Download as PDF, PPTX
![“The number of contexts [entities] is
overwhelming and had to be reduced to
a manageable size.”
- Cucerzan 2007](https://image.slidesharecdn.com/presentation-140812113218-phpapp01/85/PhD-Day-Entity-Linking-using-Ontology-Modularization-43-320.jpg)
![“Much speed is gained by imposing a
threshold below which all senses
[candidates] are discarded”
- Milne and Witten 2008](https://image.slidesharecdn.com/presentation-140812113218-phpapp01/85/PhD-Day-Entity-Linking-using-Ontology-Modularization-44-320.jpg)
Uploaded byBianca Pereira
PhD Day: Entity Linking using Ontology Modularization
The document outlines the PhD research of Bianca Pereira, focusing on entity linking, which involves grounding entity mentions in documents to knowledge base entries. It discusses the challenges of disambiguation in large knowledge bases and proposes solutions like approximation algorithms and dimensionality reduction to improve accuracy and efficiency. The research questions and hypotheses aim to explore the potential for manageable disambiguation times regardless of knowledge base size, as well as the effectiveness of exact algorithms in this context.
More Related Content
Similar to PhD Day: Entity Linking using Ontology Modularization
PhD Day: Entity Linking using Ontology Modularization
- 1. PhD Day –04/2014 Bianca Pereira
- 2.
- 4. Outline Literature Review Define thePhD topic
- 5.
- 8. Entity Linking is.. “Groundingentity mentions in documents to Knowledge Base entries” - TAC-KBP 2009
- 10.
- 12.
- 13.
- 14. Types of Entity Domainsof Knowledge Methods Accuracy Time
- 15. Types of Entity NamedEntities Unamed Entities Topics Classes Natural Language Processing Statistics Entity Linking
- 16.
- 17.
- 19. EVERYTHING ! Natural LanguageProcessing Statistics Entity Linking
- 20.
- 21. Types of Entity NamedEntities Given by Class Given by Knowledge Base Others
- 22. Types of Entity NamedEntities Given by Class Given by Knowledge Base Others
- 23.
- 24. Domains of Knowledge Cross-domainKnowledge Base
- 25. Methods “(…) Collective Inferenceover a set of entities can lead to better performance.” - Stoyanov et al 2012
- 28. Named Entity RecognitionDisambiguation
- 30.
- 31.
- 32. Collective Inference arealgorithms for Disambiguation Co
- 34.
- 35. A Local Contextis used to give the mention-candidate score Co URI1
- 36. There is coherencebetween entities in the same document. Co
- 37.
- 38.
- 39.
- 40. Disambiguation using collective inferenceis a NP problem. Co
- 41.
- 42.
- 43. “The number ofcontexts [entities] is overwhelming and had to be reduced to a manageable size.” - Cucerzan 2007
- 44. “Much speed isgained by imposing a threshold below which all senses [candidates] are discarded” - Milne and Witten 2008
- 45. “Inference is NPHard” - Kulkarni et al 2009
- 46. “(…) exact algorithmson large input graphs are infeasible.” - Hoffart et al 2011
- 47. Collective Inference -Accuracy
- 48. Collective Inference -Time Using approximation algorithms the time is suitable for the task
- 49.
- 50. Recalling Given by KnowledgeBase Cross-domain Knowledge Base
- 52. ~ 5 MILLIONentities
- 53. ~ 10 MILLIONentities
- 54. ~ 43 MILLIONentities
- 56. Problem Statement The timespent in disambiguation for Entity Linking increases with the size of the Knowledge Base. It turns the disambiguation with large Knowledge Bases infeasible.
- 57.
- 58. Two solutions forthe problem.. 1. Approximation Algorithms 2. Dimensionality Reduction
- 59. Approximation Algorithms Kulkarni etal 2009, Hoffart et al 2011
- 60. Dimensionality Reduction URI1 URI4URI5 URI6 URI7 URI8 230 24 URI1 URI4URI5 URI6 URI7 URI8 URI2 URI3 URI9 URI10 Cucerzan 2007,Milne and Witten 2008, Hoffart et al 2011
- 61.
- 62. Dimensionality Reduction (candidate space) Algorithm KnowledgeBase Related Work
- 63. Dimensionality Reduction (candidate space) Algorithm KnowledgeBase Related Work
- 64.
- 65. R1. Is itpossible to delimit a feasible maximum amount of time for disambiguation regardless of the size of the Knowledge Base? R2. Is it possible to reduce the dimensionality directly in the Knowledge Base? R3. Is it feasible to use exact algorithms for disambiguation using large Knowledge Bases?
- 66. R1. Is itpossible to delimit a feasible maximum amount of time for disambiguation regardless of the size of the Knowledge Base? R2. Is it possible to reduce the dimensionality directly in the Knowledge Base? R3. Is it feasible to use exact algorithms for disambiguation using large Knowledge Bases?
- 67. R1. Is itpossible to delimit a feasible maximum amount of time for disambiguation regardless of the size of the Knowledge Base? R2. Is it possible to reduce the dimensionality directly in the Knowledge Base? R3. Is it feasible to use exact algorithms for disambiguation using large Knowledge Bases?
- 68.
- 69. R1. Is itpossible to delimit a feasible maximum amount of time for disambiguation regardless of the size of the Knowledge Base? H1. There is a maximum size of candidate set that allows disambiguation in a feasible time.
- 70. R1. Is itpossible to delimit a feasible maximum amount of time for disambiguation regardless of the size of the Knowledge Base? H2. If the Knowledge Base can be divided in subsets of constant ambiguity then the candidate space is constant.
- 71. R1. Is itpossible to delimit a feasible maximum amount of time for disambiguation regardless of the size of the Knowledge Base? Subset of constant ambiguity Candidate space constant Candidate space = maximum allowed size Feasible time
- 72. R2. Is itpossible to reduce the dimensionality directly in the Knowledge Base? H3. The relatedness between entities is a sufficient condition to reduce the dimensionality without loss of accuracy.
- 73. R3. Is itfeasible to use exact algorithms for disambiguation using large Knowledge Bases? H4. Decreasing the ambiguity in the Knowledge Base is less time consuming that perform it at disambiguation time.
- 74. R3. Is itfeasible to use exact algorithms for disambiguation using large Knowledge Bases? H5. Exact algorithms can be used in a feasible time until a maximum size of candidate space.
- 75.
- 76. Ontology Modularization for Disambiguationin Entity Linking
- 77.
- 78.
- 79. How to Generatethe Modules? Semantic-Driven Strategies Depends on the Application. Structure-Driven Strategies Graph Decomposition based on inter-relation. Machine Learning Strategies Data Mining and Clustering.
- 80.
- 81. H1. There isa maximum size of candidate set that allows disambiguation in a feasible time.
- 82. H1. There isa maximum size of candidate set that allows disambiguation in a feasible time. Perform an experiment using different collective inference approaches to discover how the time increases with the size of the candidate set.
- 83. H2. If theKnowledge Base can be divided in subsets of constant ambiguity then the candidate space is constant.
- 84. H2. If theKnowledge Base can be divided in subsets of constant ambiguity then the candidate space is constant. Perform Ontology Modularization aiming a maximum ambiguity in each module.
- 85. H3. The relatednessbetween entities is a sufficient condition to reduce the dimensionality without loss of accuracy.
- 86. H3. The relatednessbetween entities is a sufficient condition to reduce the dimensionality without loss of accuracy. Generate the module based on the same relatedness measure used by the original method and verify the accuracy.
- 87. H4. Decreasing theambiguity in the Knowledge Base is less time consuming that perform it at disambiguation time.
- 88. H4. Decreasing theambiguity in the Knowledge Base is less time consuming that perform it at disambiguation time. Measure the time for disambiguation r e d u c i n g t h e d i m e n s i o n a l i t y a t disambiguation time and using the Modularization approach.
- 89. H5. Exact algorithmscan be used in a feasible time until a maximum size of candidate space.
- 90. H5. Exact algorithmscan be used in a feasible time until a maximum size of candidate space. Select a set of exact algorithms and measure the time for different sizes of candidate space.
- 92. Next Steps Doctoral Consortium TAC-KBP FirstExperiments Use Cases
- 93.