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Building an Ontology-Based Framework For Semantic Information Retrieval: Application To Breast Cancer

Olfa Dridi, Mohamed Ben Ahmed

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Abstract

In this paper, we will present IR framework for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently faced by in information retrieval. Current information retrieval systems mostly use keyword search, which is unsatisfactory option because of its low precision and recall. In this paper, we consider concept- based information retrieval model as a new and promising way of improving search on the Web. Informally, concept- based information retrieval is search for documents based on their meaning rather than on the presence of the keywords in the object. Much more "smartness" should be embedded to search tools to manage effectively search, retrieval, filtering and presenting relevant information. The paper is organized as follows. Section 1 introduces the problem of current information retrieval systems. Section 2 presents an overview of the ontology-based information retrieval techniques. Section 3 presents our framework of IR. Section 4 describes an example of using ontologies for searching information about breast cancer and section 5 draws conclusions.

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What this paper is about

In this paper, we will present IR framework for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently faced by in information retrieval. Current information retrieval systems mostly use keyword search, which is unsatisfactory option because of its low precision and recall. In this paper, we consider concept- based information retrieval model as a new and promising way of improving search on the Web. Informally, concept- based information retrieval is search for documents based on their meaning rather than on the presence of the keywords in the object. Much more "smartness" should be embedded to search tools to manage effectively search, retrieval, filtering and presenting relevant information. The paper is organized as follows. Section 1 introduces the problem of current information retrieval systems. Section 2 presents an overview of the ontology-based information retrieval techniques. Section 3 presents our framework of IR. Section 4 describes an example of using ontologies for searching information about breast cancer and section 5 draws conclusions.

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Available abstract

In this paper, we will present IR framework for introducing ontologies in information retrieval. The main hypothesis is that the inclusion of conceptual knowledge such as ontologies in the information retrieval process can contribute to the solution of major problems currently faced by in information retrieval. Current information retrieval systems mostly use keyword search, which is unsatisfactory option because of its low precision and recall. In this paper, we consider concept- based information retrieval model as a new and promising way of improving search on the Web. Informally, concept- based information retrieval is search for documents based on their meaning rather than on the presence of the keywords in the object. Much more "smartness" should be embedded to search tools to manage effectively search, retrieval, filtering and presenting relevant information. The paper is organized as follows. Section 1 introduces the problem of current information retrieval systems. Section 2 presents an overview of the ontology-based information retrieval techniques. Section 3 presents our framework of IR. Section 4 describes an example of using ontologies for searching information about breast cancer and section 5 draws conclusions.

Key concepts: Information retrieval, Computer science, Human–computer information retrieval, Concept search, Ontology, Cognitive models of information retrieval, Semantic search, Adversarial information retrieval

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