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Figure.1 The architecture of the proposed system During searching and retrieval process, a novel and promising approach is concept-based search [7], [8], [9]. Ontology-based approach to IR is presented. With this approach, the burden of knowing how the documents are written is taken off the user and hence the user can focus on searching on a conceptual level instead. One problem with this approach is to find good concepts. Domain ontology is useful for query expansion by proliferating the input words with the relevant domain concepts. The system is based on a domain concepts representation schema in form of ontology. With the use of ontology, concepts and relations representing concepts about a particular document in domain specific terms are built. International Journal of Information Technology Convergence and Services (IJITCS) Vol.1, No.5, October 2011

Figure 1 1 The architecture of the proposed system During searching and retrieval process, a novel and promising approach is concept-based search [7], [8], [9]. Ontology-based approach to IR is presented. With this approach, the burden of knowing how the documents are written is taken off the user and hence the user can focus on searching on a conceptual level instead. One problem with this approach is to find good concepts. Domain ontology is useful for query expansion by proliferating the input words with the relevant domain concepts. The system is based on a domain concepts representation schema in form of ontology. With the use of ontology, concepts and relations representing concepts about a particular document in domain specific terms are built. International Journal of Information Technology Convergence and Services (IJITCS) Vol.1, No.5, October 2011

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The main advantages of this search method are the good results and the applicability to non-structured texts. Our approach can overcome the lack of knowledge about the semantics of the texts with the use of domain ontology in conceptual query enrichment. So the proposed system combined the strength of the statistical IR algorithm with the benefits of ontology model to ensure high precision and recall in information retrieval within digital library.
The main advantages of this search method are the good results and the applicability to non-structured texts. Our approach can overcome the lack of knowledge about the semantics of the texts with the use of domain ontology in conceptual query enrichment. So the proposed system combined the strength of the statistical IR algorithm with the benefits of ontology model to ensure high precision and recall in information retrieval within digital library.
International Journal of Information Technology Convergence and Services (IJITCS) Vol.1, No.5, October 2011 Figure.2 The partial ontology for computer science domain For example, in input string “a” that contains “digital_signal processing”, in which the erm “digital_signal” is the key term in the domain and so its equivalent conceptual terms “digital signal processing, speech processing, wavelets, FFT algorithms, video processing, image rocessing, time-frequency analysis, digital signal processors, voice technology speech ecognition, audio editors”) are extracted from the domain ontology using SPARQL query anguage as shown in table 2. And the extracted terms are added to the input string. However, this ystem has limitation that is two or more pair domain terms must be separated with underscore, _”, not space. For instance, when user wants to search “concurrency control system’, the user nust enter “concurrency_control system’. There are so many pair domain terms such as lata_mining, machine_learning, artificial_intelligence, data_structure, knowledge_engineering, omputer_algebra, computer_vision, error_detection, knowledge_representation, rror_correction, cluster_analysis, memory_management, computational_number, omputational_statistics, computational_physics, , pattern_matching and so on. Table 2. Conceptual Terms Extraction using SPARQL Language ”
International Journal of Information Technology Convergence and Services (IJITCS) Vol.1, No.5, October 2011 Figure.2 The partial ontology for computer science domain For example, in input string “a” that contains “digital_signal processing”, in which the erm “digital_signal” is the key term in the domain and so its equivalent conceptual terms “digital signal processing, speech processing, wavelets, FFT algorithms, video processing, image rocessing, time-frequency analysis, digital signal processors, voice technology speech ecognition, audio editors”) are extracted from the domain ontology using SPARQL query anguage as shown in table 2. And the extracted terms are added to the input string. However, this ystem has limitation that is two or more pair domain terms must be separated with underscore, _”, not space. For instance, when user wants to search “concurrency control system’, the user nust enter “concurrency_control system’. There are so many pair domain terms such as lata_mining, machine_learning, artificial_intelligence, data_structure, knowledge_engineering, omputer_algebra, computer_vision, error_detection, knowledge_representation, rror_correction, cluster_analysis, memory_management, computational_number, omputational_statistics, computational_physics, , pattern_matching and so on. Table 2. Conceptual Terms Extraction using SPARQL Language ”
Table 2. Precision and Recall of the Proposed System based on the Preliminary Test 7. CONCLUSION
Table 2. Precision and Recall of the Proposed System based on the Preliminary Test 7. CONCLUSION
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Information SystemsBioinformaticsComputer ScienceInformation RetrievalInformation TechnologySocial NetworksAutonomous Cognitive AgentsEmbedded SystemsComputer SecurityComputer Hardware
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