**Section 3**

**Usage of Representations** 

134 Advances in Knowledge Representation

Jiang T. & Li, M. (1998). Formal Grammars and Languages. In Handbook on Algorithms and

Kaiya, H. & Saeki, M. (2006). Using Domain Ontology as Domain Knowledge for

Li, K., Dewar, R.G., & Pooley, R.J. (2003) *Requirements capture in natural language problem* 

Robertson, S., & Robertson, J. (2006). *Mastering the Requirements Process* (second edition),

Ruohonen, K. (2009). Formal Languages. In lecture notes for the TUT course "Formaalit

Rubin, E. (2009) *Domain Knowledge Representation in Information Systems*, Thesis of Doctor of

Seidewitz, Ed. (2003) What models mean. *IEEE Software*, Vol. 20, No. 5, (September 2003),

Wei, L., Ke-Qing, H., Kui, Z., & Jian, W. (2008) Combining Domain-Driven Approach with

Zapata, C.M., Gelbukh, A., & Arango, F. (2006). Pre-conceptual Schema: A Conceptual-

Zapata, C.M., Giraldo, G.L., & Londoño, S. (2011). Executable pre-conceptual schemas,

Zapata, C.M., Giraldo, G.L., & Mesa, J.E. (2010). A Proposal of Meta-Ontology for

Requirement Assets for Networked Software Requirements Elicitation. *Proceedings of IEEE International Conference on Semantic Computing*, 978-0-7695-3279-0,

Graph-Like Knowledge Representation for Requirements Elicitation, *Lecture Notes* 

*Revista Avances en Sistemas e Informática*, Vol.7 No.3, pp. 15-23, Medellín, December

Requirements Elicitation, *Ingeniare. Revista chilena de ingeniería*, Vol. 18, No. 1,

kielet". Available on http://math.tut.fi/~ruohonen/FL.pdf

Philosophy, University of British Columbia, Retrieved from

*in Computer Science*, Vol. 4293, pp. 17-27, 0302-9743

 http://www.macs.hw.ac.uk:8080/techreps/docs/files/HW-MACS-TR-0023.pdf Mitamura, T. & Nyberg, E. (1995). Controlled English for Knowledge Based Machine

*statements*, Heriot-Watt University. Retrieved from

Requirements Elicitation. *Proceedings of the 14th IEEE International Requirements Engineering Conference (RE'06),* 0-7695-2555-5, Minessota USA, September 2006. Leite, J.C. (1987). *A survey on requirements analysis*. Department of Information and Computer

Science, Advanced Software Engineering Project Technical Report RTP071,

Translation: Experience with the KANT System, *Proceedings of the International Conference on Theoretical and Methodological Issues in Machine Translation (TMI)*,

Theory of Computation. Editor: Mikhail J. Atallah, November 1998

California, Irvine

Belgium, July 1995

pp. 26-32, 0740-7459

California, August 2008

2010

Addison Wesley, New Jersey

https://circle.ubc.ca/handle/2429/15229

August 2010, pp. 26-37, 0718-3305

**6** 

*Malaysia* 

**Intelligent Information Access** 

**Based on Logical Semantic Binding Method** 

The idea of the computer system capable of simulating understanding with respect to reading a document and answering questions pertaining to it has attracted researchers since the early 1970s. Currently, the information access has received increased attention within the natural language processing (NLP) community as a means to develop and evaluate robust question answering methods. Most recent work has stressed the value of information access as a challenge in terms of their targeting successive skill levels of human performance and the existence of independently developed scoring algorithm and human performance measures. It is an exciting research implementation in natural language understanding, because it requires broad-coverage techniques and semantic knowledge which can be used to determine the strength of understanding the natural

In 2003, MITRE Corporation defined a new research paradigm for natural language processing (NLP) by implementing question answering system on reading comprehension. Reading comprehension offers a new challenge and a human-centric evaluation paradigm for human language technology. It is an exciting testbed for research in natural language

The current state-of-the-art development in computer-based language understanding makes reading comprehension system as a good project (Hirschman et al., 1999). It can be a valuable state-of-the-art tool to access natural language understanding. It has been proven by series of work on question answering for reading comprehension task, and it reported an accuracy of 36.3% (Hirschman et al., 1999) on answering the questions in the test of stories. Subsequently, the work of Charniak et al. (2000), Riloff & Thelen (2000), Ng et al. (2000) and Bashir et al. (2004) achieved 41%, 39.8%, 23.6% and 31.6%-42.8% accuracy, respectively. However, all of the above systems used a simple bag-of word matching, bag-of verb stem, hand-crafted heuristic rules, machine learning and advanced BOW and BOV approach. In contrast, this topic will discuss a logic representation and logical deduction approach for an inference. We aim to expand upon proposed logical formalisms towards semantic for question answering rather than just on surface analysis. Set of words, lexical and semantic clues, feature vector and a list

understanding towards the information access research problem.

of word token were utilized for knowledge representation in this approach.

**1. Introduction** 

language in computer science.

Rabiah A. Kadir1, T.M.T. Sembok2 and Halimah B. Zaman2 *1Universiti Putra Malaysia /Faculty of Computer Science and IT 2Univeristi Kebangsaan Malaysia /Faculty of Science and IT* 
