=Paper=
{{Paper
|id=Vol-1224/paper3
|storemode=property
|title=Three Birds (in the LLOD Cloud) with One Stone: BabelNet, Babelfy and the Wikipedia Bitaxonomy
|pdfUrl=https://ceur-ws.org/Vol-1224/paper3.pdf
|volume=Vol-1224
|dblpUrl=https://dblp.org/rec/conf/i-semantics/FlatiN14
}}
==Three Birds (in the LLOD Cloud) with One Stone: BabelNet, Babelfy and the Wikipedia Bitaxonomy==
https://ceur-ws.org/Vol-1224/paper3.pdf
Three Birds (in the LLOD Cloud) with One
Stone: BabelNet, Babelfy and the
Wikipedia Bitaxonomy
Tiziano Flati and Roberto Navigli
Dipartimento di Informatica
Sapienza Università di Roma
Abstract. In this paper we present the current status of linguistic re-
sources published as linked data and linguistic services in the LLOD
cloud in our research group, namely BabelNet, Babelfy and the Wikipedia
Bitaxonomy. We describe them in terms of their salient aspects and ob-
jectives and discuss the benefits that each of these potentially brings to
the world of LLOD NLP-aware services. We also present public Web-
based services which enable querying, exploring and exporting data into
RDF format.
1 Introduction
Recent years have witnessed an upsurge in the amount of semantic information
published on the Web. Indeed, the Web of Data has been increasing steadily
in both volume and variety, transforming the Web into a global database in
which resources are linked across sites. It is becoming increasingly critical that
existing lexical resources be published as Linked Open Data (LOD), so as to
foster integration, interoperability and reuse on the Semantic Web [5]. Thus,
lexical resources provided in RDF format can contribute to the creation of the
so-called Linguistic Linked Open Data (LLOD), a vision fostered by the Open
Linguistic Working Group (OWLG), in which part of the Linked Open Data
cloud is made up of interlinked linguistic resources [2].
The multilinguality aspect is key to this vision, in that it enables Natural
Language Processing tasks which are not only cross-lingual, but also independent
both of the language of the user input and of the linked data exploited to perform
the task. Both the Semantic Web and Natural Language Processing communities
have to face the new challenge of facilitating multilingual access to the Web of
data.
The benefits of such a Web of Linguistic Data are diverse and lie on both
Semantic Web and NLP sides. On the one hand, ontologies and linked data sets
can be augmented with rich linguistic information, thereby enhancing Web-based
information processing. On the other hand, NLP algorithms can take advantage
of the availability of a vast, interoperable and federated set of linguistic resources,
as well as benefit from a rich ecosystem of formalisms and technologies.
Posters & Demos Track @ SEMANTiCS2014 11
Fig. 1. BabelNet overview (picture from [10]).
This paper presents a contribution for the Multilingual Web of Data, with the
publication of BabelNet, Babelfy and the Wikipedia Bitaxonomy as linked data.
We describe the three projects in terms of their salient aspects and objectives
and discuss the benefits that each of these potentially brings to the world of
LLOD NLP-aware services.
2 Three birds in the LLOD cloud
We now describe the three major tools and resources oriented to the Linguistic
Linked Open Data Cloud developed in our research group. Despite being different
in nature as well as in their goals (Babelfy is a service while BabelNet and the
Wikipedia Bitaxonomy are linguistic resources), they all have in common the
linked data layer that enables the interlinking of information across entities.
The three services, already useful on their own, are closely intertwined and
beneficial to each other: in fact, while on the one hand the BabelNet semantic
network lies at the core of Babelfy, on the other hand the Wikipedia Bitaxonomy
is also integrated into BabelNet and acts as the taxonomical backbone of the
resource.
BabelNet BabelNet [10] is a very large multilingual encyclopedic dictionary
and ontology which covers 50 languages. Based on the automatic integration
of lexicographic and encyclopedic knowledge extracted from multiple resources
(WordNet, Wikipedia, Open Multilingual WordNet, OmegaWiki, Wiktionary
and WikiData), BabelNet offers a large network of concepts and named entities
along with an extensive multilingual lexical coverage (see Fig. 1). The last version
of BabelNet is available at babelnet.org and a SPARQL endpoint is also acces-
sible at babelnet.org:8084/sparql/. Based on the lemon model [7], a lexicon
model for representing and sharing ontology lexica on the Semantic Web, the
RDF-version of BabelNet (lemon-BabelNet) features more than 1 billion triples
which describe 9.3 million concepts with encyclopedic and lexical information
in 50 languages. The resource is interlinked with several other datasets includ-
ing DBpedia and lemon-WordNet, thus laying the foundations for further linked
data-based integration of ontology lexica.
Babelfy The current language explosion on the Web requires the ability to
automatically analyze and understand text written in any language. This task
12 Flati & Navigli
(a) Babelfy Web application. (b) WiBi Web application.
Fig. 2. Screenshots of Babelfy (a) and the Wikipedia Bitaxonomy Explorer (b).
however is affected by the lexical ambiguity of language, an issue addressed by
two key tasks: Multilingual Word Sense Disambiguation (WSD) [1, 9], aimed at
assigning meanings to word occurrences within text, and Entity Linking (EL)
[11], a recent task focused on finding mentions of entities within text and linking
them to a knowledge base.
Babelfy [8] is a unified, multilingual WSD and EL system based on Babel-
Net, which disambiguates and links text written in different languages, and also
produces multilingual linked data as output (see Fig. 2(a)). At its core are the
combination of a loose candidate identification with a novel densest graph heuris-
tic. Babelfy fares well both on long texts, such as those of the WSD tasks, and
short sentences, such as the ones in EL tasks, thus bringing together the best
of the two worlds. Experiments conducted on six gold-standard datasets used in
WSD and EL tasks show that Babelfy provides state-of-the-art results both in
monolingual and multilingual settings. Babelfy also comes with a RESTful API
which programmatically enables users to retrieve disambiguated text with a few
Java lines. An online version of Babelfy is accessible at babelfy.org.
The Wikipedia Bitaxonomy The Wikipedia Bitaxonomy, also known as
WiBi, is a project which aims at automatically extracting two taxonomies, one
for Wikipedia pages and one for Wikipedia categories, aligned to each other, in
a joint fashion with state-of-the-art results (see [4] for details).
Extensive comparison has been carried out on two datasets of 1,000 pages
and categories respectively, against all the available knowledge resources, in-
cluding MENTA, DBpedia, YAGO, WikiTaxonomy and WikiNet (see [6] for a
comprehensive survey). Results show that WiBi overcomes all competitors not
only in terms of quality, with the highest precision and recall, but also in terms
of coverage and specificity.
WiBi is also integrated into BabelNet and explorable through a Web appli-
cation at wibitaxonomy.org (see Fig. 2(b)). Backed by the Apache Jena frame-
work, the explorer integrates a single-click functionality that seamlessly converts
the displayed data into RDF format (either Turtle, RDF/XML or N-Triple), in
line with recent work on LLOD and the Semantic Web (see [3]).
Posters & Demos Track @ SEMANTiCS2014 13
3 Conclusions
We described resources and services that seamlessly integrate linked data fa-
cilities and thus foster interoperability within the LLOD cloud, also across lan-
guages. Despite addressing different goals and offering different services, all of the
three tools export data into RDF format and thus enable NLP-aware services to
consume and re-elaborate data through the Semantic Web. If carefully published
and interlinked, these tools could, indeed, potentially turn into a huge body of
machine-readable knowledge and move on towards a full-fledged linguistic linked
open data cloud.
Acknowledgments
The authors gratefully acknowledge the support of the
ERC Starting Grant MultiJEDI No. 259234.
The authors also acknowledge support from the LIDER project (No. 610782), a
Coordination and Support Action funded by the European Commission under
FP7.
References
1. Banea, C., Mihalcea, R.: Word Sense Disambiguation with Multilingual Features.
In: Proc. of IWCS 2011. pp. 25–34. Association for Computational Linguistics,
Stroudsburg, PA, USA
2. Chiarcos, C., Hellmann, S., Nordhoff, S.: Towards a Linguistic Linked Open Data
Cloud: The Open Linguistics Working Group. TAL 52(3), 245–275 (2011)
3. Ehrmann, M., Cecconi, F., Vannella, D., Mccrae, J.P., Cimiano, P., Navigli, R.:
Representing Multilingual Data as Linked Data: the Case of BabelNet 2.0. In:
Proc. of LREC 2014. pp. 401–408. Reykjavik, Iceland
4. Flati, T., Vannella, D., Pasini, T., Navigli, R.: Two Is Bigger (and Better) Than
One: the Wikipedia Bitaxonomy Project. In: Proc. of ACL 2014. pp. 945–955.
Baltimore, Maryland
5. Gracia, J., Montiel-Ponsoda, E., Cimiano, P., Gómez-Pérez, A., Buitelaar, P., Mc-
Crae, J.: Challenges for the multilingual web of data. J. Web Sem. 11, 63–71 (2012)
6. Hovy, E.H., Navigli, R., Ponzetto, S.P.: Collaboratively built semi-structured con-
tent and Artificial Intelligence: The story so far. Artificial Intelligence 194, 2–27
(2013)
7. McCrae, J., Montiel-Ponsoda, E., Cimiano, P.: Collaborative semantic editing of
linked data lexica. In: Proc. of LREC 2012. pp. 2619–2625. Istanbul, Turkey
8. Moro, A., Raganato, A., Navigli, R.: Entity Linking meets Word Sense Disam-
biguation: a Unified Approach. TACL 2, 231–244 (2014)
9. Navigli, R., Ponzetto, S.P.: Joining Forces Pays Off: Multilingual Joint Word Sense
Disambiguation. In: Proc. of EMNLP-CoNLL 2012. pp. 1399–1410. Jeju Island,
Korea
10. Navigli, R., Ponzetto, S.P.: BabelNet: The automatic construction, evaluation and
application of a wide-coverage multilingual semantic network. Artificial Intelligence
193, 217–250 (2012)
11. Rao, D., McNamee, P., Dredze, M.: Entity Linking: Finding Extracted Entities
in a Knowledge Base. In: Multi-source, Multi-lingual Information Extraction and
Summarization, pp. 93–115 (2013)