=Paper=
{{Paper
|id=Vol-2821/paper5
|storemode=property
|title=Representing Scientific Literature Evolution via Temporal Knowledge Graphs
|pdfUrl=https://ceur-ws.org/Vol-2821/paper5.pdf
|volume=Vol-2821
|authors=Anderson Rossanez,Julio Cesar Dos Reis,Ricardo Da Silva Torres
|dblpUrl=https://dblp.org/rec/conf/semweb/RossanezRT20
}}
==Representing Scientific Literature Evolution via Temporal Knowledge Graphs==
https://ceur-ws.org/Vol-2821/paper5.pdf
Representing Scientific Literature Evolution via
Temporal Knowledge Graphs
Anderson Rossanez1 , Julio Cesar dos Reis1 , and Ricardo da Silva Torres2
1
Institute of Computing, University of Campinas, Campinas - SP, Brazil
{anderson.rossanez, jreis}@ic.unicamp.br
2
Department of ICT and Natural Sciences, Norwegian University of Science and Technology,
Ålesund, Norway
ricardo.torres@ntnu.no
Abstract. Scientific publications register the current knowledge in a specific do-
main. As new researches are conducted, knowledge evolves, getting documented
in dissertations, theses and articles. In this article, we introduce new methods
that exploit Temporal Knowledge Graphs (TKGs) to model temporal knowledge
evolution in corpora of unstructured texts. In our approach, complex network
measurements are applied over TKGs to determine the relevance of concepts
dealt with in the corpora under analysis. We demonstrate the effectiveness of
our method by conducting experimental analyses on TKGs constructed from a
corpus of scientific papers extracted from different editions of the International
Semantic Web Conference (ISWC). The results demonstrate the effectiveness of
the method in representing and tracking the knowledge evolution over time.
Keywords: Temporal Knowledge Graphs · Information Retrieval · Knowledge
Evolution · Complex Network Measurements
1 Introduction
Scientific publications describe studies that contribute to advancements in the state of
the art of a given research domain. Such publications document new methodologies and
findings, as well as new data, from which new knowledge is produced. As researches are
continuously conducted, an overwhelming amount of scientific studies become avail-
able on a timely basis, documenting the evolution of knowledge in a field of study.
To further illustrate it, one may refer to scientific articles published from time to
time in journals, focusing on studies from a specific field, or in the proceedings of
important conferences released every year. For example, the proceedings of the Inter-
national Semantic Web Conference (ISWC)3 is a relevant corpora source representing
the evolution of the state of the art in the Semantic Web domain.
The amount of papers submitted and published in journals and conferences has in-
creased drastically over the years. Proper reading and understanding, as well as tracking
new knowledge from such amount of publications is now a very hard task to be accom-
plished without proper help. Our claim is that computational methods are a relevant
3
https://link.springer.com/conference/semweb (As of Aug. 2020).
Copyright c 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
solution to help scientists understand the trends and needs of their research domain.
This requires investigations to adequately extract information and represent knowledge
conveyed in the scientific literature, as well as tracking the evolution of knowledge in a
temporal manner from texts.
Building knowledge representations requires the determination of facts conveyed
in a portion of a scientific text. Facts are described by relations between entities in the
text. For instance, let us consider a text in an initial time frame, containing the sentence
The Scholarly Ontology documents research processes. The entities Scholarly Ontology
and research processes relate to each other through the verb documents. The knowledge
representation for the sentence, therefore, relies on those entities and relation. Further-
more, one may notice that, Scholarly Ontology is a sub-type of Ontology, and research
processes is a sub-type of processes. If we consider another text, in a subsequent time
frame, containing another sentence, e.g., The Gene Ontology describes the function of
genes. There is a new knowledge representation, that relies on the relation (describes),
between two entities (Gene Ontology, and function of genes). Similarly, Gene Ontol-
ogy is a sub-type of Ontology, and function of genes is a function, pertaining to genes.
Considering both time frames, one may observe that there are two sub-types of Ontol-
ogy: Scholarly Ontology, and Gene Ontology, both having their specific relations with
other entities, which are, in turn, sub-types of other entities. In this sense, knowledge
regarding the ontology concept evolves across the considered time frames. In the first
time frame, we knew the existence of Scholarly Ontology and the relations in which
such concepts take part. In the second time frame, we had the Gene Ontology concept
and its relations aggregated to the overall knowledge. Such concepts and relations were
not known in the initial time frame. This illustrates the relevance of modeling temporal
aspects of knowledge.
In this research work, we address the following research questions: (1) How to rep-
resent knowledge conveyed in temporal corpora of natural language texts? (2) How
to characterize the knowledge evolution in such temporal corpora of natural language
texts?
Knowledge Graphs (KGs) [9] are computational tools that model knowledge by
means of the interrelations of real-world entities in facts, through a graph format. Link-
ing graph entities and properties from a KG with computational ontologies, which de-
scribe concepts from a specific domain, helps in the comparison of different KGs that
are mapped to the same, or mapped, ontologies. We address the concept of Tempo-
ral Knowledge Graphs (TKGs) as a way to benefit from the graph format to represent
temporal aspects from texts in KGs.
In this article, we investigate the use of TKGs to represent a corpus of unstructured
scientific texts, and how they evolve over time. The generation of KGs from scientific
literature is still an open research problem. In our previous work [11], we investigated
how to generate KGs from unstructured texts via the KGen tool. However, how to an-
alyze and compare several generated KGs in a temporal perspective requires further
investigation. The way of representing and characterizing the evolution of knowledge
over time via KGs is the main novelty aspect in this work.
In summary, our objectives are: (1) enhance the KGen method and tool introduced
in our previous work [11], originally conceived considering biomedical texts, to semi-
automatically generate ontology-linked TKGs for corpora of unstructured texts in the
Computer Science domain; and (2) propose a method to analyze and compare the gen-
erated TKGs using centrality metrics obtained from complex networks to characterize
the evolution of knowledge over time. In our evaluation, we use a set of temporal texts
obtained from articles in the proceedings of previous editions of the ISWC conference.
The Computer Science Ontology [12] was used to determine and link the entities from
the TKGs.
The remaining of this paper is organized as follows: Section 2 discusses related
work; Section 3 presents our method; Section 4 shows the evaluation with TKGs gen-
erated from temporal sets of scientific papers; Section 5 discusses our findings. Finally,
Section 6 closes the paper presenting conclusions and future work.
2 Background and Related Work
Studies that deal with KGs consider a different concept of TKGs from the one we
consider in this work. Han et al. [3] considered TKGs as a graph that captures the
moment in which a represented fact happened. Such facts are represented by quadruples
(subject, predicate, object, and time), rather than common triples. Their work proposed
a neural network approach to predict future events that should be added to the graph.
Similarly, a survey on KGs [4] considered TKGs as graphs that incorporate the time
aspect trough quadruple structures.
Trivedi et al. [14] proposed a deep learning method to infer the moment in which
some events in the graph occur, based on events in which the time information is ex-
plicit. All these studies considered a single, but rather large, graph to perform the pro-
posed inferences. Liu et al. [6] proposed a framework to derive a new TKG that predicts
future events, considering the weights of graph vertices. Their work employed a quadru-
ple representation to model the temporal facts.
As for graph metrics, Park et al. [8] used centrality measurements, combined with
a neural model, to determine the most important nodes of a KG. Shi et al. [13], in turn,
presented a method to extract key-phrases from texts by means of strategies that are
based on degree, closeness, betweenness, and eigenvector centrality measures taken for
the nodes of a KG that represent the text.
Gengchen et al. [7] described an approach to infer the weights of KG nodes by
means of centrality measures, such as degree, eigenvector, and betweenness centralities.
Such measures are fed into a neural model, and used to determine the centrality of nodes
from secondary graphs.
Existing literature dealing with TKGs often considered a quadruple representation
that embeds temporal information, rather than the traditional representation in form of
triples. The quadruple representation allows for encoding distinct temporal information
in the same graph. In our work, triples that constitute each graph generated for the
same temporal set of texts, represent facts from the same temporal time frame, e.g., a
KG generated for the proceedings from a given year has facts that hold true for that
year. For this reason, we consider the traditional triple format in our TKG approach.
Merging our TKGs into a single graph, considering the quadruple format, would result
in a TKG as defined in literature. To the best of our knowledge, there is no study in
literature considering centrality measurements in TKGs to characterize the evolution of
knowledge in a temporal manner.
3 Construction and Analysis of TKGs
This section describes our method to represent time-evolving scientific texts by means
of ontology-linked TKGs. In our approach, we explore analyses based on complex net-
works’ centrality measurements in KGs to determine how the represented knowledge
evolves over time. Figure 1 presents an overview of our method.
Fig. 1: Method overview. KGen [11] is depicted in step 1. Steps 2 and 3 are the contri-
butions of this work.
A Knowledge Graph KG is formally described as a set of vertices V and edges
E, i.e., KG = (V, E), where the vertices represent entities, and the edges represent
the relations among such entities. A KG can also be defined as a set of RDF triples,
composed of subject, predicate, and object defined as t = (s, p, o). The subjects and
objects are vertices, and the predicates are the edges in a KG, resulting that a KG =
{t0 , t1 , · · · , tn }, where t0 = (s0 , p0 , o0 ), t1 = (s1 , p1 , o1 ), · · · , tn = (sn , pn , on ).
An ontology O describes a real-world domain in terms of concepts, represented by
classes CO interrelated by directed relations R, and a set of attributes AO , i.e., O =
(CO , RO , AO ). Subjects and objects from triples in a KG may be linked to an ontology,
denoting that they are instances of those particular ontology’s entities.
In this work, we consider a Temporal Knowledge Graph as a KG in which the
triples represent facts that are available in a determined time frame i, i.e., KG i =
{ti0 , ti1 , · · · , tin }. In this sense, if we consider KGs in two different time frames (i and
i + 1), i.e., KG i = {ti0 , ti1 , · · · , tin }, and KG i+1 = {ti+1 i+1 i+1
0 , t1 , · · · , tn }, we may
i i+1
have triples like ta ∈ KG and ta ∈ KG , meaning that the fact described by ta is
available in both time frames. We may also have triples like tb ∈ KG i and tb ∈ / KG i+1 ,
i.e., the fact described by the tb is available only in the time frame i, and not in i + 1.
The triples’ constituents in the TKG may also be linked to ontologies. Linking nodes of
the TKGs to ontologies, are a key step to enable the comparison in between TKGs, as
nodes linked to the same ontology’s classes, atributes, or properties, denote an instance
of the same concept, although in different time frames.
To generate TKGs, we evolved the KGen tool [11] to consider determining named
entities from other domains in the text for triples extraction. The extracted triples’ con-
stituents are matched and linked to an ontology. KGen was enhanced to allow batch
processing for generating TKGs taking multiple unstructured texts as input. A set of
texts from a determined time frame produce a single KG as output, i.e., the TKG for
that specific time frame. A set of TKGs are the output of multiple sets of unstructured
texts from different time frames.
Once the set of TKGs is computed, KG 0 , KG 1 , · · · , KG m , we analyze and deter-
mine triples and entities available in different subsets of TKGs. Each generated KG,
though, may contain an overwhelmingly large amount of triples, whose constituents
may be not linked to the target ontology, making it difficult to determine if nodes from
different TKGs are instances of the same concepts. For this reason, we consider the
graph nodes that are linked to the target ontology in the analysis.
Due to the possibly huge amount of edges (nodes) and vertices that such KGs may
contain, our analyses consider only the most relevant nodes in the KG. For this purpose,
we take centrality measurements for complex networks [1] in each node in order to de-
termine its relevance in the graph. In particular, we consider three measurements in our
method validation (cf. Section 4): degree centrality [5], eigenvector centrality [2], and
betweenness centrality [1]. Such measures denote, respectively, the amount of neigh-
bouring nodes linked to a particular node, the number of nodes in the overall graph that
have connections to a particular node, and the amount of shortest paths that a particular
node is part of. In our solution, ontology-linked nodes with the highest centrality val-
ues for all the graphs enables evaluating how knowledge evolves. The analysis of these
measurements change in between the temporal KGs, and thus, over time.
Regarding implementation aspects,4 our temporal analyses on TKGs were imple-
mented using the Python programming language. The TKGs are handled using RD-
FLib.5 The centrality measurements are computed using APIs from the NetworkX6
library.
4 Evaluation
We applied our solution to generate and assess TKGs by using abstracts of the research
track papers, obtained from the proceedings of three subsequent editions of the ISWC
conference (2017 to 2019). The sets of papers for each year were used as input to
our solution generating three ontology-linked TKGs (available online7 ). The Computer
Science Ontology (CSO) [12] was used to assist the method in determining named
entities from the computer science domain in the text for triples extraction.
4
https://github.com/rossanez/TKGAnalyzer (As of Aug. 2020).
5
https://github.com/RDFLib (As of Aug. 2020).
6
https://networkx.github.io/ (As of Aug. 2020).
7
https://github.com/rossanez/KGen (As of Aug. 2020).
Aiming to identify the most relevant concepts dealt within each edition of the ISWC
conference, we conducted three analyses based on distinct centrality measurements,
which are widely used in literature, in each KG: degree centrality, eigenvector centrality,
and betweenness centrality. Figure 2 summarizes the top values for each measurement.
(a) Degree Centrality. Concepts: (0) ontol- (b) Eigenvector Centrality. Concepts: (0)
ogy, (1) sparql, (2) ontologies, (3) rdf, (4) de- rdf data, (1) linked data, (2) machine learning,
scription logic, (5) knowledge base, (6) knowl- (3) caching, (4) knowledge base, (5) ontology, (6)
edge bases, and (7) semantics. ontologies, (7) sparql, and (8) knowledge bases.
(c) Betweenness Centrality. Concepts: (0) on-
tology, (1) ontologies, (2) description logic, (3)
sparql, (4) linked data, (5) rdf, (6) knowl-
edge base, (7) semantics, and (8) dbpedia.
Fig. 2: Top centrality values and their evolution. Values are normalized, and concepts
are preceded by [https://cso.kmi.open.ac.uk/topics/].
Degree centrality. The degree centrality of a node denotes the amount of nodes that
are directly connected to it (i.e., its immediate neighbors). Let G = (V, E) be a graph,
represented by a |V | × |V | adjacency matrix A with elements aij . The degree centrality
PN
is defined as cDi = j=1 φij , φij = 1, if aij > 0, and 0, otherwise (i = 1, 2, · · · , N );
where cDi is the degree of node i and N is the number of nodes. The normalized degree
cD
centrality is computed by nci = N −1 i
. Figure 2a shows the concepts from the CSO
ontology represented by the nodes with the highest degree centrality for each ISWC
edition (the complete list, along with sub-graph examples, is available online8 ).
Considering our TKGs, the nodes with the highest degree centrality values represent
concepts from the CSO ontology that mostly occur among all abstracts of that particular
8
https://github.com/rossanez/TKGAnalyzer (As of Aug. 2020).
edition. In 2017, ontology has the top value; in 2018, RDF; and in 2019, ontology has
the top value. Figure 3 presents the immediate neighbors for the node representing the
ontology concept in the TKGs for the ISWC editions considered in the analysis. We
observe that the amount of immediate neighbors vary in between the editions: in 2017,
it has 31 neighbor nodes; in 2018, 22 neighbor nodes; and in 2019, it has 32 neighbor
nodes.
(a) 2017 (b) 2018
(c) 2019
Fig. 3: Sub-TKGs for each conference edition. This figure presents a subgraph from
our TKGs generated (from the three corpus under study) considering the ontology con-
cept. Ontology node is shown in red, and its immediate neighbors are in green.
Figure 2a shows that the degree centrality of concepts vary over time. SPARQL, for
instance, increases from 2017 to 2018, and then decreases in 2019; Ontologies decreases
from 2017 to 2018, and increases to its highest value in 2019; Description logic is
available in 2017, not available in 2018, and reappears with a lower value in 2019.
Eigenvector centrality. The eigenvector centrality of a node denotes the amount of
nodes that are directly connected to it, extending to nodes directly connected to those,
and so forth throughout the network. It is a measurement of the influence that a node
has on the network, relative to the number of connections
PNthat this node has to all the
other nodes of the entire network. Formally, λcE i = i a cE
ij j (i = 1, 2, · · · , N ),
E
λc = Ac; where ci is the eigenvector centrality of node i, c is an N -dimensional
vector whose entry i represents the centrality score of node i. This formulation leads
to the problem of finding the eigenvalues (λ) and the eigenvectors (c) of the adjacency
matrix A. The eigenvector centrality is associated with the dominant eigenvalue found.
Figure 2b presents the concepts from the CSO ontology represented by the nodes with
the highest eigenvector centrality values over the considered ISWC editions.
The interpretation of the eigenvector centrality in our TKGs is that nodes with the
highest values, connected to most of the other nodes, are highly related to the overall
context of the conference at that edition. In 2017, RDF data has the highest eigenvector
centrality value; in 2018, caching has the top value; and in 2019, it is ontologies.
The analysis shows that not all nodes with high degree centrality (cf. Figure 2a) are
the same that have high eigenvector centrality (cf. Figure 2b). This particularly occurs
to nodes with high degree centrality, whose neighbors have low eigenvector centrality,
i.e., the eigenvector centrality of a node highly depends on the eigenvector centrality of
its neighbors.
Betweenness centrality. The betweenness centrality denotes how many times a
node lies between the shortest path between two different nodes. A node with high be-
tweenness centrality serves as a bridge in many shortest paths between different nodes,
being either in a more centered position of the network, or in an important cluster. It
PN PN ηjk (i)
can be computed as follows: cB i = j=1 k=1 ηjk (i = 1, 2, · · · , N ); where N
j6=i k6=i,j
is the number of nodes, ηjk is the number of the shortest paths from node j to node k
and ηjk (i) is the number of the shortest paths from j to k that contain node i. Figure 2c
shows the concepts from the CSO ontology represented by the nodes with the highest
betweenness centrality values for the considered ISWC editions.
Considering our TKGs, nodes with higher betweenness values are those represent-
ing concepts that are related to most of the other concepts represented in the graphs, as
they lie between the paths to most of the other concepts’ nodes. It is the case of ontol-
ogy, in 2017; RDF, in 2018; and ontology, in 2019. Considering the previous centrality
measurement, a node may have a high betweenness centrality, but a low eigenvector
centrality value, if it links nodes that are disconnected from the overall network. For
this reason, not all the concepts with the top values from Figure 2c (Betweenness) are
the same as Figure 2b (Eigenvector).
5 Discussion
We proposed the generation of ontology-linked TKGs to represent the knowledge con-
veyed in sets of temporal texts. Our approach explored complex networks’ central-
ity measurements to characterize the evolution of knowledge in between specific time
frames. We applied our solution to the Computer science domain by analyzing a set of
papers from distinct editions of the ISWC.
We found that our proposal was successful to represent knowledge conveyed in
temporal corpora of natural language texts. The use of the distinct complex networks’
centrality measurements was suited to help analyzing and characterizing the knowledge
evolution in such temporal corpora of natural language texts. We judge our proposal
of TKGs appropriate as means to conduct the analyses because KGs are well suited to
represent knowledge in terms of facts. In addition, our generated TKGs can be retrieved
via SPARQL queries whenever required.
The language employed in scientific papers from the computer science domain
poses some extra challenges in the generation of TKGs. One of such aspects is the
use of LaTex mathematical expressions (even on abstracts), that, when converted to
plain text format, generate incorrect information (especially due to characters such as
$, %, {, }, etc.). Despite such challenge, KGen was able to run to completion, but some
meaningless triples are generated. For this reason, and taking advantage of the fact that
KGen is a semi-automated method, some manual interventions were performed. There-
fore, the preprocessing of the text requires further improvements for smoothly batch
processing corpora of unstructured texts.
Regarding performance aspects, the generation of KGs using KGen is indeed a
time-consuming procedure, even for relatively small texts such as abstracts. This is
mainly due to the NLP tools and techniques that are employed, which take a consid-
erable amount of time to process texts. As for the centrality measurements, although
reading a KG in turtle format, and converting it to a complex network object does not
take a great amount of time, some measurements, especially the betweenness centrality,
take a considerable amount of time to complete. Computing the shortest paths for all
possible pairs of nodes is indeed a time-consuming procedure, especially true for large
KGs.
As for the centrality measurements, the aspect of considering nodes that represent
entities that match concepts from the CSO ontology opens opportunities of improve-
ments in our method. Certainly, there will be nodes that have high centrality values, that
are left out of the analysis. Possibly, considering multiple ontologies to match and link
concepts, and taking advantage of mappings between ontologies could be a solution to
tackle this improvement opportunity. Furthermore, extra centrality measurements, be-
sides the three considered in this work, could be incorporated to the analysis, to further
enrich it.
We plan to address a better strategy to describe temporal evolution of the concepts
aiming to add value in closing the analysis. Bar plots can be confusing to visualize
and understand. A possible future work therefore would be to study the feasibility of
using a graph-based visual structure to represent such evolution [10]. We also plan to
further extend and enhance our results by considering all available proceedings of the
ISWC conference. Additional metrics and analyses opportunities shall be considered,
and possibly considering predictions of the most important concepts on future editions
of the conference, based on the evolution of the previous editions.
Finally, it is worth mentioning that results obtained in the evaluation are entirely
based on the abstracts of the papers from the ISWC proceedings. We could achieve dif-
ferent results if we consider the entire text from papers. Furthermore, it is important to
enforce that different centrality measurements possibly lead to different most important
concepts.
6 Conclusion
The way of representing and analyzing temporal knowledge is valuable to understand
the evolution of key domain concepts. This article presented a method to characterize
the evolution of knowledge from temporal, unstructured texts in scientific literature,
based on analyses conducted via as we named TKGs. We evaluated our proposal by
building TKGs from abstracts of three subsequent editions of the ISWC conference.
The conducted complex networks centrality measurements analyses show promising
results in determining how the knowledge evolved in between the conference editions.
Future work mainly involves the evaluation of analyses using different complex network
measurements. Also, we plan to define suitable visual structures to represent temporal
knowledge evolution based on network measurements.
Acknowledgment
We thank the São Paulo Research Foundation (FAPESP) (Grant #2017/02325-5).9
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The opinions expressed in this work do not necessarily reflect those of the funding agencies.