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Using NLP Techniques for Meaning Negotiation
Bernardo Magnini, Luciano Serafini and Manuela Speranza
ITC-irst, via Sommarive 18, I-38050 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
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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
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Examples of contexts
Context A Context B
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in Europe
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Examples of contexts
Context A Context B
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in Europe
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Mapping between contexts
Source context Target context
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in Europe
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Mapping between contexts
Source context Target context
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in Europe
?
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Mapping between contexts
Source context Target context
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in 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)
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Our proposal
Use of a lexical database (WordNet)
Creation of specific rules for sense disambiguation
Interpretation of hierarchical relations as syntactic dependency relations
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WordNet senses and concepts: the word ‘vacation’
[vacation#2][leisure#1, leisure time#1]
ISA
ISA
[vacation#1, holiday#1]
[honeymoon#1]
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‘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
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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)
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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
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Matching algorithm (II)
Single labels’ analysis (linguistic and semantic)
Sense refinement rules
Sense matching
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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’
Sea holidays
ID Token Lemma PoS Function
0 Sea sea noun mod-1
1 holidays holiday noun head
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Labels’ semantic analysis
Use of WordNet as a repository of sensesE.g. ‘sea’ has three senses:– sea#1: ‘a division of an ocean’– sea#2: ‘anything apparently limitless’– sea#3: ‘turbulent water’
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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
ID Token Lemma PoS Function W-senses
0 Sea sea noun mod-1 sea#1sea#2sea#3
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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)
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Sense refinement (I)
Aim: Elimination of the w-senses that are in disagreement with other w-senses
tree
apple#1 (a fruit)
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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
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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
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Mapping between contexts
Source context Target context
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in Europe
?
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Contextual meanings
Source context Target context
Vacation
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in Europe
?
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Sense matrix
holiday#1holiday#2
sea#1sea#2sea#3
Europe#1 -Italy#1
vacation#1vacation#2
2001
sea#1sea#2sea#3
Spain#1
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Sense matrix
holiday#1 sea#1sea#2sea#3
Europe#1 -Italy#1
vacation#1 =
2001
sea#1sea#2sea#3
Spain#1
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Sense matrix
holiday#1 sea#1 Europe#1 -Italy#1
vacation#1 =
2001
sea#1 =
Spain#1
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Sense matrix
holiday#1 sea#1 Europe#1 -Italy#1
vacation#1 =
2001
sea#1 =
Spain#1
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Sense matrix
holiday#1 sea#1 Europe#1 -Italy#1
vacation#1 =
2001
sea#1 =
Spain#1
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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
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Computing the matching via Sat (II):
iii.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 ¬(Ai Λ Bj)
E.g. Spain → Europe holiday vacation
¬(Italy Λ Spain)
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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 intersectionsE.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
2001 2000
Sea LakeSeaMountains
Puglia Spain USA
Sea holidays
Italy in 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