Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

Integration of heterogeneous data sources is a crucial step in big data analytics, although it creates ambiguity issues during mapping between the sources due to the variation in the query terms, data structure and granularity conflicts. However, there are limited researches on effective big data integration to address the ambiguity issue for big data analytics. This paper introduces a self-adaptive model for big data integration by exploiting the data structure during querying in order to mitigate and resolve ambiguities. An assessment of a preliminary work on the Geography and Quran dataset is reported to illustrate the feasibility of the proposed model that motivates future work such as solving complex query

Providing for Perspectives: The Role of Discourse Analysis in Ontology Concept Formulation and Development

Concept formulation and ontology development are problematic to achieve in complex social settings. Previously, we have proposed and illustrated a method to develop an ontology based on grounded theory, whereby the ontology is linked to the social processes involved. Further, distinct actors in the social setting assume perspectives that are often fundamentally different from each other. We have previously argued that perspectivism is a cogent theoretical explanation for the different emergent ontologies. However, a rigorous method for analysing the text, in order to identify these perspectives has been needed. In this paper we propose the identification of perspectives by using discourse analysis to bridge between term identification and clarification of perspectives. We have found that discourse analysis provides the structure and rigour required to establish the presence of perspectives, and that actors use metaphors and the genre of historical stories to bridge between, or link with, other perspectives. It is likely that identifying perspectives and the role of language in linking them will produce ontological modularity that is true to the social setting

RDF/S)XML Linguistic Annotation of Semantic Web Pages

Although with the Semantic Web initiative much research on web pages semantic annotation has already done by AI researchers, linguistic text annotation, including the semantic one, was originally developed in Corpus Linguistics and its results have been somehow neglected by AI. ..

Classifying Ellipsis in Dialogue: A Machine Learning Approach

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The relevance of perspectivism to the task of modularisation in ontology development

An ontology development methodology seeks to provide developers with established principles, processes, practices, methods and activities for developing ontologies (Gasevic et al., 2009). Diverse methodologies have been published for the development of ontologies, and have evolved, based on the diverse experiences of researchers and practitioners, and the development teams who surveyed the benefits and shortcomings of the available methodologies in order to determine the applicability of methodologies to particular contexts. An evaluation of existing ontology development methodologies has identified that the concept formulation process is not well defined, or based on rigorous processes (Castro et al., 2006; Winters &amp; Tolk, 2009). In order for the validity of the social realism of the actors in a social setting to be captured, the perspectives of each actor needs to be acknowledged and incorporated into the concept formulation process / framework. This paper demonstrates how consideration of perspectivism leads to a meaningful modularisation of the resultant ontology.<br /

Remarks on logic for process descriptions in ontological reasoning: A Drug Interaction Ontology case study

We present some ideas on logical process descriptions, using relations from the DIO (Drug Interaction Ontology) as examples and explaining how these relations can be naturally decomposed in terms of more basic structured logical process descriptions using terms from linear logic. In our view, the process descriptions are able to clarify the usual relational descriptions of DIO. In particular, we discuss the use of logical process descriptions in proving linear logical theorems. Among the types of reasoning supported by DIO one can distinguish both (1) basic reasoning about general structures in reality and (2) the domain-specific reasoning of experts. We here propose a clarification of this important distinction between (realist) reasoning on the basis of an ontology and rule-based inferences on the basis of an expert’s view