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A Study of the Similarities of Entity
Embeddings Learned from Different Aspects of a
Knowledge Base for Item Recommendations
Guangyuan Piao and John G. Breslin
Insight Centre for Data Analytics, Data Science Institute,
National University of Ireland, Galway, Ireland
{guangyuan.piao}@insight-centre.org,{john.breslin}@nuigalway.ie
Abstract.
The recent development of deep learning approaches pro-
vides a convenient way to learn entity embeddings from different aspects
such as texts and a homogeneous or heterogeneous graph encoded in a
knowledge base such as DBpedia. However, it is unclear to what extent
domain-specific entity embeddings learned from different aspects of a
knowledge base reflect their similarities, and the potential of leveraging
those similarities for item recommendations in a specific domain has
not been explored. In this work, we investigate domain-specific entity
embeddings learned from different aspects of DBpedia with state-of-the-
art embedding approaches, and the recommendation performance based
on the similarities of these embeddings. The experimental results on
two real-word datasets show that recommender systems based on the
similarities of entity embeddings learned from a homogeneous graph
via the
dbo:wikiPageWikiLink
property provides the best performance
compared to the ones learned from other aspects.
Keywords:
Deep Learning, Semantic Similarity, Knowledge Base, Entity
Embeddings, Recommender Systems, Knowledge Graph
1 Introduction
Knowledge bases (KBs) such as DBpedia [12] and Wikidata [29] have received
great attention in the past few years due to the embedded knowledge which
is useful for a wide range of tasks including recommender systems [3]. For
example, Linked Open Data-enabled recommender systems (LODRS) aim to
utilize the background knowledge about items (entities) from linked datasets
such as DBpedia for improving the quality of recommendations [6,7]. However,
most previous studies on LODRS view a KB as a heterogeneous knowledge graph
(KG) based on the domain-specific entities and properties defined in an ontology
(e.g., DBpedia ontology). Take DBpedia as an example, the heterogeneous KG
can be seen as one aspect of a knowledge base, and a KB can contain several
aspects of knowledge with respect to entities (see Figure 1) such as:
2 Guangyuan Piao and John G. Breslin
!"#$$%&&'()*$+,-
.%/"0'1"2/3045#6-
!%07%*'8*23%9-
…
wikiPageWikiLink
wikiPageWikiLink
:32*$3';"<$"9&6=-
…
homogeneous graph
thumbnail
visual knowledge
!"#$$%&&-*&-3->?@>-A#&&*32-B$346-4"+%/=C
/03+3-)*$+-B3&%/-"2-D5%-2"9%$-!"#$$%&&…-
abstracts
textual knowledge
830*3';"<5%92*6"93-
starring
!%07%*'8*23%9-
writer
…
heterogeneous graph
Fig. 1: Knowledge about the movie entity Soulless (film) from different
aspects of DBpedia.
–
Textual knowledge: This type of knowledge denotes textual knowledge about
entities, e.g., the abstracts of movies via dbo1:abstracts property.
–
Knowledge from a homogeneous graph: This type of knowledge denotes the
inherited knowledge from Wikipedia
2
based on the
dbo:wikiPageWikiLink
property, which provides a set of connected entities via the same property.
–
Knowledge from a heterogeneous graph: This type of knowledge is powered
by the heterogeneous graph, which consists of domain-specific entities and
other nodes connected to those entities via different properties defined in
the ontology of a KB, and has been widely used for extracting background
knowledge about items (entities) for LODRS.
–
Visual knowledge: This denotes visual information about entities, e.g., the
thumbnails of movies via dbo:thumbnail property.
1The prefix dbo denotes http://dbpedia.org/ontology/
2https://www.wikipedia.org
A Study of Entity Embeddings from Different Aspects of a Knowledge Base 3
Recently, a great number of studies have been proposed to learn entity
embeddings in a KG for the KG completion task [4] or for other classification or
recommendation tasks using those low-dimensional representations of entities as
features [26, 27] based on deep learning approaches. While related work reveals
several insights regarding the entity embeddings learned from the heterogeneous
graph of a KB, there exists little research on understanding the similarities
between those entity embeddings learned from other aspects of KBs. There has
been considerable semantic similarity/distance measures which were designed for
measuring the similarity/distance between entities in the same domain in linked
datasets such as DBpedia for various purposes such as item recommendations in
a cold start. This preliminary work can be seen as being in the same direction
as these studies but with the focus on investigating the similarities between
entity embeddings learned from embedding approaches using deep learning or
factorization models with domain knowledge.
In this preliminary work, we aim to investigate the semantic similarities
of entity embeddings learned from different aspects of a KB, and evaluate
them in the context of item recommendations in the music and book domains.
Specifically, we focus on the textual knowledge, knowledge from a homogeneous
or heterogeneous graph based on dedicated embedding approaches including deep
learning techniques. Deep learning approaches have been proved to be effective on
learning the latent representations of various forms of data such as images, texts,
as well as nodes in networks. Therefore, we use
Doc2Vec
[10] and
Node2Vec
[8] to
learn the entity embeddings based on the textual knowledge and the knowledge
from a homogeneous graph, and use an embedding model for knowledge graph
completion to learn the entity embeddings based on the heterogeneous graph of
a KB. We use DBpedia as our knowledge base in this work. In particular, we are
interested in investigating the following research questions with results in Section
5:
–
How do those entity embeddings learned from different aspects of a KB reflect
the similarities between items (entities) in a specific domain in the context of
item recommendations in a cold start?
–
Do those entity embeddings learned from different aspects complement each
other?
To the best of our knowledge, this is the first work on investigating the
semantic similarities between entity embeddings learned from different aspects of
a KB, and exploring their usages in the context of recommender systems. A shorter
version of this paper was published in the CEUR proceedings of the 1st Workshop
on Deep Learning for Knowledge Graphs and Semantic Technologies [23].
2 Related Work
Here we review some related work on linked data similarity/distance measures
for measuring the similarity/distance between two entities in a specific domain
4 Guangyuan Piao and John G. Breslin
for recommendation purposes, and the approaches exploring entity embeddings
for item recommendations.
2.1 Linked Data Similarity/Distance Measures
LDSD
[19,20] is one of the first approaches for measuring the linked data semantic
distance between entities in a linked dataset such as DBpedia. Leal et al. [11] pro-
posed a similarity measure which is based on a notion of proximity. This method
measures how connected two entities are (e.g., based on the number of paths
between two entities), rather than how distant they are. Piao et al. [21] revised
LDSD
in order to satisfy some fundamental axioms as a distance-based similarity
measure, and further improved it based on different normalization strategies [22].
More recently, Alfarhood et al. [1] considered additional resources beyond the
ones one or two hops away in
LDSD
, and the same authors also proposed applying
link differentiation strategies for measuring the linked data semantic distance
between two entities in DBpedia [2]. In contrast to aforementioned approaches,
Meymandpour et al. [14] proposed a information content-based semantic similar-
ity measure for measuring the similarity between two entities in linked open data
cloud, which can consider multiple linked datasets for measuring the similarity.
In this work, we are interested in the similarities of entity embeddings learned
from different aspects of a knowledge base, and compare those similarities with
one of the semantic similarity/distance measures [22].
2.2 Exploring Entity Embeddings for Item Recommendations
Recently, entity embeddings learned from a knowledge graph using deep learning
approaches have been used for item recommendations. In [27], the authors
proposed
RDF2Vec
, which runs random walks on a heterogeneous RDF
3
graph in
DBpedia, and then applies
Word2Vec
[15,16] techniques by treating the sequences
of triples as sentences. The learned entity embeddings based on the whole KG were
then used to find the k-nearest neighbors of items. Afterwards, those neighbors
were used as side information for factorization machines [25] for providing item
recommendations. In contrast to [27] which uses the whole KG for learning entity
embeddings, we learn domain-specific entity embeddings from different aspects of
a KB. The entity embeddings learned from the whole KG might reflect relatedness
of entities instead of their similarities as they are learned by incorporating all
properties and nodes from other domains. However, related entities are not always
similar, e.g., a musical artist and his/her spouse are related but not similar.
Zhang et al. [30] proposed collaborative knowledge base embedding, which
jointly learn the latent representations in collaborative filtering for item rec-
ommendations as well as the ones for a knowledge base. However, those entity
embeddings were used as features and the similarities between them were not
investigated. Palumbo et al. [18] used domain-specific triples from DBpedia for
learning entity embeddings with
Node2Vec
for item recommendations. In order
3https://www.w3.org/RDF/
A Study of Entity Embeddings from Different Aspects of a Knowledge Base 5
to use
Node2Vec
for the heterogeneous graph based on domain-specific properties,
the authors applied
Node2Vec
to each heterogeneous graph which consists of all
triples based on a single property. Afterwards, those property-specific similarity
scores were used as features for a learning-to-rank framework with the training
dataset. In contrast, we are interested in the entity embeddings learned from the
heterogeneous graph and the similarities between those embeddings.
3 Learning Entity Embeddings from Different Aspects of
DBpedia
In this section, we discuss three state-of-the-art embedding/vectorization ap-
proaches that we adopted for learning entity embeddings based on different
aspects of knowledge from DBpedia.
3.1 Entity Embeddings with Textual Knowledge
Doc2Vec
[10], which is inspired by
Word2Vec
[15, 16], was devised for learning
embeddings for larger blocks of text such as documents or sentences. This model
uses document vectors and contextual word vectors to predict the next word,
which is a multi-class classification task. Figure 2 shows the
Doc2Vec
model,
where each document/paragraph is mapped to a latent vector which is a column
in a document/paragraph matrix D, and each word has its embedding which is
a column in a word embedding matrix W. As we can see from the figure, the
document vector and word vectors are concatenated to predict the next word
in a context. The document and word vectors can be learned by optimizing the
classification error in a given set of documents. For example, with a window size
8, the model predicts the 8th word based on the document and 7 contextual word
vectors. We used the
gensim
[24] implementation of
Doc2Vec
for our experiment.
Fig. 2: Doc2Vec model for learning document/paragraph vector [10].
6 Guangyuan Piao and John G. Breslin
In our experiment, each abstract of an entity in a specific domain is a document,
which is provided by the
dbo:abstracts
property, and the set of all abstracts
is used for learning entity embeddings in a specified domain with the
Doc2Vec
model. The window size is set to 8 in the same way as [10].
3.2 Entity Embeddings with A Homogeneous Graph
Node2Vec
[8], which is also inspired by
Word2Vec
[15], aims to learn latent
representations of nodes for a homogeneous network. It extends the Skip-gram
architecture (see Figure 3) to networks, and optimizes the (log) probability of
observing a network neighborhood for each node. To apply the Skip-gram model
for networks,
Node2Vec
first executes random walks based on a defined searching
strategy, and the sequence of nodes obtained via the search is used for the
Skip-gram model. We used the author’s implementation4for our experiment.
In our study, we treat the graph which consists of all items in a specific
domain and other connected nodes to those items via the
dbo:wikiPageWikiLink
property as the homogeneous graph from DBpedia, and apply
Node2Vec
to learn
the entity embeddings based on this homogeneous graph.
Parameters. We choose smaller values for some parameters compared to the set-
tings in the original paper as there is a great number of
dbo:wikiPageWikiLink
relationships, which takes a long time for training the model due to its ex-
pensiveness. Our settings for the main hyperparameters of
Node2Vec
are as
follows:
–walk length=10: The length of walk for each node.
–num walks=10: The number of walks per node.
–p=q=1
:
p
and
q
denote the return and in-out hyperparameters for random
walks, respectively.
–window size=5: The context size for optimization.
3.3 Entity Embeddings with A heterogeneous Graph
TransE
[4] is a translation-based model for knowledge graph completion by
learning the embeddings of entities and their relationships. In short,
TransE
learns those embeddings in order to satisfy
E
(
s
) +
E
(
p
)
≈E
(
o
) for a valid
triple (
s, p, o
) in a knowledge base, where
E
(
x
) denotes x’s embedding. Although
TransE
has been used for learning entity embeddings for KG completion by
considering all triples in a KG, for item recommendations in a specific domain,
most previous studies extract the domain-specific DBpedia graph which consists
of all entities in that domain and incoming or outgoing nodes via domain-specific
properties [19, 22]. Therefore, to learn domain-specific entity embeddings, we
extract all triples for the entities/items in that domain with relevant properties.
In consistence with a previous work [22], we used the top-15 properties for each
domain in order to obtain all triples for the subjects in that domain. Table 1
A Study of Entity Embeddings from Different Aspects of a Knowledge Base 7
Fig. 3: The Skip-gram model architecture from [16], which aims to learn word
vector representations that are good at predicting the nearby words.
shows those properties we used to extract domain knowledge about items for our
experiment in Section 4.
The dimensionality of entity embeddings is set to 100 for all three approaches,
and the trained embeddings are available at
https://github.com/parklize/
DL4KGS
. For our experiment, we used the HDT [5] dump for the DBpedia 2016-04
version, which is available at http://www.rdfhdt.org/datasets/.
4 Experiment Setup
We evaluate the similarities of entity embeddings learned from different aspects
of DBpedia in the context of cold-start scenarios in recommender systems where
the top-N items are recommended based on the cosine similarities between entity
embeddings, which are learned from different aspects of DBpedia.
4http://snap.stanford.edu/node2vec
8 Guangyuan Piao and John G. Breslin
Music Book
–dct:subject
–dbo:genre
–dbo:associatedBand
–dbo:associatedMusicalArtist
–dbo:instrument
–dbo:recordLabel
–dbo:occupation
–dbo:hometown
–dbo:bandMember
–dbo:formerBandMember
–dbo:currentMember
–dbo:influencedBy
–dbo:pastMember
–dbo:associatedAct
–dbo:influenced
–dct:subject
–dbo:author
–dbo:publisher
–dbo:literaryGenre
–dbo:mediaType
–dbo:subsequentWork
–dbo:previousWork
–dbo:country
–dbo:series
–dbo:nonFictionSubject
–dbo:coverArtist
–dbo:illustrator
–dbo:genre
–dbo:translator
–dbo:recordLabel
Table 1: The top-15 domain-specific properties used for extracting valid triples
from DBpedia and for training TransE.
4.1 Datasets
We use two real-world datasets in the music and book domains for our experiment.
The first dataset is a last.fm dataset from [17], which consists of 232 musical
artists, and the top-10 similar artists for each of the 232 artists obtained from
last.fm. Those top-10 similar artists provided in last.fm for each artist are used as
the ground truth. The second dataset is a dbbook dataset
5
in the book domain,
which consists of 6,181 users and 6,733 items which have been rated by at least
one user. We randomly selected 300 users who have liked at least 10 books for
our experiment. For each user, we randomly chose one item and recommended
items similar to the chosen one based on their similarities. Therefore, the other
books liked by each user except the chosen one are used for our ground truth
here. For both datasets, all items in each dataset are considered as candidate
items for recommendations.
To learn domain-specific entity embeddings, we extracted background knowl-
edge from DBpedia for all entities/items in two domains: the music and book
domains. The subjects in the music domain are the entities that have their
rdf:type
(s) as
dbo:MusicalArtist
and
dbo:Band
, and the subjects in the book
domain are the ones that have their
rdf:type
(s) as
dbo:Book
. After obtaining
all subjects, we further obtain their abstracts, connected nodes (entities and
categories) via the
dbo:wikiPageWikiLink
property, and the connected nodes
5http://challenges.2014.eswc-conferences.org/index.php/RecSys#DBbook_
dataset
A Study of Entity Embeddings from Different Aspects of a Knowledge Base 9
via those properties defined in Table 1. Table 2 shows the details of the domain
knowledge with respect to the music and book domains.
Music Book
# subjects 171,812 76,639
# abstracts 131,622 70,654
# wikiPageWikiLinks 5,480,222 2,340,146
# triples 1,481,335 316,969
Table 2: The statistics of background knowledge about items from different
aspects of DBpedia.
4.2 Evaluation Metrics
The recommendation performance is evaluated by the evaluate metrics below:
– P@N
: Precision at rank N (P@N) is the proportion of the top-N recommen-
dations that are relevant to the user, which is measured as follows:
P@N=|{relevant items@N}|
N
– R@N
: Recall at rank N (R@N) represents the mean probability that relevant
items are successfully retrieved within the top-N recommendations.
R@N=|{relevant items@N}|
|{relevant items}|
– nDCG@N
: nDCG (Normalized Discounted Cumulative Gain) takes into
account rank positions of the relevant items. nDCG@N can be computed as
follows:
nDCG@N=1
IDCG@N
N
X
k=1
2ˆruk −1
log2(k+ 1)
where
ˆruk
is the relevance score of the item at position kwith respect to a
user uin the top-N recommendations, and the normalization factor IDCG@N
denotes the score obtained by an ideal top-N ranking.
We used the paired t-test in order to test the statistical significance where
the significance level is set to 0.05.
10 Guangyuan Piao and John G. Breslin
4.3 Compared Methods
We compare the similarity measures below to evaluate the similarities of item
embeddings based on different aspects of DBpedia:
–Resim
[22]: This is a semantic distance/similarity measure for LOD dataset
such as DBpedia, which measures the similarity based on the direct and
indirect properties between two entities. We use the implementation from
our previous work6for our experiment.
–Cos(Vtk:Doc2Vec)
: This method uses the cosine similarity measure for the
entity embeddings learned from textual knowledge of entities from DBpedia
using Doc2Vec.
–Cos(Vhmk:Node2Vec)
: This method uses the cosine similarity measure for the
entity embeddings learned from homogeneous graph knowledge of entities
from DBpedia using Node2Vec.
–Cos(Vhtk:TransE)
: This method uses the cosine similarity measure for the
entity embeddings learned from heterogeneous graph knowledge of entities
from DBpedia using TransE.
–Cos([Vx, Vy])
: This method uses the cosine similarity measure for the
concatenated entity embeddings learned from several aspects of entities from
DBpedia. For example,
Cos([Vhtk:TransE, Vtk:Doc2Vec ])
denotes the method
using the cosine similarity measure for the concatenated entity embeddings
based on
TransE
and
Doc2Vec
, and
Cos([all])
denotes the concatenated
ones based on all embedding approaches.
5 Results
Figure 4 and 5 show the nDCG@N results and the precision-recall curve of item
recommendations based on the similarities of different entity embeddings in the
music and book domains. Overall, we observe that the recommendations based
on the entity embeddings with
Node2Vec
provide the best performance followed
by the ones with TransE and Doc2Vec.
In both datasets, the results using the embeddings learned from
Node2Vec
significantly outperform the ones learned from
TransE
and
Doc2Vec
, which
show that the great amount of information provided by
dbo:wikiPageWikiLink
reflects the similarities between entities better than other aspects of DBpedia.
We also observe that combining the embeddings based on
TransE
and
Doc2Vec
improves the recommendation performance significantly compared to using the
embeddings learned from
TransE
or
Doc2Vec
. However, combining all embeddings
learned from the three different aspects do not provide further improvement
on the recommendation performance. Also, the concatenated embeddings with
Node2Vec
and other embeddings do not provide better performance compared to
using the ones learned from
Node2Vec
alone, and the results are omitted from
Figure 4 and 5 for clarity.
6https://github.com/parklize/resim
A Study of Entity Embeddings from Different Aspects of a Knowledge Base 11
In the last.fm dataset, we observe some significant improvement of
Node2Vec
and
Cos([all])
over
Resim
. For example, the recommendation performance is
improved by 25.4% and 11.1% with
Node2Vec
and
Cos([all])
compared to using
Resim
. In contrast, there is no statistical difference between the recommendation
performance using those embeddings and using
Resim
in the dbbook dataset.
This might be due to the relatively small size of subjects in the book domain
and their related aspects for training those embeddings.
0.05$
0.15$
0.25$
0.35$
0.45$
0.55$
N=1$ N=5$ N=10$ N=20$
Cos(Vhmk:Node2Vec)$$
Cos(Vtk:Doc2Vec)$$
Cos(Vhtk:TransE)$$
Cos([Vhtk:TransE,$
Vtk:Doc2Vec])$$
Cos([all])$$
Resim$
(a) nDCG@N
0.05$
0.15$
0.25$
0.35$
0$ 0.1$ 0.2$ 0.3$ 0.4$ 0.5$
Cos(Vhmk:Node2Vec)$$
Cos(Vtk:Doc2Vec)$$
Cos(Vhtk:TransE)$$
Cos([Vhtk:TransE,$
Vtk:Doc2Vec])$$
Cos([all])$$
Resim$
(b) P@N (y-axis) and R@N (x-axis) curve when N = 1, 5, 10, 20.
Fig. 4: The performance of item recommendations on the last.fm dataset with all
methods compared.
12 Guangyuan Piao and John G. Breslin
0.05$
0.1$
0.15$
0.2$
0.25$
0.3$
0.35$
N=1$ N=5$ N=10$ N=20$
Cos(Vhmk:Node2Vec)$$
Cos(Vtk:Doc2Vec)$$
Cos(Vhtk:TransE)$$
Cos([Vhtk:TransE,$
Vtk:Doc2Vec])$$
Cos([all])$$
Resim$
(a) nDCG@N
0"
0.05"
0.1"
0.15"
0.2"
0" 0.02" 0.04" 0.06" 0.08" 0.1"
Cos(Vhmk:Node2Vec)""
Cos(Vtk:Doc2Vec)""
Cos(Vhtk:TransE)""
Cos([Vhtk:TransE,"
Vtk:Doc2Vec])""
Cos([all])""
Resim"
(b) P@N (y-axis) and R@N (x-axis) curve when N = 1, 5, 10, 20.
Fig. 5: The performance of item recommendations on the dbbook dataset with
all methods compared.
6 Conclusions and Future Work
In this paper, we investigated the embeddings learned from three different aspects
of DBpedia using state-of-the-art deep learning and embedding-based approaches,
and the recommendation performance based on the similarities captured by those
embeddings in two real-world datasets. The preliminary results indicate that
the entity embeddings learned from the homogeneous graph powered by the
dbo:wikiPageWikiLink
property provide the best performance in the context
of item recommendations compared to the ones learned from other aspects of
A Study of Entity Embeddings from Different Aspects of a Knowledge Base 13
DBpedia. We further explored potential synergies that exist by combining those
embeddings learned from different aspects. The concatenated embeddings with
the ones learned from textual knowledge (using
Doc2Vec
) and the heterogeneous
graph (using
TransE
) significantly improves the performance. This preliminary
study can be seen as a first step towards investigating the similarity between
entity embeddings learned from different aspects of a knowledge base for item
recommendations, and also poses many research questions for future work.
First, although we used state-of-the-art approaches for learning entity embed-
dings from different aspects, there are many other state-of-the-art alternatives for
learning entity embeddings such as
Tweet2Vec
[28] for learning entity embeddings
based on their abstracts, and
ETransR
[13] for learning the embeddings based
on the heterogeneous graph. A further investigation of using other deep learning
and embedding-based approaches for learning entity embeddings for different
aspects of a knowledge base is required.
Secondly, how to choose domain-specific triples out of all triples in the
knowledge base is a remaining question. Using triples extracted with domain-
specific properties might lead to a smaller number of triples for those embedding-
based approaches to learn good entity embeddings. In contrast, using the whole
heterogeneous graph might lead to general entity embeddings which tend to
capture their relatedness instead of the similarities. Further research is needed to
confirm the hypothesis, and a recent approach such as [9] for extracting domain-
specific subgraphs can be further explored for extracting domain-specific triples
for training the entity embeddings in that domain.
Finally, the results of this study showed that concatenating all embeddings
does not further improve the performance, and those results suggest more research
is needed for combining those entity embeddings which are learned from different
aspects of a knowledge base.
Acknowledgments.
This publication has emanated from research conducted
with the financial support of Science Foundation Ireland (SFI) under Grant
Number SFI/12/RC/2289 (Insight Centre for Data Analytics).
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