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Enhancing Web Search with Descriptive Graphs

Explore how descriptive graphs improve web searching by organizing information logically, using ontologies, and measuring precision. Learn about composite antecedents, partial identity, and similarity coefficient in information retrieval.

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Enhancing Web Search with Descriptive Graphs

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  1. Improving Web Searching Using Descriptive Graphs Alain Couchot Cnam, Laboratoire Cedric, Equipe Isid

  2. The web today • Information and services for the user • Excess of information • How find the good information ? • Need of information usable by computers

  3. Semantic web • Semantic annotations • Intelligible by the computers • Need of a consensus • Communication between distant computers • Addition of a « ontology » layer

  4. Ontologies • Set of objects recognized as existing • Relationships between these objects • Two views : • Universal ontology • Ontology depending from the point of view

  5. Drawbacks of ontologies • Global ontology : • Need of a general consensus • Local ontology : • Problem of the inter-ontologies links • Problem of the choice of the « good » ontology for the user

  6. Simple ontology • Set of concepts • Irreflexive, antisymmetric and transitive relation, noted < • Universal concept

  7. Global terminology • Set of simple ontologies • If c1 and c2 belong to Oi, with c1 < c2, then if c1 and c2 belong to Oj,we have c1 < c2, or c1 and c2 are not linked

  8. Descriptive graphs • Oriented graph built with a simple ontology • A node is labelled by a concept of the simple ontology • A node has one incoming node and one outgoing node

  9. Precision of a graph • Subsumption graph • Subsomption hierarchy • Precision of a concept c • Length of the longest path in the subsumption graph from the universal concept to the concept c • Precision of a descriptive graph • The greatest precision of the concepts of the graph

  10. Example • Ontology • Piece of furniture, table, antique dealer, customer, buy, at, (implicit universal concept) • With : table < piece of furniture • Graph • customerbuytable atantique dealer • Precision of the graph • 3

  11. Average and significant precisions • Average precision of a concept • Average of the precisions of the concept for all the ontologies of the terminology • Significant precision of a graph • Average of the average precisions of the concepts of the graph

  12. Example • Ontology O1 • Piece of furniture, table, antique dealer, customer • With table < piece of furniture • Ontology O2 • table, antique dealer, customer • Average precision of « table » • (3+2)/2 = 2.5

  13. Composite antecedent • Precision k antecedent of a concept • Hypernym concept whose precision is k • It is possible to prove that there is always a precision k antecedent • Composite precision k antecedent • Conjunction of all the precision k antecedents

  14. Example • Ontology • Graduate student, student, teacher • With : graduate student < student and graduate student < teacher • Precision 2 composite antecedent of graduate student • student AND teacher

  15. Partial identity • Partial identity of two composite antecedents A and B • A = a1 AND a2 AND … AND am • B = b1 AND b2 AND … AND bn • A and B partieallly identical if there is i, j / ai = bj • Example • A =land-vehicle AND amphibian-vehicle • B = land-vehicle AND flying-vehicle

  16. View of a graph at the level k • A concept whose précision is > k is replaced by its composite precision k antecedent • Notation :V(G, k) • Two views V1 = C1C2…Cn and V2 = D1 D2…Dp are identical if n = p and if the composite concepts Ci and Di are partially identical

  17. Example • Ontology • Piece of furniture, table, antique dealer, customer, buy, at • With: table < piece of furniture • Graph G • customerbuytable atantique dealer • V(G,2) • customerbuypiece of furniture atantique dealer

  18. Similarity of two graphs • We determine k1 and k2 such as • V(G1, k1) and V(G2, k2) are identical • V(G1, k1+1) and V(G2, k2) are not identical • V(G1, k1) and V(G2, k2+1) are not identical • Similarity coefficient • (Sign_Prec(V(G1,k1))+Sign_Prec(V(G2, k2))) / (Sign_Prec(G1)+Sign_Prec(G2))

  19. Example • Ontology O1 • customer, antique dealer, buy, at, piece of furniture, table, leg, decoration, good, seller • With: leg < table < piece of furniture and piece of furniture < good and antique dealer < seller • Ontology O2 • customer, seller, bibelot, decoration, buy, at • With: bibelot < decoration

  20. Example • Graph G1 built with O1 • customerbuylegatantique dealer • V(G1,4) • customerbuytableatantique dealer • V(G1,3) • customerbuypiece of furnitureatantique dealer • V(G1,2) • customerbuydecorationatseller

  21. Example • Graph G2 built with O2 • customerbuybibelotatseller • V(G2,2) • customerbuydecorationatseller • V(G1,2) and V(G2,2) are identical • Similarity coefficient • (2 + 2) / (2.8 + 2.2) = 0.8

  22. Conclusion • Global terminology and simple ontologies • Descriptive graphs • View of a graph • Similarity coefficient • Future work • Automatic buildong of the descriptive graphs associated to the web ressources • Specifcation of the queries using the natural language

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