Intelligent Information Access Based on Logical Semantic Binding Method
Rabiah Abdul Kadir, Tengku Mohd Tengku Sembok, Badioze Zaman Halimah · Advances in Knowledge Representation · 2012
This topic describes a method for natural language understanding that concerned with the problem of generating an automated answer for open-ended question answering processes that involve open-ended questions (ie.WHO, WHAT, WHEN, WHERE and WHY).The problem of generating an automated answer involves the context of sophisticated knowledge representation, reasoning, and inferential processing.Here, an existing resolution theorem prover with the modification of some components will be explained based on experiments carried out such as: knowledge representation, and automated answer generation.The answers to the questions typically refer to a string in the text of a passage and it only comes from the short story associated with the question, even though some answers require knowledge beyond the text in the passage.To provide a solution to the above problem, the research utilizes world knowledge to support the answer extraction procedure and broadening the scope of the answer, based on the theory of cognitive psychology (Lehnert, 1981, Ram & Moorman, 2005).The implementation used the backward-chaining deduction reasoning technique of an inference for knowledge based which are represented in simplified logical form.The knowledge based representation known as Pragmatic Skolemized Clauses, based on first order predicate logic (FOPL) using Extended Definite Clause Grammar (X-DCG) parsing technique to represent the semantic formalism.This form of knowledge representation implementation will adopt a translation strategy which involves noun phrase grammar, verb phrase grammar and lexicon.However, the translation of stored document will only be done partially based on the limited grammar lexicon.The queries will be restricted to verb and noun phrase form to particular document.The restriction adopted in the query is appropriate, since the objective is to acquire inductive reasoning between the queries and document input.Logical-linguistic representation is applied and the detailed translation should be given special attention.This chapter deals with question answering system where the translation should be as close as possible to the real meaning of the natural language phrases in order to give an accurate answer to a question.The aim of the translation is to produce a good logical model representation that can be applied to information access process and retrieve an accurate answer.This means that logical-linguistic representation of semantic theory chosen is practically correct for the intended application.The representation of questions and answers, and reasoning mechanisms for question answering is of concern in this chapter.To achieve a question answering system that is capable of generating the automatic answers for all types of question covered, implementation of logical semantic binding with its argument into existing theorem prover technique will describe in this chapter.Different types of questions require the use of different strategies to find the answer.A semantic model of question understanding and processing is needed, one that will recognize equivalent questions, regardless of the words, syntactic inter-relations or idiomatic forms.The process of reasoning in generating an automated answer began with the execution of resolution theorem proving.Then, the answer extraction proceeded with logical semantic binding approach to continue tracking the relevant semantic relation rules in knowledge base, which contained the answer key in skolem constant form that can be bounded.A complete relevant answer is defined as a set of skolemize clauses containing at least one skolem constant that is shared and bound to each other.The reasoning technique adopted by the system to classify answers, can be classed into two types: satisfying and hypothetical answers.Both classes were formally www.intechopen.com