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

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Rizwan Iqbal and Masrah Azrifah Azmi Murad correctness without clarification dialogues was considerably low as compared to other similar systems like PANTO (Wang et al., 2007) for the same data. Damljanovic et al. (2010) also reported that FREya was not able to answer some questions correctly while evaluating. The questions that were answered incorrectly included those with negation. PANTO (Wang et al., 2007) is another natural language interface that is portable and does not make any assumption about any specific knowledge domain. It functions in a way that it picks the words from the natural language query and map them to entities (concepts, instances, relations) in the ontology. The architecture of PANTO (Wang et al., 2007) relies on the existing tools like WordNet (Fellbaum, 1998) and different string metric algorithm. In PANTO, the Lexicon Builder automatically extracts ontological resources (Classes, Object and Datatype properties, Literals, Instances) from the ontology and constructs the Lexicon. The translator is the core processing engine of PANTO. It receives the processed natural language query from the parser as input and then performs operations to map the natural language entities to the ontological entities. PANTO uses an off the shelf parser which relies on limited NLP techniques, and this restricts the scope of queries which can be handled by the system (Wang et al., 2007). Wang et al. (2007) also discussed the limitation of PANTO in relation to the weakness in supporting complex user interactions. The current version of PANTO deals with superlative, comparative, conjunction and negation kind of queries. Nonetheless, PANTO has not discussed how effectively it can deal with the queries. PANTO discusses that it can handle negation queries including “not” and “no”. However, Wang et al. (2007) have not discussed in detail how effectively PANTO can deal with negation queries or what the precision of the system is in catering particularly with negation queries. From the literature, it is found that all systems have discussed about their supports for catering negation queries. Wang et al. (2007) mentioned that it could support negation queries but did not give any detail that to what extent and precision it could cater them. Damljanovic et al. (2010) discussed about catering negation cases, and explicitly mentioned that their system had failed to answer some questions correctly and amongst them were questions with negation. MOTIVATION FOR NEGATION QUERY HANDLING ENGINE After studying the trend in some natural language interfaces over the years, it is found that the state-of-art interfaces are particularly focusing on some specific features. Some of these features include portability (Wang et al., 2007), users’ interaction (Damljanovic et al., 2010), automated learning (Vanessa et al., 2007; Damljanovic et al., 2010), lesser customization (Lei et al., 2006), detecting and resolving the ambiguity in natural language (Lei et al., 2006) and precision in understanding the complexity of the natural language query (Wang et al., 2007; Tablan et al., 2008a). For a natural language interface to be effective for the user, it is very critical for it to understand the complexity of the natural language query inputted by the user. Over the years, researchers have improved from simple string matching algorithms to advance natural language processing engines which are designed to understand the complexity of natural language. It was found that natural language interfaces have the capability to deal with different types of queries such as instance superlative, comparative, conjunction and negation. Systems like 196 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
A Negation Query Engine for Complex Query Transformations PANTO (Wang et al., 2007) and FREya (Damljanovic et al., 2010) have discussed negation queries. In particular, PANTO has given no details about the extent, details and precision for which it can handle the negation queries. FREya only discussed that the system was not able to answer some questions correctly and amongst them were questions with negation. Natural language queries with negation are very usual and expected input from the user. A review of relevant literature has shown that the Mooney dataset (MooneyData, 1994) and AquaLog dataset (AquaLogData, 2007) are widely used for the evaluation of natural language interfaces (Wang et al., 2007; Damljanovic et al., 2010; Tablan et al., 2008a; Kaufmann et al., 2007). These datasets have negation queries in them. A natural language interface with the capability to handle negation queries must be able to correctly interpret the intent of the user’s query to its equivalent formal query language. In order to correctly interpret the intent of the user’s query, the natural language interface cannot just rely on simple keyword detection and simple string matching algorithms. An effective negation handling interface must have a sophisticated algorithm that is governed by a set of rules. These rules should give a deeper insight into the natural language interface about the negation query under consideration. An in-depth understanding of the negation query will facilitate the interface to make appropriate transformations of natural language query to its equivalent formal query language, keeping intact the intent of the user. The absence of such an algorithm focusing to handle negation queries has become a motivation for this research. This research came up with a negation query handling engine which incorporates an algorithm that has been particularly designed to cater negation queries in an effective manner. The next section discusses the design of negation query handling engine in detail. THE DESIGN OF NEGATION QUERY HANDLING ENGINE The negation query handling engine was designed to cater effective machine level transformation for the natural language query entered by the user. The engine was designed to perform some processes in a sequential manner. There is also a set of natural language query transformation rules which are implemented according to the structure of the natural language query entered by the user. Fig.1 shows how the negation query handling engine works. The processes performed by the negation query handling engine The negation query handling engine performs three processes on the natural language query entered by the user. The first process identifies the negation keywords in the natural language query. The second process is responsible to identify the coordinating conjunctions in the natural language query structure. The final process is responsible to make sure that the natural language keywords are correctly mapped to the corresponding ontological resources (Classes, Object and Datatype properties, Literals, Instances) and the machine level transformations are correctly carried out. Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 197
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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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Heng, S. C., Ibrahim, Z. B., Suleim
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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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Assessment of Heavy Metal Pollution
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Norhashila Hashim et al. effectiven
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Statistical Analysis Norhashila Has
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Norhashila Hashim et al. Post-hoc a
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Norhashila Hashim et al. Cubero, S.
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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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D. Bachtiar, S. M. Sapuan, E. S. Za
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REFERENCES Yogan Jaya Kumar, Naomie
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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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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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The Journal’s review process What
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Usability of Educational Computer G
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