=Paper=
{{Paper
|id=Vol-1699/paper-05
|storemode=property
|title=Freedom for bibliographic references: OpenCitations arise
|pdfUrl=https://ceur-ws.org/Vol-1699/paper-05.pdf
|volume=Vol-1699
|authors=Silvio Peroni,David Shotton,Fabio Vitali
|dblpUrl=https://dblp.org/rec/conf/semweb/PeroniSV16a
}}
==Freedom for bibliographic references: OpenCitations arise==
https://ceur-ws.org/Vol-1699/paper-05.pdf
Freedom for bibliographic references:
OpenCitations arise
Silvio Peroniȯ , David Shottonɞ , and Fabio Vitaliȯ
ȯ
DASPLab, DISI, University of Bologna, Bologna, Italy
ɞ
Oxford e-Research Centre, University of Oxford, Oxford, UK
silvio.peroni@unibo.it, david.shotton@oerc.ox.ac.uk,
fabio.vitali@unibo.it
Abstract. Scholarly citations from one publication to another,
expressed as reference lists within academic articles, are core ele-
ments of scholarly communication. Unfortunately, they usually can
be accessed en masse only by paying significant subscription fees
to commercial organizations, while those few services that do made
them available for free impose strict limitations on their reuse. In
this paper we provide an overview of the OpenCitations Project
(http://opencitations.net) undertaken to remedy this situation,
and of its main product, the OpenCitations Corpus, which is an
open repository of accurate bibliographic citation data harvested
from the scholarly literature, made available in RDF under a Cre-
ative Commons public domain dedication.
RASH version: https://w3id.org/oc/paper/occ-lisc2016.html
Keywords: Citation Database, OpenCitations, OpenCitations Cor-
pus, Scholarly Communication, Semantic Publishing
1 Introduction
Databases of citation data are among the most attractive and used artefacts
in the Scholarly Communication domain. They are one of the main tools
used by researchers for gaining knowledge about a particular topic, and by
scientists in Bibliometrics, Informetrics, and Scientometrics for analysing
the complex relationships that exist within huge networks of citations of
scholarly works. They also serve institutional goals, since they provide one
of the main mechanisms for assessing the quality of research by means
of (sometimes questionable) metrics and indicators calculated from such
citation databases. While some of these resources, e.g. Microsoft Academic
Graph3 and Google Scholar4 , are freely accessible (but not downloadable),
those considered the most authoritative by institutions worldwide, namely
Scopus5 and Web of Science6 , can be accessed only by paying significant
access fees, which may amount to tens of thousands of pounds annually [8].
3
https://www.microsoft.com/en-us/research/project/microsoft-
academic-graph/
4
https://scholar.google.com/
5
https://www.scopus.com/
6
http://webofscience.com/
2 Silvio Peroni et al.
Reference lists within academic articles are core elements of scholarly
communication, since they both permit the attribution of credit and in-
tegrate our independent research endeavours. But the cruel reality is that
these key data are not freely available. In the current age where Open
Access is considered a necessary practice in research, it is a scandal that
reference lists from scholarly publications (conference papers, books, jour-
nal articles, etc.) are not readily and freely available for use by all scholars.
As we have already stated in a previous work [8]:
Citation data now needs to be recognized as a part of the Commons
– those works that are freely and legally available for sharing –
and placed in an open repository, where they should be stored in
appropriate machine-readable formats so as to be easily reused by
machines to assist people in producing novel services.
This is the main premise behind the OpenCitations Project [8] [14],
which has created an open repository of scholarly citation data – the
OpenCitations Corpus (OCC) – made available under a Creative Commons
public domain dedication7 to provide in RDF accurate citation information
(bibliographic references) harvested from the scholarly literature. Since the
beginning of July 2016, the OCC has been ingesting and processing the
reference lists of scholarly papers available in Europe PubMed Central8 . In
this paper we provide a brief overview of the OCC’s main components that
make possible the extraction, a description of such reference lists in RDF,
and a progress report concerning the available citation data.
The rest of the paper is organised as follows. In Section 2 we recall the
story of the OpenCitations Project since its beginning in 2010. In Section 3
we describe the revised metadata specification and software tools that have
been recently developed within the OpenCitations Project for the creation
of a new and improved instantiation of the OCC. In Section 4 we briefly
describe other open (and RDF-based) repositories of scholarly document
metadata. Finally, in Section 5, we sketch out our future plans.
2 The story so far
The OpenCitations Project formally started in 2010 as a one-year project
funded by JISC9 (subsequently extended for an additional half year), with
David Shotton as director, who at that time was working in the Depart-
ment of Zoology at the University of Oxford. The project’s goal was global
in scope, and was designed to change the face of scientific publishing and
scholarly communication, since it aimed to publish bibliographic citation
information in RDF and to make citation links as easy to traverse as Web
7
https://creativecommons.org/publicdomain/zero/1.0/legalcode
8
http://europepmc.org/
9
http://www.jisc.ac.uk/whatwedo/programmes/inf11/jiscexpo/
jiscopencitation.aspx
Freedom for bibliographic references 3
links. The main deliverable of the project, among several outcomes10 , was
the release of an open repository of scholarly citation data described using
the SPAR (Semantic Publishing and Referencing) Ontologies11 [7], namely
the OpenCitations Corpus, initially populated with the citations from jour-
nal articles within the Open Access Subset of PubMed Central12 [14].
In May 2014, OpenCitations was adopted by the Infrastructure Services
for Open Access (IS4OA)13 as one of its academic Open Access services.
IS4OA is UK-based not-for-profit charitable company that aims to provide
benefit to the global community of research information users. It acts as
an umbrella organisation that supports openly accessible information and
discovery services relating to academic information, research results and
scholarly publications, by providing business structure and expertise and a
means of channelling financial support to these services.
At the end of 2015, Silvio Peroni joined the OpenCitations Project as
co-director, with the aim of setting up a new instantiation of the Corpus
based on a new metadata schema and employing several new technologies
to automate the ingestion of fresh citation metadata from authoritative
sources. The current instantiation of the OCC is hosted by the Department
of Computer Science and Engineering (DISI) at the University of Bologna,
and since the beginning of July 2016 it has been ingesting, processing and
publishing reference lists of scholarly papers available in Europe PubMed
Central, as described in the following section.
3 The new instantiation of the OpenCitations Corpus
The OpenCitations Project (http://opencitations.net) has recently cre-
ated a new instantiation of its open citations database, with an integrated
SPARQL endpoint and a browsing interface to support data consumers.
This database, the OpenCitations Corpus (OCC), is an open repository of
scholarly citation data made available under a Creative Commons public
domain dedication (CC0), which provides accurate bibliographic references
harvested from the scholarly literature, described using the SPAR Ontolo-
gies [7] according to the OCC metadata document [12], that others may
freely build upon, enhance and reuse for any purpose, without restriction
under copyright or database law.
3.1 The model
The newly revised metadata model used for the data stored in the OCC,
available at [12] and briefly summarised in Fig. 1, is explicitly aligned with
the SPAR Ontologies [7] and other standard vocabularies. In particular:
10
https://opencitations.wordpress.com/2011/07/01/jisc-open-
citations-project-–-final-project-blog-post/
11
http://www.sparontologies.net/
12
http://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/
13
https://is4oa.org/services/open-citations-corpus/
4 Silvio Peroni et al.
– the FRBR-aligned Bibliographic Ontology (FaBiO)14 [9] is used to pro-
vide a description of all the metadata of citing/cited bibliographic re-
sources (conference papers, book chapters, journal articles, etc.) and
their related container resources (academic proceedings, books, jour-
nals, etc.), and metadata about the particular formats in which they
have been embodied (digital vs. print, first and ending pages, etc.);
– the Publishing Roles Ontology (PRO)15 [13] is used to describe the roles
of bibliographic agents (author, editor, publisher, etc.) related to the
bibliographic resources, while the order among such roles, e.g. the list
of authors of a paper, is handled by extending PRO with an additional
property, i.e. oco:hasNext;
– the Bibliographic Reference Ontology (BiRO)16 and the Citation Count-
ing and Context Characterization Ontology (C4O)17 [4] are used to
describe the textual content of each reference in the reference list of a
citing bibliographic resource;
– finally, the DataCite Ontology18 is used to define all the identifiers
(e.g. DOI, PubMed ID, PubMed Central ID, ORCID, ISSN, etc.) for
bibliographic resources and the agents involved, while the Friend Of
A Friend (FOAF)19 ontology is used to define additional data about
agents, such as their given and family names.
For convenience, all the terms from the aforementioned ontologies are
collected within a new ontology called the OpenCitations Ontology (OCO)20 .
This is not yet another bibliographic ontology, but rather just a mechanism
for grouping existing complementary ontological entities from several other
ontologies for the purpose of providing descriptive metadata for the OCC.
3.2 The data
The OCC stores metadata relevant to bibliographic citations in RDF, en-
coded as JSON-LD21 . In early September 2016, all the ingested data will
be also available as downloadable datasets. In the meantime, two exem-
plar dataset, compliant with the OCC metadata model introduced in Sec-
tion 3.1, have been made available: the first from article metadata provided
by Springer Nature (available at [11]), and the second gathered from Eu-
rope PubMed Central (available at [10])22 .
The following six bibliographic entity types occur in the OCC, as well
as in the aforementioned exemplar datasets:
14
http://purl.org/spar/fabio
15
http://purl.org/spar/pro
16
http://purl.org/spar/biro
17
http://purl.org/spar/c4o
18
http://purl.org/spar/datacite
19
http://xmlns.com/foaf/spec/
20
https://w3id.org/oc/ontology
21
http://json-ld.org/
22
All the resources in the exemplar datasets have URLs that starts with “http:
//localhost:8000/corpus/” and do not refer to any existing IRI included in
the current version of the corpus.
Freedom for bibliographic references 5
Fig. 1. The Graffoo diagram [5] of the main ontological entities described by the
OCC metadata model.
– bibliographic resources (br), class fabio:Expression – resources
that either cite or are cited by other bibliographic resources (e.g. journal
articles), or that contain such citing/cited resources (e.g. journals);
– resource embodiments (re), class fabio:Manifestation – details
of the physical or digital forms in which the bibliographic resources are
made available by their publishers;
– bibliographic entries (be), class biro:BibliographicReference –
the literal textual bibliographic entries occurring in the reference lists
within the bibliographic resources, that reference other bibliographic
resources;
– responsible agents (ra), class foaf:Agent – names of agents having
certain roles with respect to the bibliographic resources (i.e. names of
authors, editors, publishers, etc.);
– agent roles (ar), class pro:RoleInTime – roles held by agents with
respect to the bibliographic resources (e.g. author, editor, publisher);
– identifiers (id) (class datacite:Identifier) – external identifiers
(e.g. DOI, ORCID, PubMedID) associated with the bibliographic enti-
ties.
The corpus URL (https://w3id.org/oc/corpus/) identifies the entire
OCC, which is composed of several sub-datasets, one for each of the six
aforementioned bibliographic entities included in the corpus. Each of these
has a URL composed by suffixing the corpus URL with the two-letter short
6 Silvio Peroni et al.
name for the class of entity (e.g. “be” for a bibliographic entry) followed by
an oblique slash (e.g. https://w3id.org/oc/corpus/be/). Each dataset
is described appropriately by means of the Data Catalog Vocabulary23 and
the VoID Vocabulary24 , and a SPARQL endpoint25 is made available for
all the entities included in the entire OCC.
Upon initial curation into the OCC, a URL is assigned to each en-
tity within each sub-dataset, which can be accessed in different formats
(HTML, RDF/XML, Turtle, and JSON-LD) via content negotiation. Each
entity URL is composed by suffixing the sub-dataset URL with a num-
ber assigned to each resource, unique among resources of the same type,
which increments for each new entry added to that resource class. For in-
stance, the resource https://w3id.org/oc/corpus/be/537 is the 537th
bibliographic entry recorded within the OCC. The final part of such URL,
i.e. the two-letter short name for the class of items plus “/” plus the number
(“be/537” in the example), is called the internal corpus identifier, since it
allows the unique identification of any entity within the OCC.
Each of these entities has associated metadata describing its provenance
using the PROV-O26 ontology and its PROV-DC extension27 (e.g. https:
//w3id.org/oc/corpus/be/537/prov/se/1). In particular, we keep track
of the curatorial activities related to each OCC entity, the curatorial agents
involved, and their roles.
All these RDF data are stored in BibJSON28 encoded as JSON-LD,
defined through an appropriate JSON-LD context29 which hides the com-
plexity of the model (shown in Fig. 1) behind natural language keywords.
For instance, the following excerpt is the JSON-LD linearisation of the
aforementioned “be/537” entity:
{
"iri": "gbe :537" ,
"a": "entry",
"label ": "bibliographic entry 537 [be/537]" ,
"content ": "Svahn , HA , Berg , A. Single cells or large populations , Lab Chip
, 2007, 7, 544, 546, DOI: 10.1039/ b704632b , PMID: 17476370" ,
"crossref ": "gbr :1601"
}
In this excerpt, “iri” defines the URL of the resource in consideration
(where “gbe:” is a prefix for “https://w3id.org/oc/corpus/be/”), while “a”,
“entry”, “label”, “content” and “crossref” stand for rdf:type, biro:Biblio
graphicReference, rdfs:label, c4o:hasContent and biro:references
respectively (where “gbr:” is a prefix for “https://w3id.org/oc/corpus/br/”).
Additional information about OCC’s handling of citation data, and the
way they are represented in RDF, are detailed in the official OCC Metadata
Document [12].
23
https://www.w3.org/TR/vocab-dcat/
24
https://www.w3.org/TR/void/
25
http://w3id.org/oc/sparql
26
https://www.w3.org/TR/prov-o/
27
https://www.w3.org/TR/prov-dc/
28
http://okfnlabs.org/bibjson/
29
https://w3id.org/oc/corpus/context.json
Freedom for bibliographic references 7
3.3 The ingestion workflow
The ingestion of citation data into the OCC is handled by two Python
scripts called Bibliographic Entries Extractor (BEE) and the SPAR Citation
Indexer (SPACIN), available in the OCC’s GitHub software repository30 .
BEE – The Bibliographic Entries Extractor As shown Fig. 2, BEE is
responsible for the creation of JSON files containing information about the
articles in the OA subset of PubMed Central (retrieved by using the Europe
PubMed Central API31 ). Each of these JSON files is created by asking
Europe PubMed Central about all the metadata of the articles it stores
that have available the source XML file. Once identified, BEE processes all
the XML sources so as to extract the complete reference list of each paper
under consideration, and includes all the data in the final JSON file. An
excerpt of one of those JSON files is as follows:
{
"doi": "10.1007/ s10544 -016 -0081 -z",
"pmid ": "27299468" ,
"pmcid ": "PMC4908161",
"localid ": "MED -27299468" ,
"curator ": "BEE EuropeanPubMedCentralProcessor ",
"source ": "http :// www.ebi.ac.uk/europepmc/webservices/rest/PMC4908161/
fullTextXML",
"source_provider ": "Europe PubMed Central",
"references ": [
...
{
"bibentry ": "Svahn , HA , Berg , A. Single cells or large populations , Lab
Chip , 2007, 7, 544, 546, DOI: 10.1039/ b704632b , PMID: 17476370" ,
"pmid ": "17476370" ,
"doi": "10.1039/ b704632b",
"process_entry ": "True"
},
...
]
}
In particular, for each articles retrieved by means of the Europe PubMed
Central API, BEE stores all the available bibliographic identifiers (in the
example, “doi”, “pmid”, “pmcid”, and “localid”) and all the textual refer-
ences, enriched by their own related bibliographic identifiers if those are
available. In addition, the JSON file also includes provenance information
about the source, its provider and the OCC curator (i.e. the particular
BEE Python class responsible for the extraction of these metadata from
the source). The created JSON files are then processed, independently, by
the tool presented in the next section.
We have undertaken some tests to determine the performances of BEE
in generating these JSON files. In particular, we queried Europe PubMed
Central for the metadata of articles while running BEE for 30 minutes on
a MacBook Pro, with 2 GHz Intel Core i7 processor, 8 GB DDR3 1600
MHz, OS X 10.11.3. During that time, we were able to create 185 JSON
files containing all the aforementioned metadata, giving a rate of about 6
new JSON files per minute.
30
https://github.com/essepuntato/opencitations
31
https://europepmc.org/RestfulWebService
8 Silvio Peroni et al.
SPACIN – The SPAR Citation Indexer SPACIN processes each
JSON file created by BEE, retrieving additional metadata information
about all the citing/cited articles described in it by querying the Cross-
ref API32 and the ORCID API33 . These API are also used to disambiguate
bibliographic resources and agents by means of the identifiers retrieved
(e.g., DOI, ISSN, ISBN, ORCID, URL, and Crossref member URL). Once
SPACIN has retrieved all these metadata, appropriate RDF resources are
created (or reused, if they have been already added in the past) and stored
in the file system in JSON-LD format (as shown in Section 3.2) and, addi-
tionally, within the OCC triplestore. It is worth noting that, for space and
performance reasons, the triplestore includes all the data about the curated
entities, but does not store their provenance data nor the descriptions of
the datasets themselves – these are accessible only via HTTP, not via the
SPARQL endpoint.
The SPACIN workflow, described in Fig. 2, is a process that runs until
no more JSON files are available from BEE. Thus, the current instance
of the OCC is evolving dynamically in time, and can be easily extended
beyond Europe PubMed Central by reconfiguring it to interact with ad-
ditional REST APIs from different sources, so as to gather new article
metadata and their related references.
Each new resource recorded within the OCC by SPACIN occupies be-
tween 0.3 and 4 KB, plus an additional 32 KB dedicated to storage of its
provenance data. Each day, the workflow adds about 2 million triples to
the corpus, describing more than 20,000 new citing/cited bibliographic re-
sources and about 100,000 new authors, 5% of whom are disambiguated
through their ORCID ids.
Fig. 2. The steps involving BEE and SPACIN, and their related Python classes,
in the production of the OpenCitations Corpus.
32
http://api.crossref.org/
33
http://members.orcid.org/api/
Freedom for bibliographic references 9
We have tested the performances of SPACIN in processing the JSON
files generated by BEE and produce new RDF resources for the OCC. In
particular, we run SPACIN on two subsets of JSON file: 67 JSON files
describing all 67 papers included in the Proceedings of ISWC 2015, and
the first 67 JSON files produced by BEE from the Open Access subset of
PubMed Central as the outcome of the experiment described in Section 3.3.
We use the same configuration as before, i.e. a MacBook Pro, with 2 GHz
Intel Core i7 processor, 8 GB DDR3 1600 MHz, OS X 10.11.3.
ISWC 2015 dataset. SPACIN took 45 minutes to process all 67 pa-
pers in the ISWC 2015 Proceedings, and the outcomes have been published
in [11]. Each citing paper contained about 23 references on average, and
SPACIN produced 1,441 new citing/cited resources, for a total of 1,531
citation links. These resources are contained in 411 different container re-
sources (e.g. journals, proceedings, books), published by 42 distinct pub-
lishers. The total number of authors is 3,076, 157 of whom (5.1%) have
been disambiguated through their ORCID. The total number of RDF state-
ments created is 69,995 (which, as explained, excludes provenance data and
datasets information), on average 1,044 triples per citing resource.
Europe PubMed Central dataset. SPACIN took 210 minutes to
process 67 papers from Europe PubMed Central, and the outcomes have
been published in [10]. Each citing paper contained about 50 references on
average, and SPACIN produced 3391 new citing/cited resources, for a to-
tal of 3,337 citation links. These resources are contained in 1,047 different
container resources (e.g. journals, proceedings, books), published by 137
distinct publishers. The total number of authors is 21,658, 957 of whom
(4.4%) have been disambiguated through their ORCID identifiers. The to-
tal number of RDF statements created is 377,237 (excluding, as before,
provenance data and datasets information), on average 5,630 triples per
citing resource. This number will reduce as the OCC becomes more fully
populated, since more cited resources will already be described within the
database.
In Table 1 we summarise some metrics related to the resources included
in the aforementioned exemplar datasets. While these data are far from
having a full coverage, they provide interesting snapshots of these two com-
munities. On the one hand, the community of ISWC 2015 is composed by a
relatively small number of people. Even if the average number of references
per paper is quite small (average 23, the paper with the most references
having 47 citation links), there are several papers that were cited more than
one time (the most cited one received 7 citations). Many of the citations are
to resources for which Crossref was not able to return any metadata. This
is understandable, since many citations in these Semantic Web papers are
to Web documents (e.g. W3C Recommendations) and to workshop papers
not indexed by Crossref (e.g. CEUR Workshop Series34 ). Some of these
non-Crossref-indexed publications are well known and well cited within
this community (the most cited one has 4 citations within these 67 ISWC
papers).
34
http://ceur-ws.org/
10 Silvio Peroni et al.
Table 1. Some aggregated data of the two exemplar datasets produced by
SPACIN.
Property ISWC 2015 Europe PubMed Central
Max. number of bibliographic
47 320
references within a paper
Max. number of citations
received by a paper within this 7 3
sample
Percentage of cited resources
for which Crossref did not 44% 13%
return any metadata
Max. number of citations
received by a cited resource for
4 1
which Crossref did not return
any metadata
On the other hand, we see a quite different citation behaviour in the
Europe PubMed Central papers. In this case, as expected, the number of
average references is higher (average 50, with one review paper having 320
citation links). The paper within this small sample that has been cited most
received only 3 citations from the other 66 papers, and this is clearly due
to the dimension of the citation graph of the Biomedical and Life Science
community to which PubMed Central relates, which is clearly bigger and
more sparsely linked than the ISWC one. Additionally, these papers usually
cite others published in journals to which proper identifiers (e.g. DOIs) have
been assigned, explaining the lower percentage of citations to resources that
are not indexed by Crossref.
4 Related works
In recent years we have seen a growing interest within the Semantic Web
community for in creating and making available RDF datasets concerning
bibliographic metadata of scholarly documents. While the list of such works
is quite extensive, inIn this section we describe four of the most important
contributions in the area.
The Semantic Lancet35 Project [2] aims at building a Linked Open
Dataset of scholarly publication metadata starting from the articles pub-
lished by Elsevier. In particular, the current dataset contains SPAR-based
[7] metadata about several papers published in the Journal of Web Seman-
tics36 , including citation links marked with the motivations justifying them
by means of CiTO properties. It has several graphical interfaces that allow
browsing and sense-making of these data.
Springer LOD37 [3] is an RDF dataset made available by Springer Na-
ture that publishes Springer metadata about conferences as Linked Open
35
http://semanticlancet.eu/
36
http://www.journals.elsevier.com/journal-of-web-semantics/
37
http://lod.springer.com/
Freedom for bibliographic references 11
Data (LOD). Its main focus in on proceedings volumes and the related con-
ferences, but it does not contain metadata describing the individual articles
contained in such proceedings.
OpenAIRE38 [1] is an Horizon 2020 open data project which pub-
lishes metadata of more than 14 millions of publications and thousands
of datasets. It makes available a mechanism for searching, discovering and
monitoring scientific outputs.
Finally, Scholarly Data39 [6] is a new project that refactors the Semantic
Web Dog Food40 so as to keep the dataset growing in good health, and that
adopts the new Conference Ontology41 (aligned with other existing models
including SPAR [7]) for describing the data.
5 Conclusions
In this paper we have introduced the OpenCitations Project, which has
created an open repository of accurate bibliographic references harvested
from the scholarly literature: the OpenCitations Corpus (OCC). The new
instance of the OCC has recently been established, and is already popu-
lated with data describing 595,222 citation links (as of August 24, 2016) –
a number that will grow quickly over the coming months as the continu-
ous workflow adds new data dynamically from Europe PubMed Central and
other authoritative sources. The OCC SPARQL endpoint is presently avail-
able for use, and distributions of the OCC datasets will shortly be made
openly available for bulk download – the first of these by early September
2016, with subsequent incremental additions.
We are currently working on two different aspects. First of all, we are
developing tools for linking the resources within the OCC with those in-
cluded in other datasets, e.g. Scholarly Data and Springer LOD. In addi-
tion, we are experimenting with the use of multiple parallel instantiations
of SPACIN, so as to increase the amount of new information that can be
processed daily into OCC.
Acknowledgements. All the scripts used in OpenCitations have been
developed as outcomes of several personal communications with people
responsible for the external services that OCC uses. We would like to thank
leading people in Europe PubMed Central (in particular Johanna McEntyre
and Vid Vartak), Crossref (in particular Ed Pentz, Geoffrey Bilder, and
Karl Ward) and ORCID (in particular Josh Brown and Laurel Hack) for
their help. We would also like to thank Alfred Hofmann and Aliaksandr
Birukou (Springer Nature) for allowing us to publish in Figshare the OCC
metadata concerning the Proceedings of ISWC 2015 [11], which they kindly
provided us in XML.
38
https://www.openaire.eu/
39
http://www.scholarlydata.org/
40
http://data.semanticweb.org/
41
https://w3id.org/scholarlydata/ontology/conference-ontology.owl
12 Silvio Peroni et al.
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