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Author: search.filters.author.Thongkrau, TheerayutSubject: search.filters.subject.Latent semantic analysis

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    Item type:Publication,
    OntoPop: An ontology population system for the semantic web
    (2012-01-01)
    Thongkrau, Theerayut
    ;
    Lalitrojwong, Pattarachai
    The development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. How ever, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syn tactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.9%, and executes time of 5.46 second/instance. Copyright © 2012 The Institute of Electronics, Information and Communication Engineers.
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    Item type:Publication,
    Classifying instances into lexical ontology concepts using latent semantic analysis
    (2010-05-28)
    Thongkrau, Theerayut
    ;
    Lalitrojwong, Pattarachai
    A lexical ontology is useful as the basic knowledge base in artificial intelligence and computational linguistics application. However, it is insufficient to recognize only existing instances for each concept. Adding new instances into the lexical ontology will expand knowledge in the system. In this paper, we propose an efficient unsupervised instance population system that classifies new instances into a corresponding lexical ontology concept. Compared to previous related works, it does not require manual preprocessing to prepare training data. In terms of processing time, it does not need to search for many concepts in the lexical ontology. Furthermore, it is able to handle an unlimited number of ontological concepts in any domain. Our system employs latent semantic analysis together with context voting to find the appropriate concept of the instance. The experiments demonstrate that this approach compared to similarity approach yields higher accuracy for instance classification. In sum, the system achieves higher accuracy when the lexical ontology contains a lot of concepts, which generally occurs in practical problems. ©2010 IEEE.