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  • × theme_ss:"Wissensrepräsentation"
  • × theme_ss:"Retrievalalgorithmen"
  1. Calegari, S.; Sanchez, E.: Object-fuzzy concept network : an enrichment of ontologies in semantic information retrieval (2008) 0.00 
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    Abstract
    This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.
    Type
    a
  2. Lee, J.; Min, J.-K.; Oh, A.; Chung, C.-W.: Effective ranking and search techniques for Web resources considering semantic relationships (2014) 0.00 
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    Abstract
    On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods.
    Content
    Vgl.: doi: 10.1016/j.ipm.2013.08.007. A short preliminary version of this paper was published in the proceeding of WWW 2009 as a two page poster paper.
    Type
    a

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