=Paper=
{{Paper
|id=Vol-2306/paper4
|storemode=property
|title=Knowledge Discovery and Enrichment from Scholarly Data for Expert Finding
|pdfUrl=https://ceur-ws.org/Vol-2306/paper4.pdf
|volume=Vol-2306
|authors=Stella Zevio
|dblpUrl=https://dblp.org/rec/conf/ekaw/Zevio18
}}
==Knowledge Discovery and Enrichment from Scholarly Data for Expert Finding==
https://ceur-ws.org/Vol-2306/paper4.pdf
Knowledge discovery and enrichment from
scholarly data for expert finding
Stella Zevio1[0000−0003−0877−3633]
LIPN - CNRS UMR 7030 - Université Paris XIII
99 avenue Jean-Baptiste Clément, 93430 Villetaneuse, France
zevio@lipn.univ-paris13.fr
Abstract. With the generalisation of the digitalization of scientific pub-
lications, scholarly data is now tackled with a big data perspective. In this
context, interest about applications like expert finding, research recom-
mandation systems or collaborators discovery grows and challenges re-
lated to knowledge discovery on large-scale scholarly data arise. The Uni-
fied Knowledge Platform (Plateforme de Connaissances Unifiées) project
aims at developing an open-source platform valuing scholarly as well as
business data. In that respect, we propose an approach and a method-
ology for discovering knowledge and enrich it from workings documents,
more precisely scientific publications, in the particular use case of ex-
pert finding. Compared to the state of the art, the originality of our
approach lies in the combination of text mining as well as graph mining
methods, more specifically graph abstraction. We present an experiment
with already published results on the 9-years acts of a French workshop
on semantic information retrieval. In this experiment, we managed to
obtain a graph mapping researchers who participated in the workshop.
In this graph, researchers are linked together by co-publication relation-
ships and described by their topics of publication. We were also able to
detect dense communities of researchers with the help of graph abstrac-
tion. Based on these results and in the light of the state of the art, we
discuss further research tracks.
Keywords: Scholarly data · Graph mining · Knowledge discovery · Ex-
pert finding.
Early Stage PhD - EKAW 2018 Doctoral Consortium
1 Introduction
An expertise is ”an individual’s skill, knowledge, aptitude or behaviour” [6]. The
task of expert finding consists in assessing individuals’ expertises (i.e. construct-
ing their expert profile [5]). This task has various applications in industries such
as finding employable and appropriate candidates or assigning an expert to a
task or a project for example. In academia, expert finding is also useful for as-
signing a researcher to a program committee or a project expertise, or setting
2 S. Zevio
up research projects, to name a few. According to the claim that an author of a
text is an expert of its content, text appears like a solid source of knowledge for
expert finding. More precisely, we focus on working documents (e.g CV, project
reports, etc.). Such documents contain crucial information about an individual’s
expertises. In academia, they mainly consist in scholarly data.
The PCU1 (Plateforme de Connaissances Unifiée, i.e Unified Knowledge
Platform) project’s aim is to propose an open source industrial platform valuing
business (and scholarly, to an extent) data. With the recent explosion of digitiza-
tion of academic and technical documents, scholarly data has known such a rapid
growth [18] that we now talk about big scholarly data. In that respect, interest
in big scholarly data platforms has emerged [17, 12]. In this context, our aim is
to discover knowledge from text (i.e scientific publications) for expert finding,
represent it automatically into graphs and enrich knowledge with the hypothesis
that new knowledge will emerge from the graph structure. Our research question
is the following: how precise and accurate knowledge can become thanks to the
knowledge enrichment process, and what methods are providing the best results
on working documents, or more specifically, scientific publications?
To answer this question, we will enrich PCU with a semantic platform with
the aim of supporting experiments that will test our hypothesis and enable us to
answer this research question. This research question lies in the area of knowledge
extraction and at the interface between knowledge discovery, natural language
processing and artificial intelligence, more precisely machine learning (unsuper-
vised methods) and graph mining.
We will present a state of the art in section 2, define our approach in section
3, define our methodology in section 4, present results that we have reached so
far in section 5 reflect on the relevance of our approach and discuss further work
and research tracks in section 6.
2 State of the art
According to the literature, expert finding is closely related to the problem of
expert profiling [3, 11], which implies identification of expertises and their assig-
nation to appropriate individuals owning them [5]. Initially, expert finding sys-
tems were based on people assessing their own expertises by selecting predefined
keywords [2], and the use of manually generated heuristics was predominant [19].
For the sake of the automation of expert finding, textual sources of knowledge
were harvested [2]. With the explosion of online data stored in digital libraries,
scholarly data [18, 10] became a solid source of knowledge for expert finding. In
the rest of this paper, we will consider scholarly data, more precisely scientific
publications, as a relevant textual source of expert finding concerning scholars.
From scientific publications, classical methods of expert finding described in
the literature are based on information extraction methods [10] such as meta-
data (title, authors, abstract, date of publication, etc.) as well as citations and
1
Plateforme de Connaissances Unifiée : https://www.smile.eu/fr/publications/smile-
lab/pcu-plateforme-connaissances-unifiees
Knowledge discovery and enrichment from scholarly data for expert finding 3
author information (co-authorship, authors’ affiliations) extraction. Open-source
systems for metadata, citations and author information extraction exist [16] but
still need improvement according to their error analysis. To identify underlying
expertises within scientific publications, concept extraction methods are used [4]
by means of classical keyphrase extraction algorithms for example. Such algo-
rithms can be domain-dependant, thus rely on a model trained on an annotated
corpus [9] or take advantage of knowledge of the domain by investigating rela-
tions between expertise topics extracted [5, 8]. Concerning the computer science
domain, an ontology has recently been released [13].
Some scholarly data platforms have already been developed, such as Rex-
plore [12]. Representation of knowledge extracted from text through a graph
is quite common. Rexplore takes advantage of this representation by provid-
ing a semantic network of fine-grained research areas, linked by semantic rela-
tions. As described in the literature, researchers have mostly used graph and
machine-learning techniques for expert finding [1]. As suggested [1], issues re-
lated to the identication and ranking of experts [2] can be avoided by ”combining
content-based expertise indicators and social relationships”. Combining machine
learning algorithms with graph mining methods for expert finding in order to
discover knowledge and enrich it is a challenging research question. Inspired from
the analysis of social networks, graph mining techniques have been applied to
the detection of frequent k-communities [14]. With the claim that researchers
and expertises are represented through an attributed graph, detecting strongly
connected k-communities would be interesting to investigate for expert finding.
Moreover, the application of the recent hub-authority core theory [15] is also a
promising investigation trail for directed citation graphs. As far as we know, no
scholarly data platform takes advantage of the recent advances of graph mining
techniques, even if graph representation is common.
3 Proposed approach
In the light of the state of the art, we propose an original approach for expert
finding consisting in combining text mining, more precisely machine learning
algorithms applied on text (i.e scientific publications), with graph mining meth-
ods. The graph mining methods are applied on the graph representing knowl-
edge extracted from the text. The machine learning algorithms considered are
keyphrases [9] and semantic relationships [8] extraction algorithms. From the
text, the keyphrase extraction algorithm is initially applied, in order to extract
fine-grained topics or thematics of publication within scholarly data, more pre-
cisely on full-text scientific publications. The algorithm considered is based on
a model trained on an annotated corpus thus it is language-dependent and ap-
plied to the domain of computer science. It is based on a conditional random
fields model trained with keyphrase candidated filtered with part-of-speech tag
sequences.
Then, the semantic relationships extraction algorithm is applied on the out-
put of the keyphrase extraction algorithm. It is based on semantic similarity
4 S. Zevio
between extracted keyphrases belonging to the same sentence. The semantic
similarity is measured thanks to most frequent patterns and clustering methods.
The algorithm is unsupervised, which enables the automatic extraction of al-
ready known as well as brand new relationships between extracted keyphrases,
such as ”is-a” between ”information retrieval” and ”task”. It has been tested on
the ACL corpus [7]. The sequential application of these two algorithms enables
us to collect the knowledge required for building a graph representing knowledge
extracted from the text.
The originality of our approach described in Figure 1 lies in the combination
of these algorithms with graph abstraction [14]. From the representation of a
text as a graph, the idea is to focus on strongly connected vertices applying a
topological constraint, for example by searching for the k-core (i.e the largest
subgraph verifying a topological constraint such as ”each vertex in the subgraph
has a k degree”). Several representations are possible with attributed graphs,
one of them being that vertices represent researchers and are labeled by topics
of publication. In Figure 1, experts on topic 1 are obtained by removing R5 (who
does not have 2-degree), then R4 for the same reason after R5’s removal. Our
expectation considering strongly connected vertices is that it may bring out new
and interesting knowledge from the graph that is itself built from the represen-
tation of text. In Figure 1, all experts on topic 1 obtained by 2-core abstraction
on the graph are also experts on topic t2, which is a new knowledge. This hy-
pothesis has been raised from the state of the art, more precisely from network
analysis for social sciences [15] and our claim is that interesting knowledge could
emerge for expert finding from the generalisation of such graph mining methods.
Fig. 1. Our approach combining data and graph mining for expert finding from scien-
tific publications
Knowledge discovery and enrichment from scholarly data for expert finding 5
4 Methodology
To support our experiments and meet PCU project’s purpose, we have developed
an open source semantic platform making the machine learning algorithms [9, 8]
that we selected available as a workflow. We also developed a Mathematica work-
flow as a complementary tool for our experiments, mostly for data manipulation
and displaying as well as for the automation of the connection between the ma-
chine learning algorithms outputs and the input of MinerLC (i.e the software for
applying graph abstractions). We selected the following datasets : ACL corpus
written in English and the 9-years scientific acts of the Recherche d’Information
SEmantique (RISE) workshops2 , mostly written in French. We plan on building
a larger dataset, by collecting the scientific publications of the members of our
lab. Our experiments consist in applying the machine learning algorithms on the
full-text of the scientific publications. We also collect structured metadata such
as titles, authors or keywords thanks to CERMINE [16].
From the knowledge discovered, we build a graph G = (V,E) (V being the
vertices, i.e the objects considered and described by items, E the edges, i.e the
relations between the vertices) for each dataset. Our first experiments consisted
in describing researchers (i.e vertices, objects) by topics of publication (i.e items)
and linking them together by relationships of co-publications (i.e edges). On this
graph, 2-core abstraction is applied in order to identify communities of strongly
connected researchers who published on a common set of topic of publication
with at least two other researchers that also belong to the community. Fur-
ther expriments will emerge, consisting in identifying the best way to represent
knowledge from scientific publications, being describing researchers or publica-
tions with topics of publication, dates of publication, locations of laboratories or
keywords, for example. The nature of the semantic relationships between objects
described could also be different, like co-citation relationships for example. Also,
more interesting topological constraints should be applied on the graph during
graph abstraction, such as ”each vertex in the subgraph belongs to a star” for
example.
To evaluate our work, no gold standard exists as far as we know. We should
evaluate the costs of building a gold standard with manually annotated corpus for
supporting our experiments as well as the possibility of conducting an evaluation
campaign in the domain of scholarly data. As we aim at enriching knowledge
extracted with the use of graph abstractions, an error analysis as well as a
comparison of knowledge extracted with and without (baseline) the application
of graph abstraction should be proposed. Indeed, we should be able to detect
communities straight from the graph, but such communities should be narrower
after the application of graph abstraction. As a matter of fact, our hypothesis
consisting in new knowledge emerging from graph abstraction would be verified,
as we would be able to detect core scientific publications or core researchers of
a domain with more precision.
2
RISE : https://sites.google.com/site/frenchsemanticir/documents
6 S. Zevio
5 Results
We presented the preliminary results obtained on our experiment on the 9-years
scientific acts of Recherche d’Information SEmantique (RISE) workshops (from
2009 to 2017) during the 10th edition in 2018 [20]. During this experiment, we
managed to obtain an attributed graph mapping the researchers of the work-
shops, described by their topics of publication and linked by coauthor relation-
ships. As scientific publications in RISE are mainly written in French and the
keyphrase extraction algorithm is language-dependant and trained for English,
the topics of publications were simply extracted from the keywords given by the
authors. We obtained a graph by applying text mining methods on the scientific
acts of the workshops. We were able to detect dense communities of researchers
based on graph abstraction applied on this graph.
{Ontologie}
Sylvie
Salotti
Nada
Adeline Mimouni
Nazarenko
Ines
Bannour
Kian-Lam
Mohannad
Tan
Almasri
Catherine Synda Haïfa
Philippe
Alexandre Berrut Ouardani Zargayouna
EmmanuelHervé Mulhem
Saidi
Blanchon Karam
Dellandréa
Ningning
Sandra Valérie
Abdulahhad Vanna
Liu SkaffBellynck Didier Jean-Pierre
David Gabriela
Achille Jibril ChhuoStephan
Schwab Chevallet
Rouquet
Christian
FalaiseCsurka Frej Bernard
Luca
Liming
Boitet Eric Catherine Vincent
Marchesotti Jean-Pierre
Chen Gaussier Roussey Soulignac
Loïc Chanet
Farah
Maisonnasse
Harrathi
Mohamed
Mohsen
Gammoudi
Sylvie
Calabretto
Arnaud
Renard
Beatrice
Jean Samuel Rumpler
Beney Gesche
Rami
Guy Elöd
Harrathi
Caplat
Egyed-Zsigmond
Marie-Noëlle Omar Aurélien
Kunalè Mina Guilaine Jean-Claude Mahdi Jean-Pierre Marc Sebastien Bernard Elisabeth Nadira Youssouf
Bessagnet Annig El Saint
Ehab Kudagba Ziani Talens Moissinac Gueffaz Descles Bertin Fournier Espinasse Metais Lamari Saidlai
Le Tanguy Beqqali Requier
Hassan
Davide Parc-Lacayrelle Moal Cyril Samaneh
Buscaldi Albert Christian Dumoulin Chagheri
Royer Sallaberry Mohamed
Aldo Armel Hassan Danielle Jirasri Iana Shereen Nebrasse Yves Sebastien
Ben
Gangemi Fotsoh Badir Boulanger Deslis Atanassova Albitar Ellouze Lecourtier Adam
Ahmed
Tawofaing
Lynda
Zheng Bart Nicolas Marc Khadija Kata Brigitte Clement Mathieu
Tamine
Shabai Lamiroy Delaforge Dutoo Elbedweihy Gabor Grau Jonquet Lafourcade
Lechani
Fig. 2. Community of researchers who participated in RISE workshops (2009-2017). In
red : community of researchers obtained by the application of a graph abstraction with
topological constraint of 2-degree on the topic of ”ontologie” (i.e ontology, in French)
For example, we queried researchers who published on the subject of ”ontol-
ogy” (i.e ”ontologie” in French). We obtained a community of researchers based
on authors who published on this particular topic. With the application of 2-core
graph abstraction on this community, we managed to remove researchers from
the community because of their lack of co-publication relationships with the oth-
ers within the workshops. Thus, we managed to identify a narrower community
of researchers strongly connected to each other, according to a degree 2, whose
members would be the core experts of the domain. The results are showed in
Figure 2.
Knowledge discovery and enrichment from scholarly data for expert finding 7
6 Discussion
As our preliminary results seem to imply, our approach looks quite promising.
We already managed to obtain finer-grained communities of researchers accord-
ing to a given topic of publication, which seems to validate the relevance of our
approach. We run into difficulties related to considerations such as the size of
the graph obtained and the lacking of the quality of semantic relationships de-
scribing the objects (i.e vertices) of the graph. Indeed, the graph we obtained is
quite small (less than 50 vertices) and lacks items describing the objects and re-
lationships between objects. Such a graph is interesting for experiments and ease
of manipulation for showing examples, but we should consider large-scale data
or at least larger corpus. Also, resources for French-language are quite limited.
Recommandations for future work would be supporting the semantic inter-
operability of our graphs and opening to the semantic web by integrating the
Computer Science Ontology to PCU. We should enrich our vertices’ descrip-
tions with the concepts of the ontology recognized in the full-text or abstract
of the scientific publications thanks to semantic annotation, with the idea of a
bottom-up enrichment by generalization. For supporting multilingual processing,
a translation of the Computer Science Ontology in French as well as a training
of the keyphrase extraction algorithm in French would be useful, including in
the aim of meeting PCU’s purpose, but costs of translation should be evaluated.
We should also consider conducting further experiments such as described in
the section 4, among other things describing objects with more than topics of
publication (dates of publication or locations of laboratories for example) and
finding the best parameters for graph abstraction for expert finding.
Acknowledgements
This thesis is supervised by Professor Thierry Charnois, Dr. Guillaume Santini
and Dr. Haı̈fa Zargayouna. It is financed by the FUI project PCU (Plateforme
de Connaissances Unifiées).
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