Named Entity Disambiguation on an Ontology Enriched by Wikipedia

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Named Entity Disambiguation on an Ontology
Enriched by Wikipedia
International IEEE Conference - RIVF08

• Hien Thanh Nguyen1, Tru Hoang Cao2
• 1Ton Duc Thang University, Vietnam
• 2Ho Chi Minh City University of Technology,
  Vietnam

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Outline

• Introduction
• Background
• Approach
• Evaluation
• Conclusion

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Introduction

• No explicit semantic information about data and
  objects are presented in most of the Web pages.
• Semantic Web aim at solving this problem by
  making semantic metadata available in web page
  content
• Ex the entity John McCarthy pointing to the
  homepage of the inventor of Lisp programming
• Entity disambiguation

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Introduction- Entity disambiguation

• Entity disambiguation is the process of
  identifying when different references correspond
  to the same real world entity (Jorge Cardoso and
  Amit Sheth)
• Our work aim at detecting named entities in a
  text and linking them to a given ontology

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Introduction - What are Named Entities?

• Named Entities (NE) are considered people,
  organizations, locations, date, time, money,
  measures, percentage, etc.
• Example

Ms. Washington's candidacy is being championed
by several powerful lawmakers including her boss,
Chairman John Dingell (D., Mich.) of the House
Energy and Commerce Committee.
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Introduction Basic problem in NE

• Many NEs share the same name
• Ambiguity of NE types John Smith (company vs.
  person)
• May (person vs. month)
• Washington (person vs. location)
• etc.
• Ambiguity of referent (e.g. Paris may be the
  capital of French, or a small town in Texas)

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Introduction - Our contribution are two-fold

• Utilizing ontological concepts, and properties of
  instances in a specific KB, to automatically
  generate a corpus of labeled training data
• Exploiting Wikipedia to enrich the training data
  with new and informative features.
• Exploring a range of features extracted from
  texts, a KB, and Wikipedia

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Background - Ontology

• Ontology schema defines taxonomy of classes and
  properties (relations and attributes)
• Knowledge base contains semantic descriptions,
  including attributes and relations, of named
  entities in real world

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Background - Wikipedia

• Each article defines an entity or a concept
• Four sources of information
• Title
• Redirect titles
• Categories
• Hyperlinks
• Outlinks vs. Inlinks

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Background - Wikipedia
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Approach

• Expoiting terms (i.e. base noun phrases) and
  named entities coocurring with ambiguous name for
  disambiguation
• Casting the problem as ranking problem
• Using TFIDF to calculate similarity and choose
  the candidate with the highest score

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Approach

• Constructing corpus
• Utilizing classes and properties to generate a
  snippet for each instance in an ontology
• Feature generation for enriching representation
  of those instances
• Analyzing a text for disambiguation and
  identification of NEs occurring therein

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Approach - Construct corpus
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Approach- Construct corpus
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Approach Disambiguation process

• For each ambiguous name
• Looking up candidates
• Extracting base noun phrases in the same sentence
  an in the headline
• Extracting named entities in the whole text
• Using TFIDF to rank and choose the candidate with
  the highest score

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Approach An example
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Evaluation

• Using KIM Ontology
• 140 texts of news articles in some news agencies
• Focusing on four names John McCarthy, John
  Wiliams, Georgia, and Columbia
• Measure accuracy as the total number of correctly
  assignment NEs (in text)/ontology instances
  divided by the total number of assignment

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Evaluation
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Conclusion

• Our approach is quite natural and similar to the
  way humans do, relying on co-occurring NEs and
  terms to resolve other ambiguous entities in a
  given context.
• Currently Wikipedia editions are available for
  approximately 200 languages, so our method can be
  used to build NE disambiguation systems for a
  large number of languages
• The features from Wikipedia, and NEs in the whole
  text are meaningful evidence for disambiguation
• In the future detecting NEs out of the ontology,
  and investigating other similarity metrics

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Thanks for your attention !