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

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Rizwan Iqbal and Masrah Azrifah Azmi Murad restriction of vocabulary, whereas restricted natural language interfaces contradict the former and restrict users to input a restricted vocabulary. Natural language interfaces work by converting natural language into formal semantic query (Tablan et al., 2008a). Different interfaces rely on distinct techniques and algorithms to translate natural language query into formal semantic query. Some natural language interfaces that support full natural language support are Semsearch (Lei et al., 2006), NLP-Reduce (Kaufmann et al., 2007), FREya (Damljanovic et al., 2010), QuestIO (Tablan et al., 2008b), Spark (Zhou et al., 2007), Q2Semantic (Wang et al., 2008) and NLION (Ramachandran & Krishnamurthi, 2009). Natural language interface that comes under the category of restricted natural language interfaces include Orakel (Cimiano et al., 2008), and AquaLog (Vanessa et al., 2007). Natural language queries with negation are a very usual and expected input from the user. In this study, Mooney dataset (MooneyData, 1994), which is widely applied for the evaluation of natural language interfaces and systems (Wang et al., 2007; Damljanovic et al., 2010; Tablan et al., 2008b; Kaufmann et al., 2006; Iqbal et al., 2012) was used. This dataset has a number of negation queries in it. This paper proposes a negation query handling engine, which effectively caters negation natural language queries. The proposed negation query handling engine supports full natural language rather than restricted language. The proposed engine effectively understands the intent of the user’s negation query on the basis of a sophisticated algorithm, which is governed by a set of techniques and transformation rules. The rest of this paper is organized as follows. Work related to the current study is discussed in the next section. Then, the motivation for the proposed negation query handling engine is discussed. Later sections cover the design, technical details and evaluation of the proposed engine. Finally, the paper is concluded by the conclusion and future work section. RELATED WORK All developed natural language interfaces differ from each other in one way or another. Some natural language interfaces focus on giving users the freedom of entering natural language query using free vocabulary (Lei et al., 2006), while others allow users to enter natural language query using a restricted vocabulary (Cimiano et al., 2008; Vanessa et al., 2007). Other than natural language interfaces, there are also interfaces that allow the user to search knowledge bases by inputting formal language queries. Such search engines are of two types; first is the form-based search engine, which provides web forms as a means of specifying queries (Rocha et al., 2004), and second is the RDF-based querying search engine which supports RDF-based querying languages at the front end (Rocha et al., 2004). SHOE (Heflin & Hendler, 2000) is an interface which comes in the category of form-based semantic search engine. This system is based on a semantic mark-up language. SHOE (Heflin & Hendler, 2000) allows users to define and associate vocabularies which can be understandable by the machines. The system uses knowledge annotator to add up SHOE annotations to the documents. Naive users often feel uncomfortable using SHOE as the web forms require familiarity with the knowledge bases being searched. On the other hand, adding annotations with SHOE is more time consuming as compared to standard XML. 194 Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013)
A Negation Query Engine for Complex Query Transformations Corese (Corby et al., 2004) search engine is from the category of RDF-based querying language fronted search engines. This search engine is dedicated to RDF metadata. The Corese engine internally works on the concept of conceptual graphs (CG). The query and the ontology schema are translated from RDF to conceptual graphs (CG) in order to perform matching operations. The limitation with such search systems is that they require the users to be familiar with both the knowledge base and the querying language used. The kind of interfaces that take a formal language query for input remains comparatively less preferred by users than the natural language query interfaces. The reason is the fact that users require training of the system as well as reasonable knowledge about the knowledge bases being searched. When talking about the systems that support natural language, SemSearch (Lei et al., 2006) is an interface that was designed to take input in natural language from the users. The system has idealized Google for the style of its interface. In particular, SemSearch (Lei et al., 2006) uses three heuristic operators to support its search. The use of these heuristic operators helps the system to have a clue of what information exactly the user wants from the knowledge bases. SemSearch relies on simple string matching algorithms rather than using complex techniques like WordNet (Fellbaum, 1998) in order to reduce the response time of the system. The use of the simple string matching algorithms reduces the response time but at the same time, it can be a reason for losing some good matches in certain situations. AquaLog (Vanessa et al., 2007) is a natural language based question-answering system. It takes input in the natural language query and answers on the basis of ontology loaded in the system. AquaLog has a learning mechanism in which it helps to improve the performance over time. Relation Similarity Service (RSS) is a component of AquaLog and it is considered as the backbone of the system. In case of any ambiguity between multiple terms, the RSS module directly interacts with the user to disambiguate between the terms of natural language and the concepts of the knowledge bases. The limitation with AquaLog is that it can at the most translate the query to two triples. QuestIO (Tablan et al., 2008b) is another natural language interface that was designed to take natural language input from the user. It was designed to cater language ambiguities, handle incomplete and grammatically incorrect queries. QuestIO focuses to be an open domain system that does not require any customization by the users, as well as any training to use it. This system uses light weight linguistic processing that allows the user’s text to be fully analyzed in the query processing part for the identification of ontology concepts and property names. QuestIO works by finding implicit relations which are not clearly stated in the user query. The limitation is that it only works with directly explored relations (Tablan et al., 2008b). The creators of QuestIO (Tablan et al., 2008b) later developed another natural language interface named FREya (Damljanovic et al., 2010). The focus of creating FREya was to further reduce customization efforts and to introduce clarification dialogues mechanism to avoid empty results. The clarification dialogue mechanism in FREya helps users to get answers in case the system is not able to find any answer (Damljanovic et al., 2010). If the system does not come up with an answer automatically, it will interact with the end users to get a clue for the right answer. The user’s selections are saved over time and the system learned to place correct suggestions on top of any similar query next time on the basis of the saved user selections. FREya reported satisfactory results for the learning mechanism in it but its Pertanika J. Sci. & Technol. 21 (1): 283 - 298 (2013) 195
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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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Application of Anthropometric Dimen
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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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The Empirical Method Modelling of M
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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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Lemma 1.1 The equation S. Ismail an
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CONCLUSION S. Ismail and K. A. Mohd
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INTRODUCTION Tajau, R. et al. A hig
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Aji, I. S., Zinudin, E. S., Khairul
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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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Yogan Jaya Kumar, Naomie Salim, Ahm
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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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