Learning and inferencing in user ontology for personalized Semantic Web search

Xing, Jun; Tan, Ah-Hwee

doi:10.1016/j.ins.2009.04.005

Cited by 94 publications

(46 citation statements)

References 43 publications

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“…We propose to create ontology-based user models for customizing the system and the information search task, taking the interests of individual users into account; see [14]. e concepts belonging to each user model must be weighted by analyzing the behavior of the speci c user.…”

Section: Dynamic Ematic Mapsmentioning

confidence: 99%

Thematic Maps for Geographical Information Search

Mauro

Ardissono

2017

Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

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ABSTRACTe exploration of cultural heritage information is challenged by the fact that most data provided by online resources is fragmented and it is repository or application-centered. In order to address this issue, a data integration approach should be adopted, that makes it possible to generate custom views, focused on the user's information needs, but easily extensible to support the inspection of topically related contents.In this paper, we present a model supporting the management of thematic maps for information exploration, and their integration with query expansion during the interaction with the user. Our model is based on: (i) an ontological domain knowledge representation for describing the meaning of concepts and their semantic relations; (ii) a semantic interpretation model for identifying the concepts referenced in the user's queries. We are experimenting our model in the OnToMap Participatory GIS, which manages interactive community maps for information sharing and participatory decision-making.

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Section: Dynamic Ematic Mapsmentioning

confidence: 99%

Thematic Maps for Geographical Information Search

Mauro

Ardissono

2017

Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization

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“…The first aspect of my PhD project concerns the creation of ontology-based user profiles to customize the system and the information search task taking individual interests and knowledge needs into account (Jiang and Tan (2009)). The user profiles can be combined with the domain knowledge with the purpose of enriching the search queries and increasing the likelihood that none of the potentially interesting data is missing.…”

Section: Ontology-based User Modelsmentioning

confidence: 99%

Personalization in Geographical Information Search

Mauro

2017

Electronic Workshops in Computing

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My PhD project focuses on the personalization of Participatory GIS (PGIS).In the project I analyze two methodologies to offer personalized search results in community maps and a natural interaction with the system. The first consists of automatically gathering the users interests at a concept level in order to generate clusters of concepts useful for the presentation of thematic maps. This is done by creating ontology-based user models mapped to the domain ontology adopted by the PGIS. The second concerns the creation of content-based user models useful for filtering the items belonging to each concept in a multifaceted way: the goal is that of reducing and adapting the information space presented in the map. In the project I also analyze how these techniques may be jointly used during the query expansion process to retrieve more accurate and relevant search results.

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“…Personalized semantic web search proposed by Jiang, A et al [7] discuss 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 user ontology model with the spreading activation based inferencing procedure has been incorporated into a semantic search engine to provide personalized document retrieval services.…”

Section: Related Workmentioning

confidence: 99%

Finding relevant semantic association paths through user-specific intermediate entities

Viswanathan¹,

Krishnamurthi²

2012

Hum. Cent. Comput. Inf. Sci.

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Semantic Associations are complex relationships between entities over metadata represented in a RDF graph. While searching for complex relationships, it is possible to find too many relationships between entities. Therefore, it is important to locate interesting and meaningful relations and rank them before presenting to the end user. In recent years e-learning systems have become very popular in all fields of higher education. In an e-learning environment, user may expect to search the semantic relationship paths between two concepts or entities. There may be numerous relationships between two entities which involve more intermediate entities. In order to filter the size of results set based on user's relevance, user may introduce one or more known intermediate entities. In this paper, we present a Modified bidirectional Breadth-First-Search algorithm for finding paths between two entities which pass through other intermediate entities and the paths are ranked according to the users' needs. We have evaluated our system through empirical evaluation. We have compared the execution time to discover the paths between entities for our proposed search method and existing method. According to our experiments our proposed algorithm improves search efficiently. The average correlation coefficient between the proposed system ranking and the human ranking is 0.69. It explains that our proposed system ranking is highly correlated with human ranking.

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Learning and inferencing in user ontology for personalized Semantic Web search

Cited by 94 publications

References 43 publications

Thematic Maps for Geographical Information Search

Thematic Maps for Geographical Information Search

Personalization in Geographical Information Search

Finding relevant semantic association paths through user-specific intermediate entities

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