An Approach for Identification of User’s Intentions During the Navigation in Semantic Websites

Roberto, Rafael Liberato; Silva, Sérgio Roberto

doi:10.1007/978-3-540-72667-8_27

Cited by 3 publications

(3 citation statements)

References 15 publications

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“…This approach is simple but effective in capturing a user's interests. In general, we could also use other approaches to obtain the documents of relevance, for example, by using the surfing behaviors [20,37] or referring to the context environment [35].…”

Section: Resultsmentioning

confidence: 99%

Learning and inferencing in user ontology for personalized Semantic Web search

Xing

Tan

2009

Information Sciences

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a b s t r a c tUser modeling is aimed at capturing the users' interests in a working domain, which forms the basis of providing personalized information services. In this paper, we present an ontology based user model, called user ontology, for providing personalized information service in the Semantic Web. Different from the existing approaches that only use concepts and taxonomic relations for user modeling, the proposed user ontology model utilizes concepts, taxonomic relations, and non-taxonomic relations in a given domain ontology to capture the users' interests. As a customized view of the domain ontology, a user ontology provides a richer and more precise representation of the user's interests in the target domain. Specifically, we present a set of statistical methods to learn a user ontology from a given domain ontology and a spreading activation procedure for inferencing in the user ontology. The proposed user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine, called OntoSearch, to provide personalized document retrieval services. The experimental results, based on the ACM digital library and the Google Directory, support the efficacy of the user ontology approach to providing personalized information services.

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Section: Resultsmentioning

confidence: 99%

Learning and inferencing in user ontology for personalized Semantic Web search

Xing

Tan

2009

Information Sciences

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“…Xing Jian et al used a user model to provide customized information services [12]. On the web, customization is important as it provides suitable services to different users [13,14,30]. In our architecture, we define operational and application ontology that assist the integration of agent and semantic web services.…”

Section: Related Workmentioning

confidence: 99%

Using Ontology-based BDI Agent to Dynamically Customize Workflow and Bind Semantic Web Service

Liu¹,

Chen²

2012

JSW

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As the Web gradually evolves into the semantic web, the World Wide Web consortium (W3C) recommends that web ontology language (OWL) be used to encode semantic information content over the Web. Semantic web is an essential infrastructure to enhance Web to obtain better integration of information and intelligent use of web resources. Moreover, a web service is annotated by web ontology language for service (OWL-S) to form a semantic web service that, however, is a static description. The OWL-S based semantic web services thus are reactively invoked by users. How to dynamically coordinate, composite, or discover the services is an important issue.

We use agent to proactively interpret the static OWL-S description. And, the Belief-Desire-Intention (BDI) model is applied to develop BDI agent. This work thus proposes a ontology-based BDI agent architecture, in which a BDI agent dynamically generates customized workflow, and binds semantic web services. The architecture includes four parts: 1) Application Ontology, which is description of a specialized domain, 2) Operation ontology, which is description of BDI agent, 3) Ontology-based BDI agent engines, which interpret corresponding operational ontology to dynamically generate workflows, and 4) Java agent development environment extension (JadeX) platform that our architecture is based on. Through JadeX, our BDI agent can dynamically bind semantic web services according to customized workflows

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“…Efforts in this direction include the Web Usage Ontology Generation [12] that applies fuzzy logic techniques to derive knowledge about the user's behavior. Only recently, Roberto and da Silva [18] presented an algorithm to classify the user's intentions recorded in a semantic log. Again, this approach presumes that the analyzed web site has an underlying adaptation ontology.…”

Section: Semantic User and Domain Modelingmentioning

confidence: 99%

Cultural Adaptivity for the Semantic Web

Reinecke

2007

The Semantic Web

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Abstract. Our research explores the possibilities for factoring culture into user models, working towards cultural adaptivity in the Semantic Web. The aim is to automate the localization process by acquiring details about an individual's cultural background. The paper shows how we have based our approach on research findings from related fields, outlining the connection between the cultural user and domain model ontologies, and a test environment. Furthermore, we describe a preliminary plan of how to validate our cultural user model by comparison to our previous experience in the manual adaptation of imbuto.

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An Approach for Identification of User’s Intentions During the Navigation in Semantic Websites

Cited by 3 publications

References 15 publications

Learning and inferencing in user ontology for personalized Semantic Web search

Learning and inferencing in user ontology for personalized Semantic Web search

Using Ontology-based BDI Agent to Dynamically Customize Workflow and Bind Semantic Web Service

Cultural Adaptivity for the Semantic Web

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