Representation of exceptional sentence using conceptual graph interchange format

This paper proposes a technique for representing the exceptional clauses of femalerelated issues in the Holy Quran. Verses are first extracted from www.surah.my, based on 18 female terms. Phrases abstracted from the verses are classified into one of the female issues. The exceptional sentences are then extracted based on the word "except". Using conceptual graph interchange format representation, a conceptual graph for each issue is constructed. The quality of the representation of exceptional sentences of the female issues are evaluated by using reasoning rules, which involved 240 phrases and 12 exceptional sentences that had been extracted from 228 verses. The findings rated that the proposed technique for exceptional clause has more useful reasoning than representing as a normal relation. The study suggests that the exceptional term is important for phrases classification and retrieval

Learning, Identifying, Sharing

This article argues that a cooperatively-built, well-organized, shared knowledge base is a new – and, from certain viewpoints, optimal – kind of support (refining and integrating other kinds of supports) for three complementary tasks: learning about living entities (and how to identify them), supporting their identification, and sharing knowledge about them. This article gives the ideas behind our prototype, and argues that knowledge providers can be not solely specialists, but also amateurs. In essence, for these three tasks, it argues for the (re-)use of much more semantically organized and interconnected versions of semantic wikis or scratchpads

NL-based automated software requirements elicitation and specification

This paper presents a novel approach to automate the process of software requirements elicitation and specification. The software requirements elicitation is perhaps the most important phase of software development as a small error at this stage can result in absurd software designs and implementations. The automation of the initial phase (such as requirement elicitation) phase can also contribute to a long standing challenge of automated software development. The presented approach is based on Semantic of Business Vocabulary and Rules (SBVR), an OMG’s recent standard. We have also developed a prototype tool SR-Elicitor (an Eclipse plugin), which can be used by software engineers to record and automatically transform the natural language software requirements to SBVR software requirements specification. The major contribution of the presented research is to demonstrate the potential of SBVR based approach, implemented in a prototype tool, proposed to improve the process of requirements elicitation and specification

Object-based knowledge representation of famale related issues from the Holy Quran

Focusing on the use of Semantic Network and Conceptual Graph (CG) representations, this paper presents an easy way in understanding concepts discussed in the Holy Quran.Quran is known as the main source of knowledge and has been a major source reference for all types of problems. However understanding the issues and the solution from the Quran is diffi cult due to lack of understanding of Quran literature.Meticulously, the Quran contains much important information related to female.However, this information are scattered and complexly linked. Technically, to extract and present the encapsulated knowledge on female matters in the Quran is a challenging task.Thus, this paper discusses on how to understand and represent the knowledge in an easy way.A total of 18 female terms are identifi ed. Through the terms, the name of surah, verses number and text from the verses are gathered. The texts are then analyzed and clustered into specific issues.Result of the analysis that consists of extracted knowledge on female issues is presented in a systematic structure using Semantic Network and CG.The strength and advantages of both approaches are compared, discussed and presented

Interaction history based answer formulation for question answering

With the rapid growth in information access methodologies, question answering has drawn considerable attention among others. Though question answering has emerged as an interesting new research domain, still it is vastly concentrated on question processing and answer extraction approaches. Latter steps like answer ranking, formulation and presentations are not treated in depth. Weakness we found in this arena is that answers that a particular user has acquired are not considered, when processing new questions. As a result, current systems are not capable of linking two questions such as “When is the Apple founded?” with a previously processed question “When is the Microsoft founded?” generating an answer in the form of “Apple is founded one year later Microsoft founded, in 1976”. In this paper we present an approach towards question answering to devise an answer based on the questions already processed by the system for a particular user which is termed as interaction history for the user. Our approach is a combination of question processing, relation extraction and knowledge representation with inference models. During the process we primarily focus on acquiring knowledge and building up a scalable user model to formulate future answers based on current answers that same user has processed. According to evaluation we carried out based on the TREC resources shows that proposed technology is promising and effective in question answering

Automatic Verbalisation of Biological Events

International audienceWe present a method for automatically generating descriptions of biological events encoded in the KB BIO 101 Knowledge base. In this knowledge base, events are concepts (e.g., RELEASE) related by role relations (e.g., AGENT, PATIENT, PATH, INSTRUMENT) to the concepts denoting their arguments (e.g., GATED-CHANNEL, VASCULAR-TISSUE, IRON). We propose a probabilistic, unsupervised method which extracts possible verbalisation frames from large biology specific domain corpora and uses probabilities both to select an appropriate frame given an event description and to determine the mapping between syntactic and semantic arguments. That is, probabilities are used to determine which event argument fills which syntactic function (e.g., subject, object) in the produced verbalisation. We evaluate our approach on a corpus of 336 event descriptions, provide a qualitative and quantitative analysis of the results obtained and discuss possible directions for further work