of offline school teaching. However, online
learning has become a hot trend recently. Moreover, online study is an indis-
pensable way of learning for students during the COVID-19 pandemic(Saraji
et al,2022). Struggling with confusing online content (e.g., learning content of
low quality on social media), students face major challenges in acquiring signif-
icant knowledge efficiently. Therefore, researchers have focused on improving
online learning environments by constructing education-efficient knowledge
graphs (d’Aquin,2016;Pereira et al,2017). For example, to facilitate online
learning and establish connections between formal learning and social media,
Zablith (Zablith,2022) proposed to construct a knowledge graph by integrating
social media and formal educational content, respectively. Then, the produced
knowledge graph can filter social media content, which is fruitful for formal
learning and help students with efficient online learning to some extent.
Offline school teaching and online learning are two essential parts of edu-
cation, and it is necessary to improve the quality of both to promote the
development of education. Significantly, knowledge graph-based intelligent
applications can deal with complicated educational data and make both offline
and online education more convenient and efficient.
4.2 Scientific Research
A variety of knowledge graphs focus on supporting the scientific process and
assisting researchers in exploring research knowledge and identifying rele-
vant materials (Xia et al,2016). They typically describe documents (e.g.,
research articles, patents), actors (e.g., authors, organizations), entities (e.g.,
topics, tasks, technologies), and other contextual information (e.g., projects,
funding) in an interlinked manner. For instance, Microsoft Academic Graph
(MAG) (Wang et al,2020a) is a heterogeneous knowledge graph. MAG

Knowledge Graphs: Opportunities and Challenges 17
contains the metadata of more than 248M scientific publications, includ-
ing citations, authors, institutions, journals, conferences, and fields of study.
The AMiner Graph (Zhang et al,2018) is the corpus of more than 200M
publications generated and used by the AMiner system1. The Open Aca-
demic Graph (OAG)2is a massive knowledge graph that integrates Microsoft
Academic Graph and AMiner Graph. AceKG (Wang et al,2018c) is a large-
scale knowledge graph that provides 3 billion triples of academic facts about
papers, authors, fields of study, venues, and institutes, as well as the relations
among them. The Artificial Intelligence Knowledge Graph (AI-KG) (Dess`ı
et al,2020)3describes 800K entities (e.g., tasks, methods, materials, metrics)
extracted from the 330K most cited articles in the field of AI. The Academi-
a/Industry Dynamics Knowledge Graph (AIDA KG) (Angioni et al,2021)4
describes 21M publications and 8M patents according to the research top-
ics drawn from the Computer Science Ontology (Salatino et al,2020) and 66
industrial sectors (e.g., automotive, financial, energy, electronics).
In addition to constructing academic knowledge graphs, many researchers
also take advantage of knowledge graphs to develop various applications ben-
eficial to scientific research. Chi et al. (Chi et al,2018) proposed a scientific
publication management model to help non-researchers learn methods for
sustainability from research thinking. They built a knowledge graph-based
academic network to manage scientific entities. The scientific entities, includ-
ing researchers, papers, journals, and organizations, are connected regarding
their properties. For the convenience of researchers, many scientific knowledge
graph-based recommender systems, including citation recommendation, col-
laboration recommendation, and reviewer recommendation, are put forward
(Shao et al,2021). For instance, Yong et al.(Yong et al,2021) designed a
knowledge graph-based reviewer assignment system to achieve precise match-
ing of reviewers and papers. Particularly, they matched knowledge graphs
and recommendation rules to establish a rule engine for the recommendation
process.
4.3 Social Networks
With the rapid growth of social media such as Facebook and Twitter, online
social networks have penetrated human life and bring plenty of benefits such
as social relationship establishment and convenient information acquisition
(Li et al,2020a;Hashemi and Hall,2020). Various social knowledge graphs
are modeled and applied to analyze the critical information from the social
network. These knowledge graphs are usually constituted based on the peo-
ple’s activities and their posts on social media, which are applied to numerous
applications for different functions (Xu et al,2020).
1AMiner - https://www.aminer.cn/
2Open Academic Graph - https://www.openacademic.ai/oag/
3AI-KG - https://w3id.org/aikg/
4AIDA - http://w3id.org/aida

18 Knowledge Graphs: Opportunities and Challenges
Remarkably, social media provides high chances for people to make friends
and gain personalized information. Furthermore, social media raises funda-
mental problems, such as how to recommend accurate content that interests
us and how to connect with persons interested in a common topic. To address
these issues, various studies have been proposed to match users with their
favorite content (or friends) for recommendation (Ying et al,2018). With the
increase in users’ demand, a number of researchers utilize knowledge graph-
based approaches for more precise recommendations (Gao et al,2020). A
representative example is GraphRec (a graph neural network framework for
social recommendations) proposed by Fan et al. (Fan et al,2019). They con-
sidered two kinds of social knowledge graphs: user-user and user-item graphs.
Then, they extracted information from the two knowledge graphs for the learn-
ing task. As a result, their model can provide accurate social recommendations
because it aggregates the social relationships of users and the interactions
between users and items.
In addition, people’s activities on social media reveal social relationships.
For example, we can learn about the relationships around a person through
his photos or comments on Twitter. Significantly, social relationship extrac-
tion assists companies in tracking users and enhancing the user experience.
Therefore, many works are devoted to social relationship extraction. Wang
et al. (Wang et al,2018d) propose a graph reasoning model to recognize the
social relationships of people in a picture that is posted on social media. Their
model enforces a particular function based on the social knowledge graph and
deep neural networks. In their method, they initialized the relation edges and
entity nodes with the features that are extracted from the semantic objects
in an image. Then, they employed GGNN to propagate the knowledge graph.
Therefore, they explored the relations of the people in the picture.
One of the biggest problems in this space is fake news (Zhang et al,2019a).
Online social media has become the principal platform for people to consume
news. Therefore, a considerable amount of research has been done for fake
news detection (Choi et al,2020;Meel and Vishwakarma,2020). Most recently,
Mayank et al. (Mayank et al,2021) exploited a knowledge graph-based model
called DEAP-FAKED to detect fake news on social media. Specifically, DEAP-
FAKED learns news content and identifies existing entities in the news as the
nodes of the knowledge graph. Afterward, a GNN-based technique is applied
to encode the entities and detect anomalies that may be linked with fake news.
4.4 Health/Medical Care
With medical information explosively growing, medical knowledge analysis
plays an instrumental role in different healthcare systems. Therefore, research
focuses on integrating medical information into knowledge graphs to empower
intelligent systems to understand and process medical knowledge quickly and
correctly (Li et al,2020b). Recently, a variety of biomedical knowledge graphs
have become available. Therefore, many medical care applications exploit
knowledge graphs. For instance, Zhang et al. (Zhang et al,2020a) presented a

Knowledge Graphs: Opportunities and Challenges 19
Health Knowledge Graph Builder (HKGB) to build medical knowledge graphs
with clinicians’ expertise.
Specifically, we discuss the three most common intelligent medical care
applications, including medical recommendation, health misinformation detec-
tion, and drug discovery. Firstly, with the rapid development of the medical
industry, medical choices have become more abundant. Nevertheless, in the
variety of medical choices, people often feel confused and unable to make the
right decision to get the most suitable and personalized medical treatment.
Therefore, medical recommender systems, especially biomedical knowledge
graph-based recommender systems (such as doctor recommender systems and
medicine recommender systems), have been put forward to deal with this
issue (Katzman et al,2018). Taking medicine recommendation as an example,
Gong et al. (Gong et al,2021) provided a medical knowledge graph embedding
method by constructing a heterogeneous graph whose nodes are medicines,
diseases, and patients to recommend accurate and safe medicine prescriptions
for complicated patients.
Secondly, although many healthcare platforms aim to provide accurate
medical information, health misinformation is an inevitable problem. Health
misinformation is defined as incorrect information that contradicts authen-
tic medical knowledge or biased information that covers only a part of the
facts (Wang et al,2020e). Unfortunately, a great deal of health-related infor-
mation on various healthcare platforms (e.g., medical information on social
media) is health misinformation. What’s worse, the wrong information leads to
consequential medical malpractice; therefore, it is urgent to detect health mis-
information. Utilizing authoritative medical knowledge graphs to detect and
filter misinformation can help people make correct treatment decisions and
suppress the spread of misinformation (Cui et al,2020). Representatively, Cui
et al. (Cui et al,2020) presented a model called DETERREN to detect health
misinformation. DETERREN leverages a knowledge-guided attention network
that incorporates an article-entity graph with a medical knowledge graph.
Lastly, drug discovery, such as drug repurposing and drug-drug interac-
tion prediction, has been a research trend for intelligent healthcare in recent
years. Benefiting from the rich entity information (e.g., the ingredients of a
drug) and relationship information (e.g., the interaction of drugs) in medi-
cal knowledge graphs, drug discovery based on knowledge graphs is one of
the most reliable approaches (MacLean,2021). Lin et al. (Lin et al,2020)
presented an end-to-end framework called KGNN (Knowledge Graph Neural
Network) for drug-drug interaction prediction. The main idea of KGNN is to
mine the relations between drugs and their potential neighborhoods in medical
knowledge graphs. It first exploits the topological information of each entity;
then, it aggregates all the neighborhood information from the local receptive
entities to extract both semantic relations and high-order structures. Wang
et al. (Wang et al,2020c) developed a knowledge discovery framework called
COVID-KG to generate COVID-19-related drug repurposing reports. They
first constructed multimedia knowledge graphs by extracting medicine-related

20 Knowledge Graphs: Opportunities and Challenges
entities and their relations from images and texts. Afterward, they utilized the
constructed knowledge graphs to generate drug repurposing reports.
5 Technical Challenges
Although knowledge graphs offer fantastic opportunities for various services
and applications, many challenges are yet to be addressed (Noy et al,2019).
Specifically, the limitations of existing knowledge graph technologies are the
key challenges for promoting the development of knowledge graphs (Hogan
et al,2021). Therefore, this section discusses the challenges of knowledge
graphs in terms of the limitations of five topical knowledge graph technolo-
gies, including knowledge graph embeddings, knowledge acquisition, knowledge
graph completion, knowledge fusion, and knowledge reasoning.
5.1 Knowledge Graph Embeddings
The aim of knowledge graph embeddings is to effectively represent knowledge
graphs in a low-dimensional vector space while still preserving the semantics
(Xia et al,2021;Vashishth et al,2020). Firstly, the entities and relations are
embedded into a dense dimensional space in a given knowledge graph, and a
scoring function is defined to measure the plausibility of each fact (triplet).
Then, the plausibility of the facts is maximized to obtain the entity and rela-
tion embeddings (Chaudhri et al,2022;Sun et al,2022). The representation of
knowledge graphs brings various benefits to downstream tasks. The three main
types of triplet fact-based knowledge graph embedding approaches are ten-
sor factorization-based, translation-based, and neural network-based methods
(Rossi et al,2021).
5.1.1 Tensor Factorization-based Methods
The core idea of tensor factorization-based methods is transforming the triplets
in the knowledge graph into a 3D tensor (Balaˇzevi´c et al,2019). As Fig 5
presents, the tensor X ∈ Rm×m×n, where mand nindicate the number of
entity and relation, respectively, contains nslices, and each slice corresponds
to one relation type. If the condition Xijk = 1 is met, the triplet (ei, rk, ej),
where eand rdenote entity and relation, respectively, exists in the knowledge
graph. Otherwise, if Xijk = 0, there is no such a triplet in the knowledge graph.
Then, the tensor is represented by the embedding matrices that consist of the
vectors of entities and relations.
5.1.2 Translation-based Methods
Translation-based methods exploit the scoring function, which is based on
translation invariance. Translation invariance interprets the distance between
the vectors of the two words, which is represented by the vector of their
semantic relationships (Mikolov et al,2013). Bordes et al. (Bordes et al,2013)
firstly utilized the translation invariance-based scoring functions to measure

Knowledge Graphs: Opportunities and Challenges 21
Table 3 Knowledge graph embedding methods.
Categories Techniques Evaluation Approaches Data Set Results
Tensor factorization-based
methods
RESCAL (Nickel et al,2011) Link prediction[Hits@10] FB15K 44.1%
HolE (Nickel et al,2016) Link prediction[Hits@10] FB15K 73.9%
ComplEx (Trouillon et al,2016) Link prediction[Hits@10] FB15K 84%
SimplE (Kazemi and Poole,2018) Link prediction[Hits@10] FB15K 83.8%
RotatE (Sun et al,2019a) Link prediction[Hits@10] FB15K 88.4%
QuatE (Zhang et al,2019c) Link prediction[Hits@10] FB15K 90%
Translation-based methods TransE (Bordes et al,2013) Link prediction[Hits@10] FB15K 47.1%
TransH (Wang et al,2014) Link prediction[Hits@10] FB15K 64.4%
TransR (Lin et al,2015) Link prediction[Hits@10] FB15K 68.7%
TransD (Ji et al,2015) Link prediction[Hits@10] FB15K 77.3%
TranSparse (Ji et al,2016) Link prediction[Hits@10] FB15K 79.9%
STransE (Nguyen et al,2016) Link prediction[Hits@10] FB15K 79.7%
TransA (Jia et al,2016) Link prediction[Hits@10] FB15K 80.4%
KG2E (He et al,2015) Link prediction[Hits@10] FB15K 71.5%
TransG (Xiao et al,2015) Link prediction[Hits@10] FB15K 88.2%
Neural network-based methods SME (Bordes et al,2014) Link prediction[Hits@10] FB15K 41.3 %
NTN (Socher et al,2013) Triplet classification[Accuracy] WN11 86.2%
SLM (Socher et al,2013) Triplet classification[Accuracy] WN11 76%
RMNN (Liu et al,2016) Triplet classification[Accuracy] WN11 89.9%
R-GCN (Schlichtkrull et al,2018) Link prediction[Hits@10] FB15K 84.2%
ConvKB (Nguyen et al,2017) Link prediction[Hits@10] WN18RR 52.5 %
KBGAN (Cai and Wang,2017) Link prediction[Hits@10] WN18 89.2%
(1) In this table, all the results of link prediction are filter results.

22 Knowledge Graphs: Opportunities and Challenges
Fig. 5 An illustration of tensor factorization of knowledge graphs.
the embedding results. They creatively proposed the TransE model, which
translates all the entities and relations of a knowledge graph into a continuous
and low vector space. Specifically, the vectors of the head and tail entities in a
triplet are connected by the vector of their relation. Consequently, in the vec-
tor space, the semantic meaning of every triplet is preserved. Formally, given a
triplet (head, relation, tail), the embedding vectors of the head entity, relation,
and tail entity are h,r, and t, respectively. In the vector space, the plausibility
of the triplet (h,r,t) is computed by the translation invariance-based scoring
function to ensure it follows the geometric principle: h+r≈t.
After TransE, a lot of related extensions, such as TransH (Wang et al,
2014) and TransR (Lin et al,2015), are continually proposed to improve the
performance of the Translation-based knowledge graph embeddings.
5.1.3 Neural Network-based Methods
Nowadays, deep learning has become a popular tool that is utilized for knowl-
edge graph embeddings, and a considerable amount of research proposes to
employ neural networks to represent the triplets of knowledge graphs (Dai et al,
2020a). In this section, we discuss three representative works, including SME,
ConvKB, and R-GCN, to briefly introduce neural network-based knowledge
graph embeddings.
SME (Bordes et al,2014) designs an energy function to conduct semantic
matching, which utilizes neural networks to measure the confidence of each
triplet (h, r, t) in knowledge graphs. The scoring function of SME is defined as
follows:
fr(h, t)=(Wh1h+Wh2r+bh)>(Wt1t+Wt2r+bt).(1)
The scoring function of SME (bilinear) is:
fr(h, t) = ((Wh1h)◦(Wh2r) + bh)>((Wt1t)◦(Wt2r) + bt).(2)

Knowledge Graphs: Opportunities and Challenges 23
Here W∈Rd×ddenotes the weight matrix, bindicates the bias vector. h,
r, and tare the embedding vectors of head entity, relation, and tail entity,
respectively.
ConvKB (Nguyen et al,2017) utilizes a convolutional neural network
(CNN) to conduct knowledge graph embeddings. ConvKB represents each
triplet (h, r, t) as a three-row matrix A, which is input to a convolution layer
to obtain feature maps. Afterward, the feature maps are concatenated as a
vector, and then a score is calculated to estimate the confidence of the triplet.
The scoring function is as follows:
fr(h, t) = O(g(A∗Ω))w,(3)
where Osignifies the concatenation operator, g(·) is the ReLU activation func-
tion, A∗Ω indicates the convolution operation of matrix Aby using the filters
in the set Ω, w∈R3dis a weight vector.
R-GCN (Schlichtkrull et al,2018) is an improvement of graph neural
networks (GNNs). R-GCN represents knowledge graphs by providing relation-
specific transformation. Its forward propagation is calculated as follows:
h(l+1)
k=σX
r∈RX
i∈Nr
k
1
nk,r
W(l)
ih(l)
i+W(l)
kh(l)
k,(4)
where h(l+1)
kis the hidden state of the entity kin l-th layer, Nr
kdenotes a
neighbor collection of entity kand relation r∈R,nk,r is the normalization
process, W(l)
iand W(l)
kare the weight matrices.
5.1.4 Limitations of Existing Methods
The existing methods for generating knowledge graph embeddings still suf-
fer several severe limitations. Many established methods only consider surface
facts (triplets) of knowledge graphs. However, additional information, such as
entity types and relation paths, are ignored, which can further improve the
embedding accuracy. The performance of most traditional methods that do not
consider the additional information is unsatisfactory. Table 3lists the embed-
ding methods, which do not consider the additional information. In Table 3, the
performance evaluation is based on the link prediction and triplet classification
tasks. The metrics that are for evaluation results are hit rate at 10 (Hits@10)
and accuracy. As Table 3presents, only a few models have impressive results,
including the results of QuatE (90%), RMNN (89.9%), and KBGAN (89.2%).
Recently, some researchers have started to combine additional information
with a knowledge graph to improve the efficiency of embedding models. For
example, Guo et al. (Guo et al,2015) take advantage of additional entity type
information, which is the semantic category of each entity, to obtain the cor-
relation between the entities and to tackle the data sparsity issue. Therefore,
knowledge graphs are represented more accurately. Not only entity types, some

24 Knowledge Graphs: Opportunities and Challenges
other information, including relation paths (Li et al,2021), time information of
dynamic graphs (Messner et al,2022), and textual descriptions of entities (An
et al,2018), are getting the researchers’ attention in recent years. However, it
is still a daunting challenge to effectively utilize rich additional information to
improve the accuracy of knowledge graph embeddings.
General additional information can not adequately represent the semantic
meaning of the triplets. For instance, the entity types are not related to the
semantic information of triplets. Furthermore, the types of additional infor-
mation that can be incorporated into the features of the triplets are now
severely limited. Therefore, to improve the performance of existing knowledge
graph embedding methods, multivariate information (such as the hierarchi-
cal descriptions of relations and the combination of entity types and textual
descriptions) needs to be incorporated into the features of the triplets.
To the best of our knowledge, complex relation path remains an open
research problem (Peng et al,2021). For example, the inherent relations,
referring to the indirect relationships between two unconnected entities,
are not represented effectively. Although the inherent relations between the
entities can be explored based on the chain of relationships in knowledge
graphs, the inherent relations are complex and multiple. Therefore, it is not
straightforward to represent these relations effectively.
5.2 Knowledge Acquisition
Knowledge acquisition is a critical step for combining data from different
sources and generating new knowledge graphs. The knowledge is extracted
from both structured and unstructured data. Three main methods of knowl-
edge acquisition are relation extraction, entity extraction, and attribute
extraction (Fu et al,2019). Here, attribute extraction can be regarded as a spe-
cial case of entity extraction. Zhang et al. (Zhang et al,2019b) took advantage
of knowledge graph embeddings and graph convolution networks to extract
long-tail relations. Shi et al. (Shi et al,2021) proposed entity set expansion to
construct large-scale knowledge graphs.
Nevertheless, existing methods for knowledge acquisition still face the chal-
lenge of low accuracy, which could result in incomplete or noisy knowledge
graphs and hinder the downstream tasks. Therefore, the first critical issue
regards the reliability of knowledge acquisition tools and their evaluation. In
addition, a domain-specific knowledge graph schema is knowledge-oriented,
while a constructed knowledge graph schema is data-oriented for covering all
data features (Zhou et al,2022). Therefore, it is inefficient to produce domain-
specific knowledge graphs by extracting entities and properties from raw data.
Hence, it is an essential issue to efficiently achieve knowledge acquisition tasks
by generating domain-specific knowledge graphs.
Besides, most existing knowledge acquisition methods focus on construct-
ing knowledge graphs with one specific language. However, in order to make
the information in knowledge graphs richer and more comprehensive, we
need cross-lingual entity extraction. It is thus vitally important to give more

Knowledge Graphs: Opportunities and Challenges 25
attention to cross-lingual entity extraction and the generation of multilingual
knowledge graphs. For example, Bekoulis et al.(Bekoulis et al,2018) proposed
a joint neural model for cross-lingual (English and Dutch) entity and relation
extraction. Nevertheless, multilingual knowledge graph construction is still a
daunting task since non-English training data sets are limited, language trans-
lation systems are not always accurate, and the cross-lingual entity extraction
models have to be retrained for each new language.
Multi-modal knowledge graph construction is regarded as another chal-
lenging issue of knowledge acquisition. The existing knowledge graphs are
mostly represented by pure symbols, which could result in the poor capabil-
ity of machines to understand our real world (Zhu et al,2022b). Therefore,
many researchers focus on multi-modal knowledge graphs with various entities,
such as texts and images. The construction of multi-modal knowledge graphs
requires the exploration of entities with different modalities, which makes the
knowledge acquisition tasks complicated and inefficient.
5.3 Knowledge Graph Completion
Knowledge graphs are often incomplete, i.e., missing several relevant triplets
and entities (Zhang et al,2020b). For instance, in Freebase, one of the most
well-known knowledge graphs, more than half of person entities do not have
information about their birthplaces and parents. Generally, semi-automated
and human leveraging mechanisms, which can be applied to ensure the qual-
ity of knowledge graphs, are essential tools for the evaluation of knowledge
graph completion. Specifically, human supervision is currently considered the
gold standard evaluation in knowledge graph completion (Ballandies and
Pournaras,2021).
Knowledge graph completion aims to expand existing knowledge graphs by
adding new triplets using techniques for link prediction (Wang et al,2020b;
Akrami et al,2020) and entity prediction (Ji et al,2021). These approaches
typically train a machine learning model on the knowledge graph to assess
the plausibility of new candidate triplets. Then, they add the candidate
triplets with high plausibility to the graph. For example, for an incomplete
triplet (Tom, friendOf, ?), it is possible to assess the range of tails and
return the more plausible ones to enrich the knowledge graph. These models
successfully utilized knowledge graphs in many different domains, including
digital libraries (Yao et al,2017), biomedical (Harnoune et al,2021), social
media (Abu-Salih,2021), and scientific research (Nayyeri et al,2021). Some
new methods are able to process fuzzy knowledge graphs in which each triple
is associated with a confidence value (Chen et al,2019).
However, most current knowledge graph completion methods only focus on
extracting triplets from a closed-world data source. That means the generated
triplets are new, but the entities or relations in the triplets need to already
exist in the knowledge graph. For example, for the incomplete triplet (Tom,
friendOf, ?), predicting the triplet (Tom, friendOf, Jerry) is only possible if
the entity Jerry is already in the knowledge graph. Because of this limitation,

26 Knowledge Graphs: Opportunities and Challenges
these methods cannot add new entities and relations to the knowledge graph.
To tackle this issue, we are starting to see the emergence of open-world tech-
niques for knowledge graph completion that extracts potential objects from
outside of the existing knowledge bases. For instance, the ConMask model
(Shi and Weninger,2018) has been proposed to predict the unseen entities in
knowledge graphs. However, methods for open-world knowledge graph com-
pletion still suffer from low accuracy. The main reason is that the data source
is usually more complex and noisy. In addition, the similarity of the predicted
new entities to the existing entities can mislead the results. In other words, two
similar entities are regarded as connected entities, while they may not have a
direct relationship.
Knowledge graph completion methods assume knowledge graphs are static
and fail to capture the dynamic evolution of knowledge graphs. To obtain accu-
rate facts over time, temporal knowledge graph completion, which considers
the temporal information reflecting the validity of knowledge, has emerged.
Compared to static knowledge graph completion, temporal knowledge graph
completion methods integrate timestamps into the learning process. Hence,
they explore the time-sensitive facts and improve the link prediction accuracy
significantly. Although temporal knowledge graph completion methods have
shown brilliant performance, they still face serious challenges. Because these
models consider time information would be less efficient (Shao et al,2022), the
key challenge of temporal knowledge graph completion is how to effectively
incorporate timestamps of facts into the learning models and properly capture
the temporal dynamics of facts.
5.4 Knowledge Fusion
Knowledge fusion aims to combine and integrate knowledge from different data
sources. It is often a necessary step for the generation of knowledge graphs
(Nguyen et al,2020;Smirnov and Levashova,2019). The primary method of
knowledge fusion is entity alignment or ontology alignment (Ren et al,2021),
which aims to match the same entity from multiple knowledge graphs (Zhao
et al,2020). Achieving efficient and accurate knowledge graph fusion is a
challenging task because of the complexity, variety, and large volume of data
available today.
While a lot of work has been done in this direction, there are still several
intriguing research directions that deserve to be investigated in the future.
One of them regards cross-language knowledge fusion (Mao et al,2020), which
allows the integration of information from different languages. This is often
used to support cross-lingual recommender systems (Javed et al,2021). For
example, Xu et al. (Xu et al,2019) adopted a graph-matching neural net-
work to achieve cross-language entity alignment. However, the result of the
cross-language knowledge fusion is still unsatisfactory because the accuracy of
the matching entities from different languages is relatively low. Therefore, it
remains a daunting challenge to explore cross-language knowledge fusion.

Knowledge Graphs: Opportunities and Challenges 27
Another primary challenge regards entity disambiguation (Nguyen et al,
2020). As the polysemy problem of natural language, the same entity may
have various expressions in different knowledge graphs. Hence, entity disam-
biguation is required before conducting entity alignment. Existing entity dis-
ambiguation methods mainly focus on discriminating and matching ambiguous
entities based on extracting knowledge from texts containing rich contextual
information (Zhu and Iglesias,2018). However, these methods can not pre-
cisely measure the semantic similarity of entities when the texts are short and
have limited contextual information. Only a few works have focused on solv-
ing this issue. For example, Zhu and Iglesias (Zhu and Iglesias,2018) have
proposed SCSNED for entity disambiguation. SCSNED measures semantic
similarity based on both informative words of entities in knowledge graphs
and contextual information in short texts. Although SCSNED alleviates the
issue of limited contextual information to some extent, more effort is needed
to improve the performance of entity disambiguation.
In addition, many knowledge fusion methods only focus on matching
entities with the same modality and ignore multi-modal scenes in which knowl-
edge is presented in different forms. Specifically, entity alignment considering
only single-modality knowledge graph scenario has insignificant performance
because it can not fully reflect the relationships of entities in the real world
(Cheng et al,2022a). Recently, to solve this issue, some studies have proposed
multi-modal knowledge fusion, which matches the same entities having differ-
ent modalities and generates a multi-modal knowledge graph. For example,
HMEA (Guo et al,2021) aligns entities with multiple forms by mapping multi-
modal representations into hyperbolic space. Although many researchers have
worked on multi-modal knowledge fusion, it is still a critical task. Multi-modal
knowledge fusion mainly aims to find equivalent entities by integrating their
multi-modal features (Cheng et al,2022a). Nevertheless, how to efficiently
incorporate the features having multiple modalities is still a tricky issue facing
current methods.
5.5 Knowledge Reasoning
The goal of knowledge reasoning is to infer new knowledge, such as the implicit
relations between two entities (Liu et al,2021;Wang et al,2019c), based on
existing data. For a given knowledge graph, wherein there are two unconnected
entities hand t, denoted as h, t ∈G, here Gmeans the knowledge graph,
knowledge reasoning can find out the potential relation rbetween these enti-
ties and form a new triplet (h, r, t). The knowledge reasoning methods are
mainly categorized into logic rule-based (De Meester et al,2021), distributed
representation-based (Chen et al,2020b), and neural network-based methods
(Xiong et al,2017). Logic rule-based knowledge reasoning aims to discover
knowledge according to the random walk and logic rules, while distributed
representation-based knowledge reasoning embeds entities and relations into a
vector space to obtain distributed representation (Chen et al,2020b). Neural

28 Knowledge Graphs: Opportunities and Challenges
network-based knowledge reasoning method utilizes neural networks to infer
new triplets given the body of knowledge in the graph (Xian et al,2019).
There are two tasks in knowledge reasoning: single-hop prediction and
multi-hop reasoning (Ren et al,2022). Single-hop prediction predicts one ele-
ment of a triplet for the given two elements, while multi-hop reasoning predicts
one or more elements in a multi-hop logical query. In other words, in the
multi-hop reasoning scenario, finding the answer to a typical question and
forming new triplets requires the prediction and imputation of multiple edges
and nodes. Multi-hop reasoning achieves a more precise formation of triplets
when compared with the single-hop prediction. Therefore, multi-hop reasoning
has attracted more attention and become a critical need for the develop-
ment of knowledge graphs in recent years. Although many works have been
done, multi-hop reasoning over knowledge graphs remains largely unexplored.
Notably, multi-hop reasoning on massive knowledge graphs is one of the chal-
lenging tasks (Zhu et al,2022a). For instance, most recent studies focus on
multi-hop reasoning over knowledge graphs, which have only 63K entities and
592K relations. The existing models can’t learn the training set effectively for
a massive knowledge graph that has more than millions of entities. Moreover,
multi-hop reasoning needs to traverse multiple relations and intermediate enti-
ties in the knowledge graph, which could lead to exponential computation cost
(Zhang et al,2021). Therefore, it is still a daunting task to explore multi-hop
knowledge reasoning.
Besides, the verification of inferred new knowledge is also a critical issue.
Knowledge reasoning enriches existing knowledge graphs and brings benefits to
the downstream tasks (Wan et al,2021). However, the inferred new knowledge
is sometimes uncertain, and the veracity of new triplets needs to be veri-
fied. Furthermore, the conflicts between new and existing knowledge should be
detected. To address these problems, some research has proposed multi-source
knowledge reasoning (Zhao et al,2020) that detects erroneous knowledge and
conflicting knowledge. Overall, more attention should be paid to multi-source
knowledge reasoning and erroneous knowledge reduction.
6 Conclusion
Knowledge graphs have played an instrumental role in creating many intelli-
gent services and applications for various fields. In this survey, we provided
an overview of knowledge graphs in terms of opportunities and challenges.
We first introduced the definitions and existing research directions regarding
knowledge graphs to provide an introductory analysis of knowledge graphs.
Afterward, we discussed AI systems that take advantage of knowledge graphs.
Then, we presented some representative knowledge graph applications in sev-
eral fields. Furthermore, we analyzed the limitations of current knowledge
graph technologies, which lead to severe technical challenges. We expect this
survey to spark new ideas and insightful perspectives for future research and
development activities involving knowledge graphs.

Knowledge Graphs: Opportunities and Challenges 29
Declarations
Conflict of interest. The authors declare that they have no compet-
ing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
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