=Paper=
{{Paper
|id=Vol-1363/paper_2
|storemode=property
|title=COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data
|pdfUrl=https://ceur-ws.org/Vol-1363/paper_2.pdf
|volume=Vol-1363
|dblpUrl=https://dblp.org/rec/conf/esws/SofticVMEW15
}}
==COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data==
https://ceur-ws.org/Vol-1363/paper_2.pdf
COLINDA: Modeling, Representing and Using
Scientific Events in the Web of Data
Selver Softic1 , Laurens De Vocht2 , Martin Ebner1 ,
Erik Mannens2 , and Rik Van de Walle2
1
Graz University of Technology, Inffeldgasse 16c, 8010 Graz, Austria
{selver.softic,martin.ebner}@tugraz.at
2
Ghent University, iMinds - Multimedialab,
Sint-Pietersnieuwstraat 41, 9000 Ghent, Belgium
{laurens.devocht,erik.mannens,rik.vandewalle}@ugent.be
Abstract. Conference Linked Data (COLINDA)3 , a recent addition to the LOD
(Linked Open Data) Cloud4 , exposes information about scientific events (confer-
ences and workshops) for the period from 2002 up to 2015. Beside title, descrip-
tion and time COLINDA includes venue information of scientific events which is
interlinked with Linked Data sets of GeoNames5 , and DBPedia6 . Additionally in-
formation about events is enhanced with links to corresponding proceedings from
DBLP (L3S)7 and Semantic Web Dog Food 8 repositories. The main sources of
COLINDA are WikiCfP9 and Eventseer10 . The research questions addressed by
this work in particular are: how scientific events can be extracted and summa-
rized from the Web, how to model them in Semantic Web to be useful for mining
and adapting of research related social media content in particular micro blogs,
and finally how they can be interlinked with other scientific information from the
Linked Data Cloud to be used as base for explorative search for researchers .
Keywords: Linked Data, Scientific Events, Linked Science, Research 2.0
1 Introduction and Motivation
COLINDA11 contains information about scientific events worldwide (including loca-
tion and proceedings references), published as Linked Data. The data contained in
COLINDA is extracted and accumulated from the data dumps of WikiCfP , which are
published yearly and freely available on request for research12 purposes, and from data
3
http://colinda.org
4
http://lod-cloud.net/
5
http://www.geonames.org/
6
http://dbpedia.org
7
http://dblp.l3s.de/d2r/
8
http://data.semanticweb.org/
9
http://www.wikicfp.com/
10
http://eventseer.net/
11
Available at: http://colinda.org/, see also http://datahub.io/dataset/colinda
12
http://www.wikicfp.com/cfp/data.jsp
2 Selver Softic et al.
gathered via JSON interface from Eventseer. WikiCfP and Eventseer are two very pop-
ular online scientific event archives. WikiCfP contains calls for paper for about approx-
imately 30.000 conferences and has approximately 100.000 registered users. Eventseer
contains according the latest information13 calls for around 21000 events and serves
more then 1 million users. We also track the Twitter14 feeds of both sites integrating
on the fly arrival of upcoming scientific events using the Twitter API15 to recieve the
data from Twitter profiles of Wiki CfP and Eventseer. Currently COLINDA includes
data about more than 15000 conferences. Event instances are enriched with informa-
tion from Linked Data proceedings repositories DBLP (L3S)16 and Semantic Web Dog
Food17 as well by location information from Geonames and DBPedia. Primary intention
of COLINDA was to provide hashtag based identification system for scientific events
in Twitter in the manner of the "5-star" quality Open Data18 . Researchers are using
very often hashtags, while they are discussing on Twitter. Specially during scientific
events, they are using hashtags as abbreviated reference to the event they are attend-
ing [6]. E.g. ISWC (International Semantic Web Conference) 2012 is often referred as
"iswc12" or "iswc2012". DBLP (L3S) Linked Dataset and Semantic Web Dog Food
also use this kind of notation to reference the event of conference proceedings19 ,20 . The
overall idea of COLINDA is to serve as mining reference for creation of semantically
driven microblog data Mash Ups for Research 2.0 and as interlinking hub for other sci-
ence relevant sources from the LOD cloud in order to enhance explorative search for
researchers. Efforts made in this field using COLINDA will be introduced in detail in
section 3.
2 Extraction, Modeling, Creation and Publishing of Linked
Scientific Events
COLINDA data covers generally three domains: The first domain originates from Wi-
kiCfP and Eventseer and describes the Conference as basic scientific event with a start
date, location, description, label and link to the event web page. Second domain is the
Location of the event with geographic parameters resolved using the GeoNames and
DBPedia data set in interlinking process. Each location contains reference to the city,
country and coordinates of the location. Further as extension and third domain we have
Proceedings of the conference represented by the links from DBLP (L3S) or Semantic
Web Dog Food.
2.1 Linked Scientific Events Creation Process
The data creation process comprises the following steps:
13
http://eventseer.net/data/
14
http://www.twitter.com/
15
COLINDA
16
http://datahub.io/dataset/l3s-dblp
17
http://datahub.io/dataset/semantic-web-dog-food
18
http://5stardata.info/
19
e.g. for ’iswc2012’ at DBLP(L3S): http://dblp.l3s.de/d2r/page/publications/conf/ISWC/2012
20
e.g. for ’iswc2012’ at SW Doog Food: http://data.semanticweb.org/conference/iswc/2012/
COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data 3
– Extraction - extraction and pre-processing of data sources (Subsection 2.2)
– Modeling of Events using SWRC Ontology - concept coverage (Subsection 2.3)
– Triplification - creating RDF data triples (Subsection 2.4)
– Interlinking - connection to other Linked Data sets (Subsection 2.5)
Fig. 1. Creation process of linked scientific events.
2.2 Data Extraction
COLINDA is constructed from variously structured sources. Therefore we defined a
minimal set of properties that describe the Conference concept for a single RDF in-
stance. During extraction, all properties from sources are being mapped to defined nor-
malized set in order to harmonize the federated data. The Location and Proceedings
concepts related to conference events as such are considered as optional enrichment
which will be treated in the interlinking process. We made this decision having in mind
that all conference descriptions do not explicitly include the venue information. The
quality of source data depends on the users that provide the information. Thus such data
sources implicitly exclude assumption of completeness. Table 1 represents the minimal
set of properties a Conference and Location instance should include. The Extraction
process includes steps of either pre-processing of XML dumps from WikiCfP or JSON
from Tweets and Eventseer into the temporary tables of values formatted as Comma
4 Selver Softic et al.
Separated Value (CSV). During the pre-processing cycle data fields like e.g. date or
labels are being normalized to achieve uniform representation, and to provide easier
processable input for triplification step which converts the extracted values from tem-
porary tables into RDF formatted instances of Linked Data.
Table 1. Harmonized COLINDA - minimal properties set. Entries denoted with * are optional.
Concept Property
Conference label
title
description
date*
link*
location*
Proceedings proceedings*
Location placename
city
country
longitude
latitude
2.3 Modeling Scientific Events in the Web of Data
Basic representation of scientific events was well elaborated in previous research work
about the SWRC ontology introduced by Sure et al [7]. This practice has been already
approved and adapted by the implementation of Linked Data proceedings repositories
DBLP (L3S) and Semantic Web Dog Food. We also followed the good practice of
re-using existing vocabularies before we define our own. Minimal field set defined in
table 1 for RDF instance generation matches well the range of SWRC concepts. There-
fore, we have chosen the SWRC Ontology21 and basic RDFS Schema22 as established
vocabularies to describe Conference instances. The same approach was applied for
Location concept; needed set of geographical features to describe conference venues
is well covered by elements from GeoNames23 and Basic Geo (WGS84) Vocabulary24 .
Complete model with interlinked properties (proceeding and location) can be seen in
figure 2, where a single complete and interlinked instance of a conference (ISWC2012)
is depicted. Matchings between features and the vocabulary properties is shown in ta-
ble 2.
2.4 Triplification - Creation of RDF Instances of Scientific Events
The triplification25 process uses as input temporary data tables in CSV like format gen-
erated in extraction and pre-processing step. Input generated in this way represents tab-
21
http://ontoware.org/swrc/
22
http://www.w3.org/TR/rdf-schema/
23
http://www.geonames.org/ontology/
24
http://www.w3.org/2003/01/geo/wgs84_pos#
25
Under ’triplification’ we understand ’triple-wise’ creation of Linked Data instances as RDF
graphs
COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data 5
Table 2. COLINDA concept to ontology model mapping (note: geonames - GeoNames Ontology,
geo - W3C GEO Vocabulary, swrc - SWRC Ontology). Entries denoted with * are optional.
Concept/Property RDF Class/Property
Conference swrc:Conference
label rdfs:label
title swrc:eventTitle
description swrc:description
date* swrc:startDate
link* owl:sameAs
location reference* swrc:location
location reference* dcterms:spatial
Proceedings* rdfs:seeAlso
Location* geo:SpatialThing
placename* geonames:P
city* geonames:name
country* geonames:countryName
longitude* geo:long
latitude* geo:lat
Fig. 2. Sample interlinked Conference RDF instance of ISWC 2012 generated by Visual RDF.
ular set of values compatible with properties from table 1. This input is then parsed line
by line and conference instance is generated as single RDF graph using the vocabulary
properties defined in table 2. Each conference instance is accessible via REST (Repre-
sentational State Transfer) call as described in subsection 2.6. To make them accessible
by SPARQL endpoint, background batch process loads the conference instances into
the ARC226 RDF triple store running on the server.
2.5 Interlinking to Other Interesting Sources
In order to provide 5-star data and led by the design issues described in [1], we used
swrc:location as interlinking property in order to interlink the location data with GeoN-
ames. The interlinking process uses GeoNames query service to resolve geographical
26
https://github.com/semsol/arc2/
6 Selver Softic et al.
information and retrieve coordinates. Although usually owl:sameAs is used to interlink
to other data set we used this property to resolve the connection to the conference web
page and since swrc:location seems regarding the GeoNames to be more appropriate
choice. How this connection looks like can be seen in the sample depicted in figure 2
as well as online27 ,28 . Further we use dumps of DBPedia and Semantic Web Dog Food
to enhance the instances with DBPedia location info using the dcterms:spatial property
and for interlinking the proceedings from DBLP (L3S) ans Semantic Web Dog Food
we match the conference’s rdfs:label to the corresponding labels in those data sets via
SPARQL queries. In matching case a link is established with correlating results using
the rdfs:seeAlso property.
2.6 URI Design and Public Accessibility
Access to instances of COLINDA is possible via URIs with following pattern:
– http://colinda.org/resource/conference/{label}/{year}
All responses from COLINDA are formatted as RDF/XML fragment. Other supported
formats are: HTML, Text, N3, NTRIPLES format29 . Alternative access offers the SPARQL
30
endpoint. Current endpoint supports up to 250000 result triples per query and deliv-
ers results in different formats like: JSON, RDF/XML, XML, TSV etc. How to query
the endpoint is shown by simple example in listings 1.1. Results from the query re-
turn the COLINDA link, city, country and the geo-location of ISWC 2012 conference.
Recently, a dump of COLINDA was made available as Linked Data Fragments31 . COL-
Listing 1.1. Sample SPARQL query for retrieval of conference (geo) location.
PREFIX swrc:
PREFIX gn:
PREFIX geo:
PREFIX rdfs:
SELECT DISTINCT ?x ?city ?country ?long ?lat
{
?x rdfs:label "ISWC2012";
swrc:location ?loc.
OPTIONAL
{
?loc gn:name ?city;
gn:countryName ?country;
geo:lat ?lat;
geo:long ?long.
}
}
INDA RDF data dumps are also accessible via the CKAN Registry32 of LOD Cloud.
27
http://www.colinda.org/resource/conference/ISWC/2012?format=html
28
http://graves.cl/visualRDF/?url=www.colinda.org/resource/conference/ISWC/2012
29
e.g. http://www.colinda.org/resource/conference/ISWC/2012?format=html
30
http://colinda.org/endpoint.php
31
http://data.linkeddatafragments.org/colinda#dataset
32
http://datahub.io/dataset/colinda
COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data 7
2.7 Actuality of Data
COLINDA is kept up-to-date by a daily cron job which grabs the newest event an-
nouncements over the Twitter API for accounts of WikiCfP and Eventseer. The cron job
parses, creates, interlinks and synchs new events into the triple store. Each tweet also
includes information about the call page link which allows retrieval of the extended
information about events via web (WikiCfP) or available JSON (Eventseer) interface
during the update task. Additionally to the automated job, also manual updates are ran
as soon as the fresh dumps from both sites are available.
3 Applications and Use Cases
Both use cases introduced in following subsections address the challenges of Research
2.0. Research 2.0 as adaptation of the Web 2.0 for researchers defines researchers as
main consumers of the information. The purpose of these research activities is to of-
fer a set of tools and services which researcher can use to discover resources, such as
publications or events they might be interested in, as well as to collaborate with each
other via the web. These tools and services, according to the specifications of Research
2.0, are considered as Mash Ups, APIs, publishing feeds and specially designed inter-
faces based on social profiles [5, 8]. The role of COLINDA is addressed separately in
application description.
3.1 Affinity Browser
The "Researcher Affinity browser" was developed as a tool to demonstrate semantically
driven aggregation of microbolog data for Research 2.0. (use of Web 2.0 tools in sci-
entific research). In this context, COLINDA was used as mining source for the faceted
detection of similar scientist Twitter profiles based upon conferences they visited as
special affinity criteria. This is done by matching the COLINDA tags with the hashtags
of the Twitter user. Adequate demo video showing the "Researcher Affinity Browser" in
action can be also viewed online33 . The "Researcher Affinity Browser Application" [4]
is depicted in figure 3. At the beginning it retrieves a list of relevant users. Those results
represent a current snapshot which means that every time users produce new tweets
on Twitter, the analysis result evolves with it. The relevance is measured according to
the number of common conceptual affinities. Different affinity facets are displayed on
the left. Users can explore three types of affinities: conferences, tags and mentions.
Activation of a certain affinity filters the list of matching persons. There is the result
table that displays detailed information about each person and how many affinities are
shared. Further there is a map view and an affinity plot synchronized with the result
table. The purpose of the map is to get a better impression of where the affiliations of
the found persons would lie. The affinity plot visualizes in a quick overview affinity
correspondence between the analyzed profile and other profiles in the system.
8 Selver Softic et al.
Fig. 3. "Researcher Affinity Browser Application" snapshot.
Fig. 4. Mapping of keywords
3.2 ResXplorer
"ResXplorer"34 is an Research 2.0 [8] aggregated interface for search and exploration
of the underlying Linked Data Knowledge Base. A demo video explaining the interface
shown in figure 5 is available online35 . Data from Linked Data Knowledge Base orig-
inates from: DBLP (L3S)36 which is a bibliography of computer science conference
proceedings, COLINDA37 which is a main binding hub data set including informa-
33
http://www.youtube.com/watch?v=A25DrP3Mv8w
34
http://www.resxplorer.org
35
https://www.youtube.com/watch?v=tZU97BQxE-0
36
http://dblp.l3s.de/
37
http://colinda.org
COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data 9
tion about scientific events and links to venue and proceedings, common Linked Data
Knowledge Base DBPedia38 and Open Linked Data repository with geographical infor-
mation GeoNames39 . The role of COLINDA is to act as a hub which connects all data
sets in the knowledge base by pointing with links to other data sets. In this context it is
used both for keyword matching together with other data sets and for enabling the al-
gorithms in back-end to find better connections and paths between the terms visualized
in the interface as well as for their expansion. Within ResXplorer interface a real-time
keyword disambiguation guides researchers by expressing their needs. User selects the
correct meaning from a typeahead drop down menu. Query expansion of terms happens
in real-time. Figure 4 shows the typeahead expansion of "ResXplorer" in action. At
the same time background modules also fetch neighbor links which match the selected
suggestion. As result, selection of various resources is then presented to the researchers
within radial interface. In case they have no idea which object or topic to investigate
next, they get an overview of possible objects of interest (like points of interest on a
street map e.g. figure 5). As shown in figure 5 features like color, shape and size of the
items are used to enhance the guidance of the user during the search and exploration
process [2]. Different shapes and colors represent different entities like conferences,
persons, publications or proceedings. The explored items are marked black, and rela-
tions are marked red and clearly highlight the context and history of a search. Each
presented resource is somehow related with some of other resources. This is expressed
through lines and description of the relation which is a RDF property. The path distance
in hops over links is expressed through the orbital layers.
As additional feature in ResXplorer is that researchers, when they sign in with their
Twitter account, they can either use the mentions and hashtags automatically for search
setup instead of typing keywords or to check visually the status of their network. This
happens through visualization of recent collaboration and interactions based upon data
from their Twitter accounts [3] (link to video on this procedure 40 ). Figure 6 depicts the
network of a researcher. The size of the scholar is in the middle between the minimum
and maximum size of a node. As much as possible users are placed around the focused
researcher. The more publications someone coauthored with the scholar, the bigger the
node. Several visual aspects aid the user in focusing and exploring the current state of
their network:
– Spatial: the number of co-authorships determines distance to center, a higher num-
ber results in a closer distance.
– Size: a higher frequency of being mentioned together on social media (i.e. Twitter)
increases the size.
– Color: green, already in their Twitter network; red, not in their Twitter network.
– Tooltip: displays facts about the collaborations (e.g. co-authorships and mentions),
i.e. the number of mentions for a specific conference (where conference identifica-
tion is done over user hashtags which are automatically matched with COLINDA
conference labels) and the the number of co-publications. The co-autorships are re-
38
http://dbpedia.org
39
http://geonames.org
40
http://youtu.be/QopnPvWIFzw
10 Selver Softic et al.
Fig. 5. ResXplorer - discovering scholar artifacts like conferences (represented as stars), miscel-
laneous related resources such as locations or microblog posts (represented as dots in different
colors) etc. The distance to central node represents the intensity of relation.
Fig. 6. The scholar is centered in the middle and the network is visualized in nodes around the
central (blue) node.
COLINDA: Modeling, Representing and Using Scientific Events in the Web of Data 11
solved by bibliographic records from DBLP which are matched pair-wise between
the users.
Users who whether have no co-autorships or common mentions and conference hash-
tags with central user profile are not included in visualization.
4 Conclusion and Outlook
In this work we described how we extract, model and create scientific events as Linked
Data from known conference portals. We showed also how those events can be en-
hanced with additional relevant information and applied as as mining source for gen-
eration and enhancement of Researcher Affinity Browser as well as main interlinking
hub for discovery of research related artifacts for the ResXplorer. This potential has
been also recognized by the LinkedUp Challenge at the ISWC 201441 and upcoming
Semantic Publishing Challenge 201542 at ESWC 2015 where COLINDA is nominated
as reference Linked Data set for scientific events. As one of the future efforts we also
want to implement a DBPedia Lookup43 and Spotlight44 like service for detection and
identification of scientific events with COLINDA. We also want to link the instances to
WorldCat URIs of the published proceeding volumes and to he Crossref DOIs of the
published conference articles to make it more useful for the library linked data commu-
nity. Finally, to verify the quality of COLINDA we will run in the future an evaluation
against Linked Data Integration Benchmark (LODIB)45 .
Acknowledgments.
The research activities that have been described in this paper were funded by Ghent
University, the Social Learning Department at Graz University of Technology, iMinds
(an independent research institute founded by the Flemish government to stimulate ICT
innovation), the Institute for the Promotion of Innovation by Science and Technology
in Flanders (IWT), the Fund for Scientific Research-Flanders (FWO-Flanders), and the
European Union.
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