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

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TABLE 1: Query transformation rules Rules Actions Coordination conjunction detected. Coordination conjunction is not detected. Literal or instance is not detected. Literal or instance is detected. Is/ Does/ Has is detected at the beginning of the Query. Rizwan Iqbal and Masrah Azrifah Azmi Murad The query transformation is not based on a single step. The keywords before the coordinating conjunction are considered for the first step of the natural query transformation. The remaining part of the natural query is treated as the second part of a query transformation. After the completion of the transformation for the first part of the query, the remaining part of the query will be treated as a different part and all the transformation processes will be performed from the beginning. The query transformation is a single step. All the keywords in the natural language query are considered for the natural language query transformation. The natural language query refers to an object or data property. Matching algorithms are run to find the appropriate matches for the object or data property from a list of ontological resources. The natural language query has detected a literal or an instance. The next step is to find the associated data or object property with the detected literal or instance from a list of ontological resources. This is the condition that deals with affirmative-negative and affirmativenegative-pseudorel type of queries. The next step is to detect an instance in between Is/ Does/ Has and "?" in the query. If an instance is detected, all the keywords (excluding Is/ Has/ Does) and the instance will then be matched to find the best match for object property associated with the instance. 6. Transformations are based on some predefined rules, depending whether a literal/ instance or a data/object property is detected. 7. If there is a coordinating conjunction detected at step 3, all the steps will be repeated for the remaining part of the query. The step-by-step processing for the second case is as below. 1. Is/ Does/ Has is detected at the beginning of the query which ends with a “?”. 2. The next step is to look for an instance after the words Is/ Does/ Has. 3. If an instance is found, the next step is to find an object property. 4. Matching is performed by the algorithm, which excludes Is/ Does/ Has, and the instance is to find the best match for the object property associated with the instance. 5. Transformations are performed based on some predefined rules and templates. EVALUATION OF NEGATION QUERY HANDLING ENGINE The designed negation query handling engine is evaluated using the negation queries in the Mooney dataset (MooneyData, 1994) and Aqualog dataset (AquaLogData, 2007). The Mooney data set included a total of 88 negation queries. There are 74 negation queries in the job Mooney dataset and 14 negation queries in the geography Mooney dataset. There are a total 24 negation queries in the Aqualog dataset. The Aqualog dataset has divided these negation queries into 200 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
A Negation Query Engine for Complex Query Transformations three different categories. The category names are affirmative-negative, affirmative-negativepseudorel and negation. The designed algorithm of the negation query handling engine correctly transformed 72.7% of the negation queries in the Mooney dataset and 41.6% of the negation queries in the Aqualog data set to their formal query language equivalent. Table 2 and Fig.2 show the evaluation of the proposed algorithm. The designed algorithm of the negation query handling engine manifested encouraging results for the negation queries in the Mooney dataset and AquaLog dataset. All the negation queries in the Mooney dataset and AquaLog data set were parsed through the algorithm of the proposed negation query handling engine. Below are some examples of how every query with negation is individually parsed and the proposed algorithm is evaluated. Sample Query 1 (Job Mooney data set): “Are there ada jobs outside austin?” Refer to Fig.3 for details of the sequence of processing natural language in the proposed negation query handling engine. Step 1 : Negation keyword detected: outside. Step 2 : Coordinating conjunction not detected. According to the transformation rules, all the words in the natural language query will be considered for formal language query transformation (SPARQL). Step 3 : Instances are detected: “ada” and “austin”. According to the query transformation rules (Table 2) if a literal or an instance is detected, the next step is to find the associated data or object property with the detected literal or an instance from the list of ontological resources. Step 4 : The negation query handling engine will look for the object properties associated with the instances. The engine will find the associated object properties associated with TABLE 2: Results obtained for query transformation Dataset Total negation queries No of queries correctly transformed to formal query language Mooney 88 64 72.7% Aqualog 24 10 41.6% Fig.2: Query transformation results Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) % of queries correctly transformed to formal query language 201
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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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Different Media Formulation on Bioc
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CONCLUSION Heng, S. C., Ibrahim, Z.
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Y. Yusuf, J. M. Juoi, Z. M. Rosli,
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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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Norhashila Hashim et al. effectiven
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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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The Role of Similarity Measurement
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TABLE 5: Winners MRS The Role of Si
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REFEREES FOR THE PERTANIKA JOURNAL
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Guidelines for Authors Publication
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table should be prepared on a separ
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