Jingwei Cheng

• Doctor of Philosophy
• Lecturer at Northeastern University

Entity alignment (EA) refers to the task of linking entities in different knowledge graphs (KGs). Existing EA methods rely heavily on structural isomorphism. However, in real-world KGs, aligned entities usually have non-isomorphic neighborhood structures, which paralyses the application of these structure-dependent methods. In this paper, we invest...

Commonsense knowledge is essential for performing inference and retrieval in many artificial intelligence applications, including those in natural language processing and expert system. However, a large amount of valuable commonsense knowledge exists implicitly or is missing in commonsense knowledge graphs (KGs). In this case, commonsense knowledge...

Most existing knowledge graphs (KGs) suffer from incompleteness, which will be detrimental to a variety of downstream applications. Link prediction is the task of predicting missing links in the KGs and can effectively address the issue of incompleteness by knowledge graph embedding (KGE). ConvE, a relatively popular KGE model based on convolutiona...

Knowledge graph completion (KGC, also referred to as link prediction) aims at predicting missing entities and relations in knowledge graphs (KGs). Knowledge graph embedding (KGE) techniques have been proven to be effective for link prediction. Currently, a series of convolutional neural networks (CNNs) based models (e.g., ConvE and its extended mod...

Currently, named entity recognition (NER) is mainly evaluated on standard and well‐annotated data sets. However, the construction of a well‐annotated data set will consume a lot of manpower and time. In lots of applications of NER, data sets may contain a lot of noise, and a large part of noise comes from unlabeled entities. At present, the trainin...

Knowledge Graph (KG) provides high-quality structured knowledge for various downstream knowledge-aware tasks (such as recommendation and intelligent question-answering) with its unique advantages of representing and managing massive knowledge. The quality and completeness of KGs largely determine the effectiveness of the downstream tasks. But in vi...

The goal of entity alignment is to find the equivalent entity pairs in different Knowledge Graphs (KGs), which is a key step of KG fusion. Recent developments often take embedding-based methods, which mainly focus on embedding structure information (relationship triples) of KGs to align entities. However, attribute information (attribute triples) a...

Over the years, a large amount of temporal data needs to be shared and exchanged on the Web. Resource Description Framework (RDF) has been widely accepted and has rapidly gained popularity to al RDF) model as mentioned in represent and share data in many application domains (e.g., the Data of Web, Linked Data, and Knowledge Graph). Accordingly, eff...

Commonsense reasoning is an important aspect of building robust AI systems and is receiving significant attention in the natural language understanding, computer vision, and knowledge graphs communities. At present, a number of valuable commonsense knowledge sources exist, with different foci, strengths, and weaknesses. In this paper, we list repre...

A Knowledge Graph (KG) is a directed graph with nodes as entities and edges as relations. KG representation learning (KGRL) aims to embed entities and relations in a KG into continuous low-dimensional vector spaces, so as to simplify the manipulation while preserving the inherent structure of the KG. In this paper, we propose a KG embedding framewo...

With the advent of the information age, excessive information collection leads to information overload. Automatic text summarization technology has become an effective way to solve information overload. This paper proposes an automatic text summarization model, which extends traditional sequence-to-sequence (Seq2Seq) neural text summarization model...

Representation learning of knowledge graphs has gained wide attention in the field of natural language processing. Most existing knowledge representation models for knowledge graphs embed triples into a continuous low-dimensional vector space through a simple linear transformation. In spite of high computation efficiency, the fitting ability of the...

With the explosive growth of temporal data, how to query and manage temporal data has become an important research issue. Resource Description Framework (RDF), as the standard data and knowledge description language of the Semantic Web, has been widely used to represent various of domain data. Aiming at the representation and querying of temporal d...

The fuzzy UML model has been introduced to enable the conceptual modeling of imprecise data in many applications. How to prevent, detect, and correct errors as early as possible in the modeling process by verifying the correctness of fuzzy UML models is desirable. But it is difficult to manually verify the correctness of the models. Thanks to the e...

RDF plays an important role in representing Web resources in a natural and flexible way. As the amount of RDF datasets increasingly growing, storing and querying theses data have attracted the attention of more and more researchers. In this chapter, we first make a review of approaches for query processing of RDF datasets. We categorize existing me...

In the context of the Semantic Web, fuzzy extensions to OWL (the W3C standard ontology language) and Description Logics (DLs, the logical foundation of OWL) have been extensively investigated, and there are many real fuzzy DL ontology knowledge bases. Therefore, how to store fuzzy DL ontology knowledge bases has become an important issue. In this p...

Since SPARQL has been the standard language for querying RDF data, keyword search based on keywords-to-SPARQL translation attracts more intention. However, existing keyword search based on keywords-to-SPARQL translation have limitations that the schema used for keyword-to-SPARQL translation is incomplete so that wrong or incomplete answers are retu...

In recent years there have been a great deal of interests in effectively extracting information from RDF repositories. However, users often experience empty answer problems (EAP) when they are not familiar with the structure or content of a RDF dataset and issue a restrictive query to the system. A solution is to allow users to incorporate fuzzy co...

In this paper, we propose a query relaxation approach to handle the problem of empty or too little answers returned from RDF query. We apply RDF entailment to triple patterns in the original query to get more general answers. We propose the notion of semantic similarity degree so that the returned answers are semantically close to the original quer...

Ontology, as a standard (World Wide Web Consortium recommendation) for representing knowledge in the Semantic Web, has become a fundamental and critical component for developing applications in different real-world scenarios. However, it is widely pointed out that classical ontology model is not sufficient to deal with imprecise and vague knowledge...

Based on the high expressive powers and effective reasoning services of description logics (DLs, for short), DLs have been employed in data modeling to support the development and maintenance of data models. The basic idea is that once the correspondences between data models and DLs can be established, reasoning techniques from DLs become applicabl...

With the widespread acceptance of RDF (Resource Description Framework) as the de-facto standard recommended by W3C (World Wide Web Consortium) for the representation and exchange of information on the Web, a huge amount of RDF data is being proliferated and becoming available. Efficient querying of RDF data is therefore of increasing importance. Pr...

RDF (Resource Description Framework) and RDF Schema (collectively called RDF(S)) are the normative language to describe the Web resource information. How to construct RDF(S) from the existing data sources is becoming an important research issue. In particular, UML (Unified Modeling Language) is being widely applied to data modeling in many applicat...

Knowledge representation and reasoning plays an essential role in creating machine-processing content in the context of the Semantic Web. Ontology, which can capture the knowledge in a domain in a formal and machine-processable way, is a W3C standard knowledge representation model for the Semantic Web. Also, as an important representation of knowle...

With the requirement of fuzzy Description Logic (DL) and ontology extraction, it is necessary and meaningful to extract fuzzy DL and ontology knowledge from various data resources. In particular, with the widespread studies and the relatively mature techniques of fuzzy databases as introduced in Chap. 3, much valuable information and implicit knowl...

Based on the introduction in this chapter, it is shown that lots of fuzzy Description Logics (DLs) and fuzzy ontologies have been investigated in order to handle fuzzy information in real-world applications. In particular, fuzzy DLs and fuzzy ontologies are the acknowledged key of representing and reasoning with knowledge in the Semantic Web. In th...

In real-world applications, information is often imprecise or uncertain. Many sources can contribute to the imprecision and uncertainty of data or information. It is particular true in the knowledge representation and reasoning in the Semantic Web as well as applications using Semantic Web techniques such as ontologies, Description Logics, and rule...

As an important representation of knowledge, rules possess many features including highly expressive power and requiring small memory space, which let rules become indispensable and widely used in many areas of the Semantic Web. However, there is much imprecise and uncertain knowledge in the context of the Semantic Web. Therefore, fuzzy extensions...

With the growing number of heterogeneous fuzzy ontologies in the Semantic Web, fuzzy ontology mapping becomes an important problem to solve the interoperation among heterogeneous ontologies containing fuzzy information. In particular, defining similarity relations among fuzzy ontology components is the core of fuzzy ontology mapping. In this chapte...

In order to represent the widespread imprecision and uncertainty in Semantic Web applications, there have been substantial amounts of work carried out in the context of fuzzy extensions of (Description Logics) DLs and ontologies, and corresponding reasoning algorithms and reasoners are thus developed. Also, some querying techniques are developed to...

This book goes to great depth concerning the fast growing topic of technologies and approaches of fuzzy logic in the Semantic Web. The topics of this book include fuzzy description logics and fuzzy ontologies, queries of fuzzy description logics and fuzzy ontology knowledge bases, extraction of fuzzy description logics and ontologies from fuzzy dat...

In the context of the Semantic Web, fuzzy extensions to OWL (the W3C standard ontology language) and Description Logics (DLs, the logical foundation of OWL) have been extensively investigated as introduced in Chap. 4, and many real knowledge bases based on fuzzy DLs and fuzzy OWL tend to become very large to huge. Therefore, how to store fuzzy know...

Currently, many research studies have been concentrated on construction of fuzzy ontologies from different sources. Over the years, standard and variations of the entity-relationship (ER) model has widespread use, and also some approaches have been proposed for modeling fuzzy information in ER and extended entity-relationship (EER) models. Therefor...

RDF fuzzy retrieval is an important module for realizing intelligent retrieval of Web semantics. In this paper, Zadeh's type-II fuzzy set theory, as well as the concepts of α-cut set and linguistic variable is adopted to put forward the RDF fuzzy retrieval mechanism supporting user preference, which extends SPARQL language for further realizing fuz...

Recently, the rapid evolution of large-scale domain ontologies brought about higher requirements for data access in the Semantic Web community. The basic reasoning services for ontologies, however, cannot meet the needs of dealing with complex queries (mainly conjunctive queries) in data-intensive applications. For that purpose, significant researc...

The relationships between description logics and object-oriented data models are analyzed, and the paper aims at investigating the representation and reasoning of fuzzy object-oriented data (FOOD) models with description logics. The FOOD models are investigated, and the formal definition and semantics of FOOD models are proposed first. Then, aiming...

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy ontologies have been extensively investigated and increasingly created. Therefore, it is critical to develop scalable and efficient fuzzy ontology storage mechanism. The fuzzy relational database may be a good candidate for storing fuzzy ontologi...

Table is ubiquitous in web documents. Turning the web-table information into ontology requires automatic approaches. In this paper, we discuss how to learn ontology from web-table. In order to obtain table schemata for learning of ontology, we first present general layout structure of the table, then, we propose ontology extraction method according...

In recent years, how to extract useful information and knowledge from fuzzy relational databases has received much attention. Based on the high expressive power and effective reasoning service of Description Logics (DLs), this paper proposes a DL approach for automatically extracting knowledge from fuzzy relational databases (FRDB). To represent th...

Today XML has reached a wide acceptance as the data exchange format for e-commerce. Unfortunately, XML covers the syntactic level, but lacks semantics. Ontology can represent shared domain knowledge and enable semantic interoperability. Therefore, in this paper, we propose an approach for representing and reasoning on XML with ontologies. The forma...

UML is the most widely accepted formalism for the analysis and design of software. Recent proposals to improve the ability of reasoning automatically on UML models. However, information imprecision and uncertainty exist in many real-world applications and hence fuzzy UML models have been extensively investigated. In this paper, we propose a descrip...

Significant research efforts in the Semantic Web community are recently directed toward the representation and reasoning with fuzzy ontologies. Description logics (DLs) are the logical foundations of standard Web ontology languages. Conjunctive queries are deemed as an expressive reasoning service for DLs. This chapter focuses on fuzzy (threshold)...

Fuzzy ontologies are deemed as useful formalisms for dealing with vagueness in the Semantic Web community. Description logics (DLs) are the logical foundations of standard web ontology languages. Conjunctive queries are deemed as an expressive reasoning service for DLs. DL reasoners can be enriched by a conjunctive query service. In this study, we...

We propose an flexible extension of SPARQL by introducing fuzzy set theory and the α-cut operation of fuzzy numbers into SPARQL. We show how to efficiently compute the top-k answers of flexible SPARQL queries with regard to membership degrees and user-defined weights. Based on our method, a flexible query service is implemented and evaluated on the...

In the Semantic Web context, information would be retrieved, processed, shared, reused and aligned in the maximum automatic way possible. Our experience with such applications in the Semantic Web has shown that these are rarely a matter of true or false but rather procedures that require degrees of relatedness, similarity, or ranking. Apart from th...

Significant research efforts in the Semantic Web community are recently directed toward the representation and reasoning with fuzzy ontologies. As the theoretical counterpart of fuzzy ontology languages, fuzzy Description Logics (DLs) have attracted a wide range of concerns. With the emergence of a great number of large-scale domain ontologies, the...

How to quickly and cheaply construct Web ontologies has become a key technology to enable the Semantic Web. Classical ontologies are not sufficient for handling imprecise and uncertain information that is commonly found in many application domains. In this paper, we propose an approach for constructing fuzzy ontologies from fuzzy UML models, in whi...

Existing fuzzy description logic (DL) reasoners either are not capable of answering conjunctive queries, or only apply to DLs with less expressivity. In this paper, we present an algorithm for answering expressive fuzzy conjunctive queries, which allows the occurrence of both lower bound and the upper bound of thresholds in a query atom, over the r...

The shortcomings of existing description logics in the representation of fuzzy knowledge and data types are analyzed and a kind of new fuzzy description logic named F-SHOIQ(G) is proposed in the paper. F-SHOIQ(G) can support not only the representation of fuzzy knowledge, but also the representation of fuzzy data information with customized fuzzy d...

Based on negation and negation as failure, in the paper, we propose a new rule language-fuzzy nonmonotonic semantic Web rule language (f-NSWRL), which suffices to represent and reason nonmonotonic fuzzy knowledge. The issue of priority is discussed and the deciding principles of priorities of competing rules are offered. To make RuleML play a role...

Rules have been playing an increasingly important role in the Semantic Web. However, general rule languages are not capable of representing much imprecise and uncertain knowledge in the Semantic Web, nor are if-then rules. Combining if-then rules with OWL DL in the framework of fuzzy sets and possibility distribution, we propose fuzzy Semantic Web...

In the Semantic Web context, information would be retrieved, processed, shared, reused and aligned in the maximum automatic way possible. Our experience with such applications in the Semantic Web has shown that these are rarely a matter of true or false but rather procedures that require degrees of relatedness, similarity, or ranking. Apart from th...

In this paper, we present the very first algorithm for answering expressive fuzzy conjunctive queries, which allow the occurrence of both lower bound and the upper bound of threshold in a query atom, over the relative expressive fuzzy DL, f-ALC. The algorithm we suggest here can easily be adapted to existing (and future) DL implementations. We dist...

This paper mainly discusses the fuzzy extension to the data type representation mechanism of the existing XML schema and OWL. In accordance with the needs of practical application, we prompt a kind of fuzzy extension form to XML schema data typing and point out the limitations of OWL data typing. Furthermore, we mainly investigate the description l...

The current research progress and shortages of the conjunctive query answering to description logic are analyzed. Based on the description logic DL-Lite and fuzzy set theory, a new fuzzy description logic f-DLR-Lite∩ is presented to represent and process n-ary relation and allow the concept conjunction to occur in the left-hand of inclusion axioms,...

Description logics (DLs) play an important role in representing and reasoning domain knowledge. Conjunctive queries stemmed from the domain of relational databases, and have attracted more attentions in semantic Web recently. To acquire a tractable DL for query answering, DL-Lite is proposed. Due to the large amount of imprecision and uncertainty i...

In the real world, human knowledge and natural language have a big deal of imprecision and uncertainty. Imprecision and uncertainty play in the semantic Web context, as well as to many applications that use description logics (DLs) to capture, represent and perform reasoning with domain knowledge. In this paper, a fuzzy extension of description log...

The Semantic Web is expected to process concept knowledge and data information in an intelligent and automatic way. Recent research has shown that OWL has a serious limitation on data types; i.e., it does not support customized data types and customized data type predicates. Furthermore, it canpsilat process imprecise and uncertain information whic...