8/7/2019 UBICC-ID365_365

1/9

SEMANTIC APPROACH BASED MULTI-AGENT SYSTEMFOR INFORMATION SEARCH ON THE WEB

Nessah DjamelUniversity Center of Khenchela, Algeria

Kazar OkbaBiskra University, Algeria

ABSTRACTCurrentely most search systems developed for information retrieval are based onvector representations as the space vector model, others use various statistical and / or probabilistic approach. Generally, for a given user request, the documentsretrieved are not relevant, because the precision measurement is partial (presenceof noise), and furthermore other relevant documents will never be found, this

difficulty is an effect of a low recall measurement (presence of silence).Our work is to propose a model whose objective is to improve the results towardsa user query, this will be done by acting on measures of precision and recall, forthis, first we use a multi agents system to reproduce the concepts of autonomy,cooperation and communication, which are inherent to this type of search systems,and secondly our approach will combine a syntactic search improved by the use of semantics that provides the WordNet taxonomy with a semantic search enginebased domain ontology. The knowledge base (domain ontology) is used toannotate documents by the concepts and the defined instances, therefore theseform a set of semantic index classes on which our search model is based.

Keywords: Semantic Web, Multi-agent System, Semantic Search, Ontology,Semantic index, WordNet Taxonomy.

1 INTRODUCTION

Today the web becomes an immense source of heterogeneous information that requires theimplementation of powerful tools for retrievinginformation quickly and to discover new knowledge.

Traditional techniques applied in many searchinformation models such as the vector space modelsuffer of the handicap of not being able to representthe semantic of documents contents, so that theindexing of a document produced at the end is a bag

of independent terms without any semanticrelationship between themThe semantic information search is a complex

process, it has several stages, for example we havesemantic annotation, query processing, and stage of evaluation and classification of results. Thecomplexity comes from the nature of informationresources of semantic web that is not restricted tomultimedia objects; also other objects may bepeople, places, events and others exist.

Then, the semantic web doesnt work only withthe known hyperlink relationship; there are severalother types of relationships that link its different

resources. We will introduce in the following an

architecture based agents, we will deal mainly in ourapproach with the problem of how to develop andconduct a semantic search engine based ontology andusing paradigm agent techniques. Nevertheless,taking into account the lack and the use of incompletes knowledge bases which limits theexclusive use of models based ontology; we chose toretain the use of keyword based search to completethis lack while making some improvements based onthe semantics offered by WordNet taxonomy.

Section two describes some related works for

semantic search, next section defines elements usedfor representing domain knowledge and performingsemantic annotation process, in section four we'llpresent the different multi-agents system componentsand describe their interactions, the last sectionincludes a conclusion and some prospects for futureimprovements.

2 RELATED WORKS

In order to improve quality of models developedfor information search, many efforts have beendeployed to annotate documents with semantic

information. The related works to our approach

8/7/2019 UBICC-ID365_365

2/9

concerns: Ontology based information search, which usesreasoning mechanism and ontological querylanguages to retrieve ontology instances (semanticlayer) that annotate documents in the search domain. query expansion based information search,

generally used with vector space models drivenapproaches.

Several work exist, in [6] is described anarchitecture based agents and ontology whichintroduces the interest of restricting research on theweb and the advantage of using software agents withontology. The architecture is detailed in severallevels of sub systems layers.

Another semantic search system based agents isdescribed in [7], it uses the concept of conceptualgraph closely related to natural language, this type of knowledges representation provides the ability toextract useful information by exploiting the logical

relationships in the form of triplets (Relation,concept1, Concept2) between the terms in thedocuments collection.

In [8] we have a multi-agent system whichperforms an intelligent search; it is based on asemantic approach and a process of enrichment of the ontological concepts by probabilistic notions.The semantic approach is based semantic network associated to domain knowledge; in the graph, edgeshave weights that express the strength of semanticrelationship which the edge carries between thenodes (concepts).

Another interesting project developed at the

university George Mason [9], is interested insearching information on heterogeneous databases(web), the research is guided by ontology and basedmulti agent system, this project uses a modularconceptual model expressed in OWL, and allowingthe integration of other ontologies and otherinformation resources.

3 KNOWLEDGE REPRESENTATION

The system architecture includes the followingcomponents used for handling and representing thedomain knowledge:- WordNet Taxonomy- Domain Ontology (Knowledge Base Annotation)- Semantic Annotation

3.1 WordNet TaxonomyWordNet is a lexical reference developed at

Princeton university offering two separate services:- A vocabulary describing the different meanings of words.- A concept hierarchy describing the semanticrelationships between words. Bringing together theterms of natural language (English), about 160,000terms are organized in hierarchical taxonomies of names, verbs, adjectives and adverbs.

Each entry in WordNet is called "synset"; it is aset of synonyms with the same meaning. Forexample, the words "car","auto" and "automobile"are parts of the same synset, moreover, the sameword can have several meanings, for the word "car"we find five possible senses. [1]

The synsets are connected at the top or bottom of the hierarchy by different types of relationships,most relationships are hypernym / hyponym i.e. "Is-a" relationship and holonym / meronym relationships"Part-of", in our approach we use only hypernym/ hyponym relations and representations of names thatare commonly claimed to be the most representativeform for the semantics of a language, these namesare extracted from documents and queries. Betweenthe words, several methods for calculating semanticsimilarity were tested on the taxonomy WordNet, wefind essentially two categories:

Methods based ontology structure. Methods based information content of oncepts.

An important property which we tried to takeadvantage of in our approach is that, most methodsof calculating semantic similarity try to assign highersimilarity to terms which are close together (in termsof path length) and lower in the hierarchy (morespecific terms), than to terms which are equally closetogether but higher in the hierarchy (more generalterms).

3.2 Domain Ontology (Knowledge Base)In computer science the ontology is a formal

declaration that associates the names of entities in

the universe of discourse (classes, relations andfunctions) with documents content understandableby human and describing their meanings. Thisformalism also defines a set of axioms that constrainthe interpretation of these terms

The concept of ontology has long existed,especially in philosophy, several definitions of ontology have been made, the most used is the onegiven by Gruber An ontology is an explicit specification of a conceptualization .

The fundamental objective of the semantic webis to extend current interfaces oriented to humanunderstanding in a format automatically interpretableby programs, this requires developing a rich and astandard scheme of knowledge representation, thisschema is named "domain ontology".

An ontology as instrument for buildingknowledge bases provides a controlled vocabulary toformulate queries, representing knowledge(concepts, relationships and functions), classify thecontent of the documents, and make expansions of requests based on class hierarchy and rules onrelationships. [2]

The ontology must be expressed in languageenough expressive and carrying out reasoningmechanism, this understandable representation of theknowledge will allow software agents ability to find

8/7/2019 UBICC-ID365_365

3/9

and handle domain entities.

3.3 Semantic Annotation Process The goal is to build a semantic index

according to domain ontology. The index processcan be divided into four steps : [10]

1. Extraction of the index terms, a flat index isbuilt, and then each term of this index isassociated with its weight calculated by atechnique such as TF-IDF algorithm.

2. We use the WordNet hierarchy foridentifying all the candidate concepts.

3. Selecting candidate concept, this analysisconcerns its representation's degree for theweb pages contents; it is based on thefrequency of occurrence of the label'sconcept and the relationships which thecandidate concept maintains with the otherconcepts.

4. Among these selected candidate concepts,we consider those that are specified in theontology, i.e. that best represent thesemantic content of the pages, then thoseconcepts will form the terms of oursemantic index. (Fig. 1)

Figure 1 : The indexing process

The semantic annotation allows agents who usea semantic search engine to decide intelligently aboutthe relevance of the returned results, for thesereasons the process of retrieving informationdepends largely on the quality of formal semanticannotations defined by domain ontology.

Documents in the domain of interest areannotated by concepts and instances of concepts, thisannotation has two relational properties that areinstances annotations and documents annotated andthrough which the concepts and documents arelinked. So, terms (class, concept, data type, objectproperty, data type property) defined in the ontologyare used as metadata to annotate the content of the

documents, these terms form the semantic index,they are identified by URIs.

In our work we opted for OWL Lite as standardontology specification because this languagemaintains a compromise between expressiveness toformulate domain knowledge and ensure reasoning

decidability (e.g. Jena ).3.3.1 Equivalent annotation classAnnotation process and related techniques used

such as the technique of generating a set of equivalents annotation classes are not part of theobjective of this work, we assume these classes havebeen generated by using an appropriate inferencemechanism applied to the knowledge base thatspecifies the domain ontology; nevertheless, we givehere an overview to clarify this concept.

Let Ti: a term of the semantic index, and [Ti] itsequivalent class, so we can have Ti1 [Ti], Ti2 [Ti] (Fig.2)

We consider document dj and A, B, C threestrings in document dj semantically annotated byTi1, Ti1, Ti2. From the semantic point of view thesethree strings are equal even with their differentsyntax because A, B and C are semanticallyannotated with the same semantic index term Ti.

Figure 2 : Generating equivalent annotation classes

3.3.2 Weight mappingWeights of the annotations are used to evaluate

the relevance, the appropriateness and to implementa classification algorithm (ranking) of retrieved andrelevant documents.

These annotations weights reflect the relevanceof an instance for the semantic of the documentwhere it appears, our model is based on thefrequency of occurrence of annotation instances ineach document and takes into account the principleof generating an equivalent annotation classdescribed above, so the adaptation of the Tf-Idf algorithm will consider the number of times anannotation's label within an equivalent class appears

8/7/2019 UBICC-ID365_365

4/9

in a document, the formula is:

(1)

d x : weight of the instance "x" in document "d"

freq x,d : number of occurrences in "d" of keywordslinked with instance "x"max y freq y,d : The frequency of occurrence of themost repeated instance in the document "d"nx : The number of documents annotated by "x"D : Total number of documents.

Based on works presented in [3], [4] and tosimplify the calculations well only retain theimportance of an instance in the document:

(2)

Let D = (d1, d2 ... dn) the collection of documents in the search space.([T1], [T2] ... [Tt]) the equivalents classes of thesemantic index terms. freq xi,d : The total frequency of all elements of theequivalent class [Ti], appearing in all the semanticannotations of document "d".

d yi y freq ,max : Max (freq T1,d ,freq T2,d freq Tt,d )

Each document in space D will have a logicalview relative to the weight of its annotationinstances. So a document "dj" will be represented bythe vector weights, and we write:

(3)

Similarly, for a given request, we get its logicalview to the whole equivalents classes ([T1], [T2] ...

(4)

4 SYSTEMS AGENTS

The main advantage of using paradigm agent isto perfect the applications related to informationresearch. In particular, the objective is to design anddevelop a multi-agent system which provide: [11]

1. Respond to user queries through a set of URLs2. Automatically and simultaneously browsemultiple web sites to find concepts of interest.3. Monitor changes on selected web sites.4. Identify and extract useful information.

The architecture that we propose as shown inFig.3 is composed of three units:

User Interface Unit Management Query Unit Research Information Unit

Agent "User-Interface" is considered to be thedoor through which the query is entered in the

system, it receives and transmits to the system theuser feedback and presents him the search results, theagent "Information-Research" collects in the area of interest relevant information resources; it may dealwith several other subcontracts agents to accomplishthis goal, while the "Domain-Ontology" agent

inspects and monitors the dynamic changes ininformation resources contents, it extracts and storesin an RDF base document's links that are annotatedby concepts and instances of the specified ontology.In the management query unit (processing) issituated the "Query-Treatment" agent whichcoordinates the activities of the system, it formulatesand refines (prepare) the query to be submitted to theagent "Information-Research".

Figure 3 : System's architecture

Figure 4 : Interaction in multi-agents system-AUML: Sequence diagram-

xd

d x x n

D

freq

freqd log*

max,

=

d yi y

d xi xi freq

freqw

,

,

max=

),...,,...,( 21 tjij j j wwwwdj

),...,,...,( 21 tqiqqq wwwwq

8/7/2019 UBICC-ID365_365

5/9

Finally returned results will be analyzed andevaluated by the "Evaluate-Ranking" agent that todetermine their degree of relevance and decide toaccept or reject them.

Systems architecture including agents is givenin Fig.3; the internal structure of each agent who

composes the system will be detailed below.All agents interact by sending messages inFIPA-ACL protocols formatting, as showed inFig.4 we have large type of information exchangedbetween agents throughout their communications.

4.1 User-Interface AgentInterface agent resides on the desktop user; it

provides the interface to interact with the system. Fora search session it records the user request in termsof keywords.

Possibly the user can define its Search domainand introduce various user preferences such as the

favorite search engine (default Google), and a set of variables defining thresholds calculations. Also thisagent presents the user the search results when theyarrive, it can implement an intelligent behavior andlearn from past experiences and user feedback onearlier requests.

4.2 Query-Treatment AgentIn our multi-agents system, this agent manages

the cooperative execution of the user request; it hasknowledge about each agent which includes theidentification and roles that the agent can perform inits capabilities order. (Fig.5) According to their

various skills it allocates them tasks to achieve theircommon goal. Through interactions that the agentmaintains with the "Evaluate-Ranking" agent itperforms various substitutions involving:

Figure 5 : Query-Treatment agent structure

The weight of keywords: the weights of keywords are replaced by values calculated by aheuristic evaluation of similarity; these values are

provided by the agent "Evaluate-Ranking". In otherwords, the keyword Mi with weight Wi isassigned a new weight Wsi calculated by theexpression:

The keywords by Hyponym / hypernym:This task aims to assist the user to reformulate itsrequest by offering him choices, i.e. the synsetsexcerpts from the WordNet hierarchy.

Another module that complements the first oneprepares the same query based concepts; it is asemantic search which uses relationships betweenconcepts as follow:

An RDQL query will be generated from thekeywords expressed in the original request, also thismay be done by the "User Interface" agent who in

this case reaches the domain ontology and help theuser to explicitly select classes and introduce thedesired values of properties.

The "Query-Treatment" agent interacts with theagent "Domain-Ontology" to run on the pattern of domain ontology and instances of concepts specifiedin OWL the RDQL query, the result is a set of instances that strictly satisfy conditions of the RDQLquery. (Standard engine such as "Jena" is used toexecute RDQL queries). The execution is aninstantiation operation of the concepts of theontologys scheme OWL by values of variables usedin the constructed query and the invocation of reason

such as Jena to infer the related knowledge.

4.3 Information-Search AgentThe first research component of this agent is

based syntactic keywords and targets the area of research (e.g. the web) through a traditional searchengine; however, to improve research results purelysyntactic we introduce a second component whichperforms semantic search.Both modules operate simultaneously, each onereceives input model adapted to query search modeprepared by the agent "Query-Treatment" (keywordsto perform syntactic-semantic search and generated

instances of concepts derived from the execution of RDQL query to perform a semantic search).The agent can contract several other agents tocomplete the research, choosing an agent for suchresearch can depend on the agent capability and thenature of the information sought. (Fig.6)4.3.1 Semantic-Syntactic search

Uses a syntactic search engine such as Google tofind in the area of interest documents that satisfy thesubmitted query. That is a search of purely syntacticcorrespondence between the keywords in the queryand terms indexing the documents available in spaceresearch.

Wsi=Sim* Wi. 0

8/7/2019 UBICC-ID365_365

6/9

Figure 6 : Information-Search agent structure

4.3.2 Semantic searchThis module research in the RDF documents

base, the RDF annotations that match tuplesinstances recovered by the "Query-Treatment" agent.The module receives input instances which are theresults of the RDQL query, then, documents whoselinks have been stored in the RDF database areanalyzed and those annotated by these tuplesinstances are found, they are considered semanticallyrelevant. Then the agent records in a temporary filethe following details: Links of resources found andtheir evaluated similarities.

4.4

Evaluate-Ranking AgentThe "Information-Search" agent stores links of resources found in a temporary file to which the"Evaluate-Ranking" agent accesses, so it is a type of memory that can be modeled by a blackboard. Foreach entry, the "Evaluate-Ranking" agent downloadpage referenced by the link. Furthermore Keywordsthat syntactically index the page or semantic indexinstances are assigned weights according to theprinciple of vector model.4.4.1 Evaluate module

Let Wij: weight of term "i" (keywords) in page j.

(6)

n: the number of keywords User's query is also represented by the weight

vector Q = (w1q, w2q, wiq ,..., wnq), where wiq isthe weight of the keyword i in query Q, a keywordmay be a keyword's synonym, its hyponym orhypernym. (Fig.7)

The semantic annotation is based ontology, theontology defines the concept's terms used asmetadata to form the semantic index, and thus, these

terms are identified by URIs and may be equivalent

classes. By analogy with the space vector model,semantic annotations are assigned weights reflectingthe importance of the annotation instance for thedocument, therefore in RDQL queries; the variablesin the SELECT clauses are assigned weightsaccording to the principle of vector model.

The formula of cosine is used to calculate thesimilarity document-query, so for a page "j" and arequest "q" we used the expression:

(7)

When the similarity is evaluated, it is comparedto a minimum threshold indicated by user variableRmin initially fixed.

qP jr

r

, : weight vectors associated to Pj, q.

|p j|,|q| : respectively P j and q vectors normsWhen > =Rmin, the page Pj is

considered relevant, its link and the similarity's valueare returned to agent "Information-Search" for finalstorage, in the case

8/7/2019 UBICC-ID365_365

7/9

4.4.2 Calculation of similaritiesWe based our approach on user feedback in the

choice of terms that will be used to extend the query,in (Fig.8) we give a formula to review similaritiesand update the weight of keywords. This formula isimproved from those presented in [5], our choice to

use these forms is justified by opportunity forconsidering the structure of the ontology through twoparameters: The length of the path linking concepts C a and C b The depth of concepts C a, C b in the hierarchy

Figure 8 : Forms of calculating similarities

D: depth hierarchy; n: minimum path length betweenconcepts Ca and Cb (number of arcs).

Da, Db : concepts depths.Terms of synonymous synsets are assumed to

have a similarity of "1" i.e. they are identical.For hyponyms / hypernym synsets and

considering the user choice, a similarity value is sentwith the term to "Query-Treatment" agent torecompose query and restart a new syntactic search.),the process is repeated until all sets constructedwould be entirely explored. 4.4.3 Ranking results

It is desirable to present to the user the obtained

results ordered by their relevance measure. Theagent accesses the temporary database of relevantdocuments, for each one it estimate its similaritywith submitted query. The final similarity iscalculated by an expression of type:

(9)

Documents returned having a high similarity arethose with: a # 0 and b # 0.

4.5 Domain-Ontology AgentAttached to the domain ontology, the main goal

of this agent is to maintain the ontology closely and

according to dynamic web changes (i.e. on the fly).Moreover the agent uses a standard engine (e.g. Jenaand racer) to infer knowledges formalized in theontology and executes the RDQL query, then results(instances) obtained would be communicated to the"Query-Treatment agent.

The other task of the agent is to browse the web (areaof interest) at regular intervals to detect documentsannotated RDF consistent with the specified domainontology, the document's links found will be storedin the database documents annotated RDF. Agentcan therefore take into account the dynamics of information on the web in independently andproactively manner.

5 CASE STUDY

We chose to experiment and implement ourmodel the tourism domain where tourist sites on the

web are annotated by their owners by RDF triplesand instances defined in the domain ontology used.Tourism activity has several domains (transport,

entertainment, sports, scientific conferences, etc.),but to simplify the analysis, we will limit study tohotel domain for which we associate a domainontology named hotel.

5.1 Ontology UML SpecificationThe ontology ("hotel") is specified in the

language OWL-Lite; this ontology is associated witha UML diagram specifying the classes (concepts),the properties and relationships between concepts

and some instances of concepts. (Fig. 9)

Figure 9 : UML diagram.Oontology hotel

An inference engine applied to the ontologyschema and defined instances, will infer knowledgeother than those explicitly declared, inference is amechanism that is based on the expressiveness of thelanguage (OWL-Lite) and its formal semantics basedon description logics, especially this concerns

Cb,Ca Hhypernyms

(8)

Cb,Ca Hyponyms

1 Cb,Ca Synonyms0 Cb ,Ca Not linked

S i m

( C a ,

C b )

),(*

log

2

1

ba D D Max

n D

Sim F=a*Sim syn+b*sim sem ; a [0,1] ; b=1-a

1),.(*

log

2

1

+ba D D Minn D

8/7/2019 UBICC-ID365_365

8/9

restrictions on classes, on the properties amongclasses and a set of defined axioms on classes, forexample , we specify that a 5 star rated hotel musthave as service guided-visits by the class: guided-visits=((hotel) (> = 5 rated.star)).

5.2 Inference ModelsThe integration of the Jena API in our modelwill allow it deriving additional RDF assertionsincluded in the OWL knowledge base; thismechanism supports the languages RDF /RDFS andOWL and uses an inference model which has twocomponents:

The schema of the model The instances of the modelThe example below is an illustration of an

inference model used by inference engine RDFS.Inference is performed by the transitive relation onproperties which defines 'room service as a sub

property of the property hotel service. 5.2.1 Models schema

]>

5.2.2 Models instances

]>

3

The execution of code associated with thismodel produced the following results:Type: Chelia is

http://mydomain/ontology/infohotel/chelia rdf:typehttp://mydomain/ontology/infohotel/ hotel

Type: Chelia ishttp://mydomain/ontology/infohotel/chelia rdf:typehttp://mydomain/ontology/infohotel/ serviceh

6 CONCLUSION

The proposed semantic research model basedmulti-agent system and using domain ontologyillustrate the concept of cooperative resolution of distributed problems, the process combines a searchengine based ontology with a traditional search-based keyword which include relations of synonymyand hyponymy provided by the WordNet taxonomy.

The semantic search uses as support an RDQLquery generated from query keywords, then aninference engine such as "Jena" will use the ontologyscheme to retrieve defined instances incorrespondence with keywords in the query, theseinstances will be sought in the RDF database andreturn the documents that they annotate.

As prospects for research in this area and inrelation with our model, we propose to enrich the

knowledge base agents with techniques forformulation query including explicit rules and policydecision, this will allow the "Query-Treatment"agent to optimize the request in an intelligent way, itis true that over the query is well-defined, betterrelevant results are obtained.

Also, to take advantage of new technologiesapplied to artificial intelligence systems, we intend tocouple the agent "Query-Treatment" with a system of reasoning from cases (CBR), this will enable andperfect the search process by reasoning from casesalready resolved and stored in the CBR data base.

7

REFERENCES[1] G. Varelas, E. Voutsakis, P. RaftopoulouSemantic similarity methods in WordNet andtheir application to information retrieval on theWeb, WIDM05, November 5, 2005, Germany.Copyright 2005 ACM 1-59593-194-5/05/0011.

[2] T. Osman, D. Thakker, G. Schaefer, P. Lakin,An integrative semantic framework for imageannotation and retrieval InternationalConference on Web Intelligence 0-7695-3026-5/07, 2007 IEEE/WIC/ACM

[3] S. Jun-feng et al.Ontology-based information retrieval model for

the semantic Web Changsha ,China 2005

8/7/2019 UBICC-ID365_365

9/9

[4] P. Castells, M. Fernandez, D. Vallet,An adaptation of the vector-space model forontology-based information retrievalIEEE transactions on knowledge & Engineering,Vol. 19, No. 2, February 2007 1041-4347/07

[5] E. Toch & A. Gal

A semantic approach to approximate serviceretrieval ACM Transactions on InternetTechnology, Vol. 8, No. 1, Article 2, Nov.2007.

[6] B.Espinasse ; S.Fournier ; F.FreitasAGATHE : Une architecture gnrique basedagents et dontologies pour la collectedinformation sur domaines restreints du webCORIA 2007

[7] Tanveer J Siddiqui ; U.S tiwaryIntegration notion of agency and semantics ininformation retrieval :An intelligent multi agentmodel University of Allahabad IEEE 2005

[8] Carmine Cesarano et al.

An Intelligent search agent system of semanticinformation retrieval on the Internet ACM 2003[9] Larry Kershberg et al.

Knowledge Sifter: Agent based ontology-driven search over heterogeneous databasesusing semantic web servicesGeorge Mason University USA 2004

[10] E. Desmontils & C. Jacquin"Indexing a Web Site with a TerminologyOriented Ontology"

IRIN, Universit de Nantes- IOS Press, 2002[11] Kwang Mong Sim

Towards Holistic Web-based Information

Retrieval: An Agent-based ApproachIEEE/WIC International Conference on WebIntelligence (WI03) IEEE 2003