JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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Rizwan Iqbal and Masrah Azrifah Azmi Murad “ada” and “austin”. As for “ada”, the system will locate “useslanguage” and for “austin”, the system will recognize “isinCity”. Step 5: The selected object properties are set into pre-defined template in SPARQL in a specified format. The basic format is as below. The selected object property is set in the following sequence of triple. ?subject name of selected property?Var FILTER(?Var!=(name of instance or literal)) In the case of the exampled query, the exact query transformation in SPARQL will be in the following format. The negation query handling engine will intelligently identify the intent of the user in the negation sense and will place a “!” in the filter part of the SPARQL query. In the case of the example query, the “?City!=p1:austin” means to exclude all those jobs which are not in “Austin” city. SELECT ?subject ?Lang ?City WHERE{?subject a p1:ITJob. ?subject p1:usesLanguage ?Lang. ?subject p1:isInCity ?City.} FILTER(?Lang=p1:ada && ?City!=p1:austin). CONCLUSION AND RECOMMENDATIONS FOR FUTURE WORK This paper has proposed a negation query handling engine that was specifically designed for handling negation queries. The designed negation query handling engine was evaluated using the Mooney dataset (MooneyData, 1994) and AquaLog dataset (AquaLogData, 2007). It was found that the proposed negation query handling engine correctly transformed 72.7% of Mooney dataset and 41.6% of AquaLog dataset negation queries to their formal query language equivalent. The proposed engine demonstrated encouraging results. Hence, it is a step forward towards an effective handling of complex natural language query transformations. It is an intention for the future to further improve the algorithm so as to bring more correctness in the negation query transformations. An evaluation of the proposed engine on other datasets may also lead to new insights, which can be incorporated in the existing algorithm of the negation query handling engine to make it more effective and robust in handling negation queries. 202 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
REFERENCES A Negation Query Engine for Complex Query Transformations Fig.3: The step-by-step processing of the natural language query AquaLogData. (2007). Retrieved from http://kmi.open.ac.uk/technologies/aqualog/examples.html Cimiano, P., Haase, P., Heizmann, J., Mantel, M., & Studer, R. (2008). Towards portable natural language interfaces to knowledge bases - The case of the ORAKEL system. Data and Knowledge Engineering, 65, 325-354. Corby, O., Dieng-Kuntz, R., & Faron-Zucker, C. (2004). Querying the Semantic Web with the corese search engine. 16th European conference on Artificial Intelligence (ECAI-2004), 705-709. Cohen, W. W., Ravikumar, P., & Fienberg, S. E. (2003). A Comparison of String Distance Metrics for Name-Matching Tasks. Workshop on information integration on the Web (IIWeb-03), 73-78. Damljanovic, D., Agatonovic,M., & Cunningham, H. (2010). Natural Language interface to ontologies: Combining syntactic analysis and ontology-based lookup through the user interaction. European Semantic Web Conference (ESWC 2010), 106-120. Fellbaum, C. (1998). WORDNET: An Electronic Lexical Database. MIT Press, 1998. Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 203
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Journal of Science & Technology Jou
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International Advisory Board Adarsh
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Selected Articles from UPM-Malaysia
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ISSN: 0128-7680 © 2013 Universiti
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Aspect of Fatigue Analysis of Compo
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Surface Roughness Y. Yusuf, J. M. J
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DISCUSSION Y. Yusuf, J. M. Juoi, Z.
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Modelling of Motion Resistance Rati
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Statistical Analysis Norhashila Has
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INTRODUCTION Tajau, R. et al. A hig
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CONCLUSION Aji, I. S., Zinudin, E.
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Hasiah Mohamed@Omar, Azizah Jaafar
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The Role of Similarity Measurement
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TABLE 5: Winners MRS The Role of Si
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Guidelines for Authors Publication
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Usability of Educational Computer G
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JST Vol. 21 (1) Jan. 2013 - Pertanika Journal - Universiti Putra ...

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