Ontology Construction for Information Selection

  • Authors:

  • Latifur Khan;Feng Luo

  • Affiliations:

  • -;-

  • Venue:
  • ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
  • Year:
  • 2002

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Abstract

Technology in the field of digital media generates huge amounts of non-textual information, audio, video, and images, along with more familiar textual information. The potential for exchange and retrieval of information is vast and daunting. The key problem in achieving efficient and user-friendly retrieval is the development of a search mechanism to guarantee delivery of minimal irrelevant information (high precision) while insuring relevant information is not overlooked (high recall). The traditional solution employs keyword-based search. The only documents retrieved are those containing user specified keywords. But many documents convey desired semantic information without containing these keywords. One can overcome this problem by indexing documents according to meanings rather than words, although this will entail a way of converting words to meanings and the creation of ontology. We have solved the problem of an index structure through the design and implementation of a concept-based model using domain-dependent ontology. Ontology is a collection of concepts and their interrelationships, which provide an abstract view of an application domain. We propose a new mechanism that can generate ontology automatically in order to make ourapproach scalable. For this we modify the existing self-organizing tree algorithm (SOTA) that constructs a hierarchy from top to bottom. Furthermore, in order to find an appropriate concept for each node in the hierarchy we propose an automatic concept selection algorithm from WordNet called linguistic ontology.To illustrate the effectiveness of our automatic ontology construction method, we have explored our ontology construction in text documents. The Reuters21578 text document corpus has been used. We have observed that our modified SOTA outperforms hierarchical agglomerative clustering (HAC).