BabelNet

1. Shrikrishna R. Parab
Mtech Part 1
DCST, Goa University
Mtech.parab@unigoa.ac.in
BabelNet: The automatic construction of a wide-
coverage multilingual semantic network

2. Outline
• References papers
• Introduction
• Knowledge resources
• WordNet
• Wikipedia
• BabelNet
• Mapping WordNet synset to wikipedia pages
• Disambiguation Context.

3. Reference papers
• R. Navigli and S. Ponzetto. BabelNet: The Automatic Construction,
Evaluation and Application of a Wide-Coverage Multilingual Semantic
Network. Artificial Intelligence, 193, Elsevier, 2012, pp. 217-250.
• M. Ehrmann, F. Cecconi, D. Vannella, J. McCrae, P. Cimiano, R.
Navigli. Representing Multilingual Data as Linked Data: the Case of
BabelNet 2.0. Proc. of the 9th Language Resources and Evaluation
Conference (LREC 2014), Reykjavik, Iceland, 26-31 May, 2014.
• S. Fernando and M. Stevenson, “Mapping WordNet synsets to
Wikipedia articles.,” in LREC, 2012, pp. 590–596.

4. Introduction
• In the information society, knowledge – i.e., the
information and expertise needed to understand any
subject of interest.
• Much information is conveyed by means of linguistic
communication, therefore it is critical to know how words
are used to express meaning, i.e., we need lexical
knowledge.
• lexical knowledge is an essential component for
performing language-oriented automatic tasks effectively.
• areas of Natural Language Processing (NLP) have been
shown to benefit from the availability of lexical knowledge
at different levels.

5. • In this paper author presented an automatic approach to
the construction of BabelNet.
• Key to this approach is the integration of lexicographic
and encyclopaedic knowledge from WordNet and
Wikipedia.
• Machine Translation is applied to enrich the resource with
lexical information for all languages.

6. Knowledge resource
• BabelNet aims at providing an “encyclopaedic dictionary”
by merging WordNet and Wikipedia
• WordNet
• Wikipedia

7. WordNet
• is by far the most popular lexical knowledge resource in
the field of NLP.
• A concept in WordNet is represented as a synonym set
(called synset), i.e., the set of words that share the same
meaning.
• WordNet provides a textual definition, or gloss for each
synset.
• Synsets can contain sample sentences to provide
examples of their usage
• It also consist of part of speech tagging.

8. • synsets are related to each other by means of lexical and
semantic relations.
• is-a relations such as hypernymy and hyponymy.
• instance-of relations denoting set membership between a
named entity and the class it belongs to.
• part-of relations expressing the elements of a partition by
means of meronymy and holonymy.

9. Wikipedia
• Wikipedia, is a multilingual Web-based encyclopaedia.
• It is a collaborative open source medium edited by
volunteers to provide a very large wide-coverage
repository of encyclopaedic knowledge.
• Each article in Wikipedia is represented as a page known
as Wikipage and presents information about a specific
concept or named entity.
• The title of a Wikipage is composed of the lemma of the
concept defined.
• an optional label in parentheses which specifies its
meaning if the lemma is ambiguous.

10. • Relation between the pages:
• Redirect pages: These pages are used to forward to the
Wikipage containing the actual information about a concept of
interest.
• Disambiguation pages: These pages collect links for a
number of possible concepts an arbitrary expression could be
referred to.
• Internal links: Wikipages typically contain hypertext linked to
other Wikipages, which refers to related concepts.
• Inter-language links: Wikipages also provide links to their
counterparts (i.e., corresponding concepts) contained within
wikipedias in other languages.
• Categories: Wikipages can be assigned to one or more
categories.

11. What is BabelNet??
• BabelNet is a multilingual lexicalized semantic network and
ontology.
• BabelNet was automatically created by linking the largest
multilingual Web encyclopaedia i.e. Wikipedia, to the most
popular computational lexicon of the English language
i.e. WordNet.
• combine WordNet and Wikipedia by automatically acquiring
a mapping between WordNet senses and Wikipages
• Harvest multilingual lexicalizations of the available concepts
by using (a) the human-generated translations provided by
Wikipedia,(b) a machine translation system to translate
occurrences of the concepts within sense-tagged corpora

12. Mapping WordNet synset to Wikipedia
pages
• This process is divided into 3 phases:
• Generation of Candidate Articles: aims to reduces the
search space by identifying a small set of candidate
articles for each noun synset.
• this can be done in 2 ways, 1st by matching words in
WordNet synsets.
• And secondly, using an Information Retrieval system to
search the full article text against Wikipedia article titles.
• Selecting the Best Mappings: uses this candidate article
set to select the best matching article for each synset.
• Refining the Mappings

13. Generation of Candidate Articles
• Two methods were used to find candidate articles: title
matching and Information Retrieval.
• The title matching approach examines the titles of
Wikipedia articles to identify WordNet synsets that could
map onto them.
• use of an Information Retrieval system to index Wikipedia
and makes use of entire articles in Wikipedia rather than
just their titles.
• This stage returns a set of candidate articles for each
noun synset in Wikipedia.

14. Selecting the Best Mappings
• This stage attempt to identify the best matching article
from this set using two methods: text similarity and title
similarity.
• Wikipedia articles are pre-processed by removing markup
then stemming and removing stopwords from the
remaining text.
• Various combinations of features from the WordNet
synset (lemmas, glosses, related lemmas etc.) are used
to calculate the similarities.

15. • The previous method use the whole Wikipedia article for
comparison.
• the title of the article is the single most important feature
when considering similarity to the synset.
• Therefore this method assigns a similarity score using the
title alone.

16. Refining the Mappings
• The result of the mapping from WordNet to Wikipedia is a
set of synset-article pairings.
• A global view of the mappings and information about the
link structure in Wikipedia is then used to refine the
mappings.
• It remove all mappings where more than one synset maps
to the same Wikipedia article i.e. many to 1 relations is
converted to 1 to 1 relation.

17. Disambiguation contexts
• Disambiguation context of a Wikipage:
• Sense labels: e.g., given the page Play (theatre), the
word theatre is added to the disambiguation context.
• Links: the titles’ lemmas of the pages linked from the
Wikipage w (i.e., outgoing links). For instance, the links in
the Wikipage Play (theatre) include literature, comedy,
etc.
• Redirections: the titles’ lemmas of the pages which are
redirecting to w are taken into context.
• Categories: Wikipages are typically classified according
to one or more categories. For example, the Wikipage
Play(theatre) is categorized as PLAYS, DRAMA,
THEATRE, etc.

18. • Disambiguation context of a WordNet sense:
• Synonymy: all synonyms of s in synset S. For instance,
given the synset of play, all its synonyms are included in
the context.
• Hypernymy/Hyponymy: all synonyms in the synsets H
such that H is either a hypernym (i.e., a generalization) or
a hyponym (i.e., a specialization) of S.
• Gloss: the set of lemmas of the content words occurring
within the gloss of s.

19. Representing Multilingual Data as Linked
Data: the Case of BabelNet 2.0

20. Thank you