partners Using NLP Techniques for Meaning Negotiation Bernardo Magnini, Luciano Serafini and Manuela Speranza ITC-irst, via Sommarive 18, I Trento-Povo, Italy

2 Outline  Motivations  Matching algorithm  NLP techniques  Conclusions

3 Meaning negotiation in Distributed KM  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

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 SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe

6 Examples of contexts Context A Context B Vacation SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe

7 Mapping between contexts Source context Target context Vacation SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe

8 Mapping between contexts Source context Target context Vacation SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe ?

9 Mapping between contexts Source context Target context Vacation SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe 

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] [honeymoon#1]

13 ‘Vacation’ in WordNet Sense 1 vacation, holiday => leisure, leisure time => time off => time period, period of time, period => fundamental quantity, fundamental measure => measure, quantity, amount, quantum => abstraction Sense 2 vacation => 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 =  Output: a data structure providing identification number, lemma, part of speech and linguistic function of each token  Example: Data structure for ‘Sea holidays’ Sea holidays IDTokenLemmaPoSFunction 0Seaseanounmod-1 1holidaysholidaynounhead

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 IDTokenLemmaPoSFunctionW-senses 0Seaseanounmod-1sea#1 sea#2 sea#3

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#1Europe#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#1Europe#1 – Italy#1

24 Mapping between contexts Source context Target context Vacation SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe ?

25 Contextual meanings Source context Target context Vacation SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe ?

26 Sense matrix holiday#1 holiday#2 sea#1 sea#2 sea#3 Europe#1-Italy#1 vacation#1 vacation# sea#1 sea#2 sea#3 Spain#1

27 Sense matrix holiday#1sea#1 sea#2 sea#3 Europe#1-Italy#1 vacation#1 = 2001 sea#1 sea#2 sea#3 Spain#1

28 Sense matrix holiday#1sea#1Europe#1-Italy#1 vacation#1 = 2001 sea#1 = Spain#1

29 Sense matrix holiday#1sea#1Europe#1-Italy#1 vacation#1 = 2001 sea#1 = Spain#1 

30 Sense matrix holiday#1sea#1Europe#1-Italy#1 vacation#1 = 2001 sea#1 = Spain#1 

31 Computing the matching via Sat (I): 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): iii.For each single relation in the matrix a propositional formula is generated –A i  B j  A i → B j –A i  B j  B j → A i –A i = B j  A i  B j –A i  B j  ¬(A i Λ B j ) 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) Λ Λ ¬(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 SeaLakeSeaMountains PugliaSpainUSA Sea holidays Italyin Europe 

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