Knowledge Seeker - Ontology Modelling for Information Search by Edward H. Y. Lim, James N. K. Liu, Raymond S.T. Lee

By Edward H. Y. Lim, James N. K. Liu, Raymond S.T. Lee

The KnowledgeSeeker is an invaluable method to strengthen quite a few clever purposes comparable to ontology-based seek engine, ontology-based textual content class procedure, ontological agent procedure, and semantic net approach and so on. The KnowledgeSeeker includes 4 diverse ontological parts. First, it defines the data illustration version ¡V Ontology Graph. moment, an ontology studying strategy that in keeping with chi-square records is proposed for computerized studying an Ontology Graph from texts for various domain names. 3rd, it defines an ontology iteration approach that transforms the educational consequence to the Ontology Graph structure for computing device processing and in addition should be visualized for human validation. Fourth, it defines varied ontological operations (such as similarity size and textual content class) that may be performed with using generated Ontology Graphs. the ultimate aim of the KnowledgeSeeker procedure framework is that it will probably enhance the conventional info procedure with larger potency. particularly, it will possibly bring up the accuracy of a textual content class approach, and likewise improve the quest intelligence in a seek engine. this is performed by way of improving the process with computer processable ontology.

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Sample text

Data mining is a semi-automatic process to discovery knowledge, rules, pattern in data store, and is implemented with several technologies such as artificial intelligence, machine learning, computational statistics, etc. In web mining, the entire World Wide Web is treated as the large database for mining the interested knowledge or data. The data source is retrieved from different web servers connected to the Internet, and the data might be in any format but mostly HTML. There are different tasks of web mining.

Y. K. T. Lee: Knowledge Seeker, ISRL 8, pp. 27 – 36. com 28 3 Text Information Retrieval represented by a collection of terms T = where ti represents the importance values, or weight, of term i assigned to document dj. The term weighting system varies among different approaches, but is mostly based on counting the term frequency in a document. For example, a collection of n documents indexed by m terms are presented by an m x n term by document matrix A=[aij]. For each aij in the matrix A is defined as the observed (or weighted) frequency of term i which occurs in document j.

The methods focus on the combination of natural language processing techniques, statistical measurement, and text mining with pattern matching algorithms (Kalvans and Muresan 2001). Most of these automatic terminology or glossary extraction methods work at the lexical level which refers to the term (a word or word phase) that used in a domain text. Terminology can be described technically by a graph-theoretic object. The graph object consists of nodes associated together by links, and the whole structure indexed by version number (Smith et al.

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