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Improving Web Searching Using Descriptive Graphs

Alain CouchotCnam,

Laboratoire Cedric,Equipe Isid

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The web today

Information and services for the user

Excess of information How find the good information ?

Need of information usable by computers

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Semantic web

Semantic annotations Intelligible by the computers

Need of a consensus Communication between distant

computers Addition of a « ontology » layer

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Ontologies Set of objects recognized as

existing Relationships between these

objects Two views :

Universal ontology Ontology depending from the point of

view

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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

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Simple ontology

Set of concepts Irreflexive, antisymmetric and

transitive relation, noted < Universal concept

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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

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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

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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

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Example Ontology

Piece of furniture, table, antique dealer, customer, buy, at, (implicit universal concept)

With : table < piece of furniture Graph

customerbuytable atantique dealer Precision of the graph

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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

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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

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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

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Example Ontology

Graduate student, student, teacher With : graduate student < student

and graduate student < teacher Precision 2 composite antecedent

of graduate student student AND teacher

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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

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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 = C1C2…Cn and

V2 = D1 D2…Dp are identical if n = p and if the composite concepts Ci and Di are partially identical

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Example Ontology

Piece of furniture, table, antique dealer, customer, buy, at

With: table < piece of furniture Graph G

customerbuytable atantique dealer V(G,2)

customerbuypiece of furniture atantique dealer

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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))

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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

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Example Graph G1 built with O1

customerbuylegatantique dealer V(G1,4)

customerbuytableatantique dealer V(G1,3)

customerbuypiece of furnitureatantique dealer

V(G1,2) customerbuydecorationatseller

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Example Graph G2 built with O2

customerbuybibelotatseller V(G2,2)

customerbuydecorationatseller V(G1,2) and V(G2,2) are identical Similarity coefficient

(2 + 2) / (2.8 + 2.2) = 0.8

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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