Using Wikipedia to Enable Entity Retrieval and Visualization Concerning the Intellectual/Cultural Heritage

1
Using Wikipedia to Enable
Entity Retrieval and
Visualization Concerning
the Intellectual/Cultural
Heritage
Athenikos, Sofia J.
sofia.j.athenikos@acm.org
Drexel University, Philadelphia, USA
At the 2009 Digital Humanities conference
I presented my paper on the
WikiPhiloSofia (
http://research.cis.drexel.e
du:8080/sofia/WPS/
) project (Athenikos and
Lin, 2009), which was concerned with
extraction and visualization of facts, relations,
and networks concerning philosophers using
Wikipedia (
http://www.wikipedia.org/
) as the
data source. In this proposal, I present
a related, extended project in progress,
entitled PanAnthropon, which incorporates the
problems of retrieving entities in response to
a query and retrieving entities related to a
given entity and which extends the scope of
application to domains other than philosophy.
1. Background
Traditional information retrieval is concerned
with retrieving documents that are potentially
relevant to a user’s query. The relevance of a
document to a given query is usually measured
by lexico-syntactic matching between the terms
in the query and those in the document (title).
Familiar Web search engines, such as Google
and Yahoo, for example, return a ranked list
of Web pages that contain all or some of
the keywords in the query entered by a user.
The Semantic Web (Berners-Lee et al., 2001)
initiative aims at transforming the Web of pages
(documents) into the Web of entities (things
in the broadest sense) (cf. OKKAM project (
h
ttp://www.okkam.org/
) (Bouquet et al., 2007)).
Information retrieval on the Semantic Web is
no longer a matter of retrieving documents
via semantics-unaware keyword matching but
a matter of retrieving entities that satisfy the
semantic constraints imposed by the query, i.e.
those that are of specific semantic type and that
satisfy the given semantic conditions. Wikipedia
has become an important semantic knowledge
resource (cf. Zesch et al., 2007) thanks to its
unique set of semi-structured semantic features
and the huge amount of content covering a wide
range of topics. What renders Wikipedia more
interesting is the fact that it can be considered as
a self-contained web of entities. Each Wikipedia
article is concerned with one entity, and the
given entity is connected to other entities via
explicit semantic relations as in infoboxes and
wikitables or via implicit semantic relations as in
hyperlinks.
2. Motivation
Through the WikiPhiloSofia project I
demonstrated extracting, retrieving, and
visualizing specific facets of information, not
documents, concerning entities of a selected
type, namely, philosophers, by exploiting the
hyperlinks, categories, infoboxes, and wikitables
contained in Wikipedia articles. The interface
that I created enables the users to select a focus
of query in the form of an entity (philosopher)
or a pair of entities (philosophers) and then to
retrieve entities that satisfy specified conditions
with respect to the given entity or pair of
entities. However, the project did not consider
the problems of retrieving entities as answers
to queries, semantically typing entities, or
retrieving related entities by type and condition.
The proposed PanAnthropon project takes up
the aforementioned problems left out of the
WikiPhiloSofia project. The dual objective is
to enable retrieval of entities that directly
answer a given semantics-based query and
to enable retrieval of related entities by
semantic type, subtype (role), and relation,
by using information extracted/integrated from
Wikipedia. The project applies the approach to
the entities concerning the intellectual/cultural
heritage – people, works, concepts, etc. The Web
portal interface thereby constructed will allow
the users to retrieve entities that directly answer
their queries as well as to explore people, works,
concepts, etc.
in relation to
other people, works,
concepts, etc.
3. Conceptualization
In the proposed project, “entities” are conceived
of as things of all kinds that have certain

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properties (or attributes). The “type” of an
entity is considered as a generic kind (or class
or category) into which the given entity is
classified, e.g., person, work, etc. In general,
the type of an entity is fixed and exclusive in
the sense that an entity that belongs to one
type does not or cannot belong to other types.
The “subtype” of an entity refers to a subclass
or subcategory into which the entity can be
classified, under a given type. The subtype of
an entity is fluid and non-exclusive in the sense
that an entity may belong to more than one
subtype (under a given type). This is especially
so in the case of person-type entities, and thus
a subtype may better be understood as a “role”
in this case. In general, there are multiple
subtypes under a given type, and the former
can be further classified into still more specific
subtypes. A type or subtype of an entity can be
considered as a special kind of property. A “fact”
concerning an entity refers to a tuple consisting
of <entity, attribute, value, [context]>, which
adds the optional “context” element to the
<subject, predicate, object> triple model. An
entity can have “relations” to other entities,
given its properties. The kinds of properties
and relations that are relevant or of interest
concerning an entity, except certain basic facts,
depend on the domain at issue. An entity
may belong to multiple domains, but not every
subtype, property, or relation is relevant or
equally important in one domain as in another
domain. The project therefore intends to build
a portal consisting of sub-portals representing
different domains.
4. Methodology
4.1. Data Extraction and Processing
The pre-processing stage of the project (for
each domain) concerns: (1) compiling a seed
list of entities of interest by extracting names
from various lists and categories in Wikipedia,
(2) downloading Wikipedia article pages for
each entry on the list, and (3) inspecting
typical attributes and (types/subtypes of) values
contained in infoboxes, wikitables, etc. The
main processing stage concerns extracting
information on an entity and related entities
from each Wikipedia page. The semantic type/
subtypes of a given entity are extracted and/
or assigned. Semi-structured templates and
portions of the article are processed so as to
extract attribute–value (or predicate–object or
relation–entity) pairs. The related entities are
matched to the entities on the seed entity list.
Additional Wikipedia pages are downloaded
for entities not matched on the list, and
the information on those entities is extracted.
The (optional) post-processing stage concerns
converting the data stored in a MySQL database
to XML files and RDF triples, thereby creating
a semantic data repository that can be linked
to other resources involved in the Linked Data
(
http://linkeddata.org/
) initiative in the latter
case.
4.2. Semantic Search Interface
The semantic search interface created will
support three types of query and retrieval. The
first type of query/retrieval concerns retrieval of
entities that correspond to queries the expected
answers of which are entities. The second type
of query/retrieval concerns retrieval of entities
related to an entity, according to type/subtypes
and specified properties/values. The third type
of query/retrieval concerns retrieval of facts
concerning an entity. The interface will also
incorporate some of the visualization features
available in the WikiPhiloSofia portal interface.
5. Current Application
The film domain has been chosen as the
initial proof-of-concept domain of application.
In my presentation I will demonstrate the entity
retrieval functionalities with 1.5+ million (and
growing) facts about 11370 films, 69545 persons,
74545 film roles, 253 places, 6033 dates, etc.
6. Related Work
The task of retrieving entities in response to
user queries using the information in Wikipedia
has since 2007 been the focus of the INEX
(Initiative for the Evaluation of XML Retrieval)
XML Entity Ranking (XER) Track (de Vries
et al., 2008; Demartini et al., 2009). Unlike
in the INEX XER Track, the proposed project
addresses the task by extracting information
from the HTML Wikipedia files and building
a knowledge base based on it. The task of
constructing a knowledge base by extracting
information from templates in Wikipedia such
as infoboxes has been attempted in large

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scale by, e.g., Auer and Lehmann (2007)
and Suchanek et al. (2007). There is also
the DBpedia (
http://dbpedia.org/
) knowledge
base, which contains the information extracted
from Wikipedia. The proposed project, however,
utilizes the information in the main content
of Wikipedia articles, as well as templates, to
enable and enhance entity retrieval. It will also
provide a more flexible working search interface
for both general and entity-specific queries.
7. Conclusion
The PanAnthropon project utilizes Wikipedia
as a semantic knowledge source for entity
retrieval and applies the approach to materials
concerning the intellectual/cultural heritage.
The semantic search interface created will allow
the users to retrieve entities that directly answer
their queries as well as to explore various
semantic facets concerning those entities. As
such, it will provide a useful resource for digital
humanities.
References
Athenikos, S.J., Lin, X.
(2009).
'WikiPhiloSofia: Extraction and Visualization
of Facts, Relations, and Networks Concerning
Philosophers Using Wikipedia'.
Conference
Abstracts of Digital Humanities 2009.
Pp.
56-62.
Auer, S., Lehmann, J.
(2007). 'What Have
Innsbruck and Leipzig in Common? Extracting
Semantics from Wiki Content'.
Proceedings
of 4th European Semantic Web Conference
(ESWC 2007).
Innsbruck, Austria, June 2007.
Berners-Lee, T., Hendler, J., Lassila,
O.
(2001). 'The Semantic Web'.
Scientific
American.
5 (May 2001)
.
Bouquet, P., Stoermer, H., Giacomuzzi,
D.
(2007). 'OKKAM: Enabling a Web of
Entities'.
Proceedings of the 16th International
World Wide Web Conference (WWW 2007).
Banff, Alberta, Canada, 8-12 May 2007.
Demartini, G., de Vries, A.P., Iofciu, T.,
Zhu, J.
(2009).
Overview of the INEX 2008
Entity Ranking Track, INEX 2008.
LNCS.
Heidelberg: Springer-Verlag, Berlin. V. 5631, pp.
243-252.
de Vries, A.P., Vercoustre, A.-M., Thom,
J.A., Craswell, N., Lalmas, M.
(2008).
Overview of the INEX 2007 Entity Ranking
Track, INEX 2007.
LNCS. Heidelberg:
Springer-Verlag, Berlin. V. 4862, pp. 245-251.
Suchanek, F.M., Kasneci, G., Weikum, G.
(2007). 'YAGO: A Core of Semantic Knowledge
Unifying WordNet and Wikipedia'.
Proceedings
of the 16th International World Wide Web
Conference (WWW 2007).
Banff, Alberta,
Canada, pp. 697-706.
Zesch, T., Gurevych, I., Mühlhäuser, M.
(2007). 'Analyzing and Accessing Wikipedia
as a Lexical Semantic Resource'.
Proceedings
of the Biannual Conference of the Society
for Computational Linguistics and Language
Technology.
Tübingen, Germany, April 2007.