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ITC-irst, via Sommarive 18, I-38050 Trento-Povo, Italy

Using NLP Techniques for Meaning Negotiation Bernardo Magnini, Luciano Serafini and Manuela Speranza. ITC-irst, via Sommarive 18, I-38050 Trento-Povo, Italy. Motivations Matching algorithm NLP techniques Conclusions. Outline.

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ITC-irst, via Sommarive 18, I-38050 Trento-Povo, Italy

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  1. Using NLPTechniques for Meaning Negotiation Bernardo Magnini, Luciano Serafini and Manuela Speranza ITC-irst, via Sommarive 18, I-38050 Trento-Povo, Italy

  2. Motivations Matching algorithm NLP techniques Conclusions Outline

  3. Autonomous communities within an organization have their own conceptualizations of the world, that are partial and perspectival Meaning negotiation is a dynamic process, through which mappings between different conceptualizations are discovered Meaning negotiation in Distributed KM

  4. Local Ontology • A set of terms and relations used by the members of an autonomous community to operate with local knowledge • Examples: the directory structure of a file system, the logical organization of a web site, e-commerce catalogues, etc. • Data structures: local ontologies are represented by means of contexts

  5. Examples of contexts Context A Context B Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope Puglia Spain USA

  6. Examples of contexts Context A Context B Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope Puglia Spain USA

  7. Mapping between contexts Source context Target context Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope Puglia Spain USA

  8. Mapping between contexts Source context Target context Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope Puglia Spain USA ?

  9. Mapping between contexts Source context Target context Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope  Puglia Spain USA

  10. Problems • Relations between concepts expressed by different labels (e.g. ‘holiday’ is more general than ‘honeymoon’ but equal to ‘vacation’) • Semantic ambiguity of labels (e.g. ‘apple’ as a fruit vs. ‘apple’ as a computer brand) • Structural differences between overlapping heterogeneous contexts (e.g. classification of holidays according to years vs. places)

  11. Our proposal • Use of a lexical database (WordNet) • Creation of specific rules for sense disambiguation • Interpretation of hierarchical relations as syntactic dependency relations

  12. WordNet senses and concepts: the word ‘vacation’ [vacation#2] [leisure#1, leisure time#1] ISA [vacation#1, holiday#1] ISA [honeymoon#1]

  13. ‘Vacation’ in WordNet Sense 1vacation, holiday       => leisure, leisure time           => time off               => time period, period of time, period                   => fundamental quantity, fundamental measure                       => measure, quantity, amount, quantum                           => abstractionSense 2vacation       => abrogation, repeal, annulment           => cancellation               => nullification, override                   => change of state                       => change                           => action                               => act, human action, human activity

  14. Context mapping • A relation between a node S of a source context and a node T of a target context • Possible mappings: • S  T (e.g. animal  dog) • S  T (e.g. dog  animal) • S = T (e.g. holiday = vacation) • S  T (e.g. mountain  sea) • S * T (e.g. car * hi-fi)

  15. Matching algorithm (I) • Input: a source node in the source context and a target node in the target context • Output: a mapping between the source and the target node

  16. Matching algorithm (II) • Single labels’ analysis (linguistic and semantic) • Sense refinement rules • Sense matching

  17. Labels’ linguistic analysis • Input: a label = <token1, token2, …, token n> • Output: a data structure providing identification number, lemma, part of speech and linguistic function of each token • Example: Data structure for ‘Sea holidays’

  18. Labels’ semantic analysis • Use of WordNet as a repository of senses E.g. ‘sea’ has three senses: • sea#1: ‘a division of an ocean’ • sea#2: ‘anything apparently limitless’ • sea#3: ‘turbulent water’

  19. Labels’ semantic analysis • Use of WordNet as a repository of senses • Each token in the data structure is provided with its WordNet senses, if any

  20. Sense refinement (I) • Aim: Elimination of the w-senses that are in disagreement with other w-senses tree apple#1 (a fruit) apple#2 (a computer brand)

  21. Sense refinement (I) • Aim: Elimination of the w-senses that are in disagreement with other w-senses tree apple#1 (a fruit)

  22. Sense refinement (II) • Assumption: sibling nodes are disjoint • Consequence: if a W-concept has a part-of or an inclusion relation with a w-concept of a sibling node, the meanings have to be composed Italy#1 Europe#1

  23. Sense refinement (II) • Assumption: sibling nodes are disjoint • Consequence: if a W-concept has a part-of or an inclusion relation with a w-concept of a sibling node, the meanings have to be composed Italy#1 Europe#1 – Italy#1

  24. Mapping between contexts Source context Target context Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope Puglia Spain USA ?

  25. Contextual meanings Source context Target context Vacation Seaholidays 2001 2000 Mountains Sea Sea Lake Italy in Europe Puglia Spain USA ?

  26. Sense matrix

  27. Sense matrix

  28. Sense matrix

  29. Sense matrix

  30. Sense matrix

  31. Computing the matching via Sat (I): • The set of documents classifiable under a node is the intersection of the components of its contextual meaning (e.g. A1 ∩ A2, if the node has contextual meaning A1-A2) ii. Computing the mapping between two nodes means finding the best relation between the intersections

  32. Computing the matching via Sat (II): • For each single relation in the matrix a propositional formula is generated • Ai Bj Ai → Bj • Ai Bj Bj → Ai • Ai= Bj Ai Bj • Ai Bj ¬(AiΛ Bj) E.g. Spain → Europe holiday  vacation ¬(Italy Λ Spain)

  33. Computing the matching via Sat (III): iv.We check for satisfiability the union of all the propositions and the negation of the implication between the intersections E.g. (h v) Λ (S → E) Λ¬(I Λ S) ΛΛ¬(v Λ 2001 Λ s Λ S → h Λ s Λ E Λ¬I) v. If the check fails, the source node contains the target node; otherwise a similar procedure is followed for the other possible mappings

  34. Mapping between contexts Source context Target context Vacation Sea holidays 2001 2000 Mountains Sea Sea Lake Italy inEurope  Puglia Spain USA

  35. Conclusions • Meaning negotiation • Mappings between contexts • Matching algorithm

  36. Future Work • Evaluation of the algorithm • Further development of the algorithm • Use of the algorithm within an information retrieval system

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