Do Judge an Entity by Its Name! Entity Typing Using Language Models

Abstract

The entity type information in a Knowledge Graph (KG) plays an important role in a wide range of applications in Natural Language Processing such as entity linking, question answering, relation extraction, etc. However, the available entity types are often noisy and incomplete. Entity Typing is a non-trivial task if enough information is not available for the entities in a KG. In this work, neural language models and a character embedding model are exploited to predict the type of an entity from only the name of the entity without any other information from the KG. The model has been successfully evaluated on a benchmark dataset.

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Do Judge an Entity by its Name! Entity Typing using Language Models
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Do Judge an Entity by its Name!
Entity Typing using Language Models
Russa Biswas1,2, Radina Sofronova1,2, Mehwish Alam1,2,
Nicolas Heist3, Heiko Paulheim3, and Harald Sack1,2
1FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany
firstname.lastname@fiz-karlsruhe.de,
2Karlsruhe Institute of Technology, Institute AIFB, Germany
3University of Mannheim, Germany
{nico,heiko}@informatik.uni-mannheim.de
Abstract. The entity type information in a Knowledge Graph (KG)
plays an important role in a wide range of applications in Natural Lan-
guage Processing such as entity linking, question answering, relation ex-
traction, etc. However, the available entity types are often noisy and
incomplete. Entity Typing is a non-trivial task if enough information
is not available for the entities in a KG. In this work, neural language
models and a character embedding model are exploited to predict the
type of an entity from only the name of the entity without any other
information from the KG. The model has been successfully evaluated on
a benchmark dataset.
Keywords: Entity Type Prediction ·Knowledge Graph Completion ·
Deep Neural Networks.
1 Introduction
Entity Typing is a vital task in Knowledge Graph (KG) completion and con-
struction. The entity types in KGs such as DBpedia, YAGO, Wikidata, etc. are
either extracted automatically from structured data, generated using heuristics,
or are human-curated. These factors lead to incomplete and noisy entity type
information in the KGs. More specifically, in case of DBpedia, the Wikipedia
infoboxes are the primary source of information. The types of the entities in
Wikipedia infoboxes are mapped to the classes in DBpedia. Recent years have
witnessed research in the automated prediction of entity types in KGs using
heuristics [5] as well as neural network-based models [1, 3, 4]. The existing state-
of-the-art (SOTA) models exploit the triples in the KGs whereas others consider
the textual entity descriptions as well. While those approaches work well if there
is a lot of information about an entity, it is still a challenge to type entities
for which there is only scarce information. This paper focuses on predicting the
entity types solely from their label names, e.g., Is it possible to predict that the
entity dbr:Berlin is a place only from its name?. To do so, the SOTA contin-
uous space-based Neural Language Models (NLM) such as Word2Vec, GloVe,

2 R. Biswas et al.
Wikipedia2Vec [11], BERT [6] as well as a character embedding model are ex-
ploited. This work tackles the challenge of insufficient information for the enti-
ties. Since the NLMs are trained on a huge amount of textual data, they provide
implicit contextual information about the entities in their corresponding latent
representations. In this work, the task of entity typing is considered as a classi-
fication problem in which a neural network-based classifier is applied on top of
the NLMs. Furthermore, an analysis of the performance of the different NLMs
for this task is provided.
2 Related Work
A heuristic based approach SDType [5] leverages the relations between the in-
stances to predict the types of the entities. In [3, 4], the authors propose embed-
ding based entity typing models considering the structural information in the
KG as well as the textual entity descriptions. The word embedding models such
as Word2Vec, GloVe, FastText are trained on KGs in [1] to generate the entity
vectors to predict the types of entities. Other language model based entity typ-
ing models are proposed in MuLR [10] and FIGMENT [9] in which multi-level
representations of entities are learned by using character, word, and entity em-
beddings. However, these entity type prediction models based on NLMs do not
restrict themselves to only the label names and consider the other information
available in the KGs. In [8], the authors propose a model in which the pre-trained
RDF2Vec vectors are used to predict the entity types using a classifier. Also, the
meaningfulness of the entity names in Semantic Web has been studied in [7].
However, unlike the SOTA models, in this work, the NLMs are leveraged to gen-
erate the entity embeddings from the names of the entities for the task of entity
type prediction.
3 Model
This section discusses the NLMs and the classifiers used for the task of entity
typing only from the names of the entities.
Word2Vec. It aims to learn the distributed representation for words reduc-
ing the high dimensional word representations in a large corpus. The CBOW
Word2Vec model predicts the current word from a window of context words and
the skip-gram model predicts the context words based on the current word.
GloVe. GloVe exploits the global word-word co-occurrence statistics in the cor-
pus with the underlying intuition that the ratios of word-word co-occurrence
probabilities encode some form of the meaning of the words.
BERT. Bidirectional Encoder Representations from Transformers is a contex-
tual information based embedding approach in which pretraining on bidirectional
representations from unlabeled text by using the left and the right context in all
the layers is performed.
Wikipedia2vec. The model jointly learns word and entity embeddings from
Wikipedia where similar words and entities are close to one another in the vector

Do Judge an Entity by its Name! Entity Typing using Language Models 3
space. It uses three submodels to learn the representation namely: Wikipedia
Link Graph Model, Word-based skip-gram model, and Anchor context model.
Character Embedding. Character embedding represents the latent represen-
tations of characters trained over a corpus which helps in determining the vector
representations of out-of-vocabulary words.
Embeddings of the Entity Names. In this work, pre-trained Word2Vec model
on Google News dataset4, GloVe model pre-trained on Wikipedia 2014 ver-
sion and Gigaword 55, Wikipedia2Vec model pre-trained on English Wikipedia
2018 version6, and pre-trained English character embeddings derived from GloVe
840B/300D dataset7are used with a vector dimension of 300. The average of
all word vectors in the entity names is taken as the vector representation of the
entities. For BERT, the average of the last four hidden layers of the model is
taken as a representation of the names of entities and the dimension used is 768.
Classification. In this work, entity typing is considered a classification task
with the types of entities as classes. Two classifiers have been built on top of
the NLMs: (i) Fully Connected Neural Network (FCNN), and (ii) Convolutional
Neural Network (CNN). A three-layered FCNN model consisting of two dense
layers with ReLU as an activation function has been used on the top of the
vectors generated from the NLMs. The softmax function is used in the last layer
to calculate the probability of the entities belonging to different classes. The
CNN model consists of two 1-D convolutional layers followed by a global max-
pooling layer. ReLu is used as an activation function in the convolutional layers
and the output of the pooling layer is then passed through a fully connected
final layer, in which the softmax function predicts the classes of the entities.
4 Evaluation
This section consists of a detailed description of the datasets used for evaluating
the models, followed by an analysis of the results obtained.
Datasets. The experiments are conducted on the benchmark dataset DBpe-
dia630k [12] extracted from DBpedia consisting of 14 non-overlapping classes8
with 560,000 train and 70,000 test entities. However, predicting fine-grained type
information of an entity only from its name is a non-trivial task. For e.g. identify-
ing dbr:Kate Winslet as an Athlete or Artist from only the entity name is chal-
lenging. Therefore, seven coarse-grained classes of the entities in this dataset are
considered: dbo:Organisation,dbo:Person,dbo:MeanOfTransportation,dbo:Place,
dbo:Animal,dbo:Plant, and dbo:Work. Also, 4.656% of the total entities in the
train set and 4.614% entities in the test set have their type information men-
tioned in their RDF(S) labels. For example, dbr:Cybersoft (video game company)
has the label Cybersoft (video game company) stating that it is a Company.
4https://code.google.com/archive/p/word2vec/
5http://nlp.stanford.edu/data/glove.6B.zip
6https://wikipedia2vec.github.io/wikipedia2vec/pretrained/
7https://github.com/minimaxir/char-embeddings/blob/master/output/
8https://bit.ly/3bBgjiV

4 R. Biswas et al.
Table 1. Results on the DBpedia630k dataset (in accuracy %)
Embedding Types in Labels no Types in Labels CaLiGraph Test Set
Models FCNN CNN FCNN CNN FCNN CNN
word2vec 80.11 46.71 72.08 44.39 48.93 25.91
GloVe 83.34 54.06 82.62 53.41 61.88 31.3
wikipedia2vec 91.14 60.47 90.68 57.36 75.21 36.97
BERT 67.37 62.27 64.63 60.4 53.42 35.55
character embedding 73.43 58.13 72.66 58.3 54.91 45.73
Therefore, the experiments are conducted both with and without the type infor-
mation in the names for the DBpedia630k dataset. To evaluate the approaches
independently of DBpedia, we use an additional test set9composed of entities
from CaLiGraph [2]. The latter is a Wikipedia-based KG containing entities ex-
tracted from tables and enumerations in Wikipedia articles. It consists of 70,000
entities that are unknown to DBpedia and evenly distributed among 7 classes.
Results. The results in Table 1 depict that for all the NLMs, FCNN works
better compared to the CNN model. This is because the CNN model does not
work well in finding patterns in the label names of the entities. Also, BERT
performs the worst in predicting the type of the entities from their label names.
Further error analysis shows that only 4.2% of the total person entities in the
test set with Types in Labels variation of the dataset have been correctly iden-
tified as dbo:Person for BERT. Since the names of persons can be ambiguous
and BERT is a contextual embedding model, the vector representations of the
entities generated only from their label names do not provide a proper latent
representation of the entity. However, FCNN achieves an accuracy of 84.74%
on the same dataset without the class dbo:Person for BERT. On the other
hand, Wikipedia2Vec works best amongst all the NLMs for FCNN with an ac-
curacy of 91.14% and 90.68% on the Types in Labels and no Types in Labels
variants of the dataset respectively. Also, on removal of the class dbo:Person
from the dataset, it achieves an accuracy of 91.01% on Types in Labels vari-
ant. Therefore, the decrease of 0.13% in the accuracy infers that entities of the
class dbo:Person are well represented in the entity vectors obtained from the
pre-trained Wikipedia2Vec model.
However, after removing the type information from the name labels, a slight
drop in the accuracy for each model has been observed for both the classifiers.
Wikipedia2Vec and the character embedding model experience the smallest drop
in accuracy of 0.46% and 0.77% with the FCNN classifier. This is because DB-
pedia entities are extracted from Wikipedia articles, therefore the vectors of the
entities are well represented by the Wikipedia2Vec model. Also for character
embedding, removal of the type information from their labels has low impact
because the vector representation of the entity names depends on the corre-
sponding character vectors and not word vectors. Furthermore, an unseen test
9http://data.dws.informatik.uni-mannheim.de/CaLiGraph/whats-in-a-name/whats-
in-a-name caligraph test-balanced70k.csv.bz2

Do Judge an Entity by its Name! Entity Typing using Language Models 5
set from CaLiGraph has been evaluated on the classification model trained on
the no Types in Labels variation of the dataset. On the CaLiGraph test set, the
FCNN model achieves the best results with the Wikipedia2Vec model with an
accuracy of 75.21%. The entities in the CaLiGraph test set are not contained in
DBpedia, hence the representations of these entities are not learned during the
training of the Wikipedia2Vec model. This depicts the robustness of the proposed
model and the entity vectors generated by taking average of the word vectors
present in the names of the entities provides a better latent representation.
5 Conclusion and Future Work
In this paper, different NLMs for entity typing in a KG have been analyzed. The
achieved results imply that NLMs can be exploited to get enough information
to predict the types of entities in a KG only from their names. In the future,
fine-grained type prediction using other features from the KG using the NLMs
is to be explored.
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