P2P Concept Search

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P2P Concept Search

• Fausto Giunchiglia
• Uladzimir Kharkevich
• S.R.H Noori

April 21st, 2009, Madrid, Spain
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Problems of syntactic approach

• Low precision. Caused by
• Polysemy ,word or phrase with more senses
• Java -gt Island, coffee, programming language?
• Check -gt bank check or Verification?
• Complex concepts
• Computer table -gt A laptop computer is on a
  coffee table.
• Low recall. Caused by
• Synonymy, different words with similar meanings
• Student and Pupil
• Related concepts
• Color -gt Red , Blue
• Car -gt Volvo, FIAT, BMW

Polysemy
Synonymy
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Scalability problem

• Current web is a huge repository of documents
• Number of documents keeps growing significantly
• Making difficult to locate relevant documents
• Web is a highly dynamic system
• Peers are continually joining and leaving the
  network
• All these makes the search problem complex.

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Concept Search
Fausto Giunchiglia, Uladzimir Kharkevich, and
Ilya Zaihrayeu. Concept search. In Proc. of
ESWC'09, Lecture Notes in Computer Science.
Springer, 2009.

• Goal To extend syntactic search (address
  problems)
• address the ambiguity problem of NL
• make uses of related complex concepts
• should not be worse than syntactic search!!!
• IR_System ltModel, Data_Structure, Term, Matchgt
• Moving from Syntactic IR to C-Search does not
  require the introduction of new data structures
  or retrieval models
• CSearch reuses retrieval models and data
  structures of syntactic search
• words (W) are substituted with complex concepts
  (C)
• syntactic matching WMatch is substituted with
  semantic matching
• When no semantic information is available,
  CSearch reduces to syntactic search

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Words To Complex Concepts

• Extract phrases
• Descriptive phrase
• E.g., A little dog or a huge cat
• Convert NL phrases to Complex formulas
• Complex concepts are computed by analyzing
  meaning of the words and phrases.
• Expressed in a propositional Description Logic
  (DL)
• E.g., (little-4 ? dog-1) ? (huge-1 ? cat-1)
• Lack of background knowledge
• Sometimes it is not possible to find a concept
  for a word.
• gt word is used as the identifier for a concept

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Syntactic matching to semantic matching

• Query answer
• E.g., A(big-1 ? animal-1, T) D1 (huge-1 ?
  white-1 ? elephent-1)
• CSearch uses the following three methods to
  access the background knowledge T , stored on a
  single peer
• getConcepts(W) - returns a set of all the
  possible meanings (atomic concepts A) for word W.
• getChildren(A) - returns a set of all the more
  specific atomic concepts of the given atomic
  concept A in T .
• getParents(A) - returns a set of all the more
  general atomic concepts of the atomic concept A
  in T .

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P2P CSearch

• Main idea is to extending CSearch to address the
  scalability
• problem
• Reasoning extended single BK T to the
  distributed BK TP2P
• Centralized inverted index to distributed index
  build on top of DHT.

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Distributed Background Knowledge(DBK)

• Atomic concepts are indexed by words using the
  DHT 'put' operation
• e.g., put(canine, canine-1, canine-2).
• Every atomic concept is indexed by related atomic
  concepts their relations.
• DHT 'put' operation is modified to put(A, B,
  Rel),
• e.g., put(canine-2, dog-1, ' ? '), put(canine-2,
  carnivore-1, ' ? ').
• Getting data from DBK
• getConcepts(W) , getChildren(A) and getParents(A)
  are implemented by using are implemented by using
  the DHT 'get' operation
• we modified DHT 'get operation get(A, Rel)
• e.g., getChildren(A) get(W, ? ') ,
  getParents(A) get(W, ? ').

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Indexing

• Indexing of documents is performed as follows.
• Every peer computes a set of atomic concepts A
  which appear in the representations of peer's
  documents.
• For every atomic concept A, the peer computes a
  set of documents d which contain A.
• For every pair ltA, dgt the peer computes a set
  S(d, A) of all the document complex concepts Cd
  in d, which contain A.
• For every A, the peer sends document summaries
  corresponding to A, i.e., pairs ltd, S(dA)gt, to a
  peer pA responsible for A in DBK.
• The peer pA indexes these summaries using the
  local CSearch.

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Retriveval

• Step 1 A peer pI initiates the query process for
  query concept Cq and initialize the query answer
  QA.
• Step 2 For every conjunctive component ? Aq in
  Cq, pI selects concept A in ? Aq with the
  smallest number of more specific atomic concepts.
  For every selected A, Cq is propagated to the
  peer pA responsible for A.
• Step 3 pA receives the Cq and locally computes a
  set of documents which belong to the query
  answer. The results are sent directly to pI . On
  receiving new results, pI merges them with QA.
• Step 4 pA computes a set Cms of all more
  specific atomic concepts B which are directly
  connected to the given atomic concept A in TP2P .
  Cms is computed by querying locally stored more
  specific concepts.
• Step 5 pA propagates Cq to all the peers pB
  responsible for concepts B in Cms, i.e., Step 2
  is repeated on all pB.

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Example query answering
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Conclusion Future work

• P2P CSerarch addresses the scalability problem of
  CSerarch and the ambiguity problem of natural
  language in P2P syntactic search.
• Future work includes
• Development of techniques which can control the
  quality of a user input and in general to control
  the quality of DBK
• Development of document relevance metrics based
  on both syntactic and semantic similarity of
  query and document descriptions
• Evaluating the efficiency of the proposed
  solution.

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• Thank You!

• To read more
• Fausto Giunchiglia, Uladzimir Kharkevich, and
  Ilya Zaihrayeu. Concept Search. In Proc. of
  ESWC'09.
• Fausto Giunchiglia, Uladzimir Kharkevich, S.R.H
  Noori P2P Concept Search. Poster
  at SemSearch 2009 workshop.