Concept Search: Semantics Enabled Syntactic Search

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Fausto Giunchiglia, Uladzimir Kharkevich, Ilya
Zaihrayeu
Concept Search Semantics EnabledSyntactic Search
June 2nd, 2008, Tenerife, Spain
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Outline

• Information Retrieval (IR)
• Syntactic IR
• Problems of Syntactic IR
• Semantic Continuum
• Concept Search (C-Search)
• C-Search via Inverted Indices
• Preliminary Evaluation
• Conclusion and Future work

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Information Retrieval (IR)

• IR can be represented as a mapping function
• IR Q ? D
• Q - natural language queries which specify user
  information needs
• D - a set of documents in the document
  collection, which meet these
• needs, (optionally) ordered according to
  the degree of relevance.
• Ex. document collection
• Ex. queries

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Information Retrieval System

• IR_System ltModel, Data_Structure, Term,
  Matchgt
• Model IR models used for document and query
  representations, for computing query answers and
  relevance ranking.
• Bag of words model (representation)
• Boolean Model, Vector Space Model, Probabilistic
  Model (retrieval)
• Data_Structure data structures used for
  indexing and retrieval.
• Inverted Index
• Signature File
• Term an atomic element in document and query
  representations.
• a word or multi-words phrase
• Match matching technique used for term
  matching.
• a syntactic matching of words or phrases
• search for equivalent words
• search for words with common prefixes
• search for words within a certain edit distance
  with a given word

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Syntactic IR (Ex. Inv. Index)
Q3
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Problems of Syntactic IR

• (I) Ambiguity of Natural Language
• Polysemy one word ? multiple meanings
• e.g., baby is a young
  mammal or a human child
• Synonymy different words ? same meaning
• e.g., mark and print a visible indication
  made on a surface
• (II) Complex Concepts
• Syntactic IR does not take into account complex
  concepts formed by Natural Language Phrases
  (e.g., Noun Phrases).
• E.g., Computer table ? A laptop computer is on a
  coffee table
• (III) Related Concepts
• Syntactic IR does not take into account related
  concepts
• E.g., carnivores (flesh-eating mammals) is more
  general than
• dog OR cat

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Syntactic IR

• We can think of Syntactic IR as a point in a
  space of IR approaches

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(1) Ambiguity Natural Language ? Formal
Language

• E.g., baby ? C(baby) a human child
• print ? C(print) a visible indication
  made on a surface

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(2) Complex Concepts Words ? Multi-word
Phrases

• E.g., Computer table ? C (computer table)
• A laptop computer is on a coffee table ?
  
• C (laptop computer), C (coffee
  table)

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(3) Related Concepts String similarity ?
Knowledge

• E.g., carnivores ? dog ? C(carnivores) ?
  C(dog)

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Semantic Continuum

• C-Search

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C-Search in Semantic Continuum

• NL2FL-axis - Lack of background knowledge
• It is not always possible to find a concept which
  corresponds to a given word (e.g., a concept does
  not exist in the lexical database).
• In this case, word itself is used as the
  identifier for a concept.
• W2P-axis - Descriptive phrases
• (Complex) concepts are extracted from descriptive
  phrases
• descriptive phrase noun phrase OR noun
  phrase
• E.g., C(A little dog OR a huge cat) (little-2 ?
  dog-1) ? (huge-1 ? cat-3)
• KNOW-axis - lexical knowledge
• We use synonyms, hyponyms, hypernyms
• Semantic Matching ? search for related complex
  concepts.

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C-Search in Semantic Continuum
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C-Search via Inverted Indices

• Moving from Syntactic IR to C-Search does not
  require the introduction of new data structures
  or retrieval models
• The current implementation of C-Search
• Model Bag of concepts (representation),
• Boolean Model (retrieval),
• Vector Space Model (ranking)
• Data_Structure Inverted Index
• Term an atomic or a complex concept
• Match semantic matching of concepts

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C-Search (Ex. Inv. Index)
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Concept Matching

• Goal To find a set of document concepts matching
  query concept
• 1st approach - directly via S-Match
• Sequentially iterate through all document
  concepts
• Compare document concept with query concept
  (using S-Match)
• Collect those concepts for which S-Match return
  more specific (?)
• It can be slow! (because number of document
  concepts gt 10E6)
• 2nd approach - via Inverted Indices (brief
  overview)
• A-Index
• ? Index atomic concepts by more general atomic
  concept
• ?-Index
• ? Index conjunctive clauses by its components
  (i.e., atomic concepts)
• ? -Index
• ? Index DNF formulas by its components (i.e.,
  conjunctive clauses)

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Concept Indices (An example)

• Let us consider the following concept
• C1 (little-2 ? dog-1) ? (huge-1 ? cat-3)
• Fragments of concept indices for document concept
  C1

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Concept Retrieval (An example)

• 0. Query concept Cq canine ? feline
• 1. For each atomic concept ? more specific atomic
  concepts
• Search A-Index
• E.g., canine ? dog, wolf, and feline ? cat,
  lion,
• 2. For each atomic concept ? more specific
  conjunctive clauses
• Search ?-Index
• E.g., dog ? C2 little ? dog, and cat ? C3
  huge ? cat,
• (Note that canine ? C2 little ? dog, and
  feline ? C3 huge ? cat, )
• 3. For each disjunctive clause ? more specific
  conjunctive clauses
• Union of conjunctive clauses
• E.g., canine ? feline ? C2 little ? dog, C3
  huge ? cat,
• 4. For each disjunctive clause ? more specific
  DNF formulas
• Search ? -Index
• E.g., canine ? feline ? C1 (little ? dog) ?
  (huge ? cat),
• 5.

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Evaluation Settings

• Data_set_1 Home sub-tree of DMoz web directory
• Document set documents classified to nodes
  (29506)
• Query set concatenation of node's and its
  parent's labels (890)
• Relevance judgment node-document links
• Data_set_2 Only difference with Data_set_1 is
• Document set concatenation of titles and
  descriptions of docs in DMoz.
• WordNet is used as Lexical DB
• GATE is used as NLP Tool
• Lucene is used as Inverted Index

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Evaluation results

• Data_set_1
• Data_set_2

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Conclusion and Future work

• Conclusion
• In C-Search, syntactic IR is extended with a
  semantics layer
• C-Search performs as good as syntactic search
  while allowing for
• an improvement when semantics is available
• In principle, C-Search supports a continuum from
  purely syntactic IR to fully semantic IR in which
  indexing and retrieval can be performed at any
  point of the continuum depending on how much
  semantics is available
• Future work
• Development of more accurate concept extraction
  algorithm
• Development of document relevance metrics based
  on both syntactic and semantic similarities of
  query and document descriptions
• Allow semantic scope (such as equivalence,
  more/less general, disjoint)
• Comparing the performance of the proposed
  solution with the state-of-the-art syntactic IR
  systems using a syntactic IR benchmark

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• Thank You!