AQUA: an ontology driven question answering system

This paper describes AQUA our question answering over the Web. AQUA was designed to work over heterogeneous sources. This means that AQUA is equipped to work as closed domain and in addition to open-domain question answering. As a first instance, AQUA tries to answer a question using a Knowledge base. If a query cannot be satisfied over a knowledge base/database. Then, AQUA tries to find an answer on web pages (i.e. it uses as corpus the internet as resource). Our system uses NLP (Natural Language Processing), First order logic and Information Extraction technologies. AQUA has been tested using an ontology which describes academic life. Keywords Ontologies, Information Extraction, Machine Learnin

Introducing fuzzy trust for managing belief conflict over semantic web data

Interpreting Semantic Web Data by different human experts can end up in scenarios, where each expert comes up with different and conflicting ideas what a concept can mean and how they relate to other concepts. Software agents that operate on the Semantic Web have to deal with similar scenarios where the interpretation of Semantic Web data that describes the heterogeneous sources becomes contradicting. One such application area of the Semantic Web is ontology mapping where different similarities have to be combined into a more reliable and coherent view, which might easily become unreliable if the conflicting beliefs in similarities are not managed effectively between the different agents. In this paper we propose a solution for managing this conflict by introducing trust between the mapping agents based on the fuzzy voting model

deep|think: A Second Life environment for part-time research students at a distance

This paper reports on the design of a Second Life campus for a new innovative post-graduate research programme at the Open University, UK, a world leader in supported distance higher education. The programme, launched in October 2009, is a part- time Master of Philosophy (MPhil) to be delivered at a distance, supported by a blend of synchronous and asynchronous Internet technologies. This paper briefly discusses the pedagogical thinking behind the Second Life campus, and the way the implementation was designed to meet the pedagogy. The paper also reports on the outcome of an early evaluation we have conducted

Event recognition on news stories and semi-automatic population of an ontology

This paper describes a system which recognizes events on news stories. Our system classifies stories and populates a hand-crafted ontology with new instances of classes defined in it. Currently, our system recognizes events which can be classified as belonging to a single category and it also recognizes overlapping events within one article (more than one event is recognized). In each case, the system provides a confidence value associated to the suggested classification. Our system uses Information Extraction and Machine Learning technologies. The system was tested using a corpus of 200 news articles from an archive of electronic news stories describing the academic life of the Knowledge Media (KMi). In particular, these news stories describe events such as a project award, publications, visits, etc.

Towards an Automatic Semantic Data Integration: Multi-agent Framework Approach

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Developing research degrees online

Research degrees have been changing radically in the last twenty years, with an extensive body of work accumulated on improving the practice of research degrees and on developing skills for independent researchers. However, most of this work focuses on full-time residential research degrees, and little attention has been paid to part-time research degrees at a distance. This paper presents a novel research degree, the Virtual MPhil in Computing, offered by The Open University (UK), supported by a blend of technologies, and designed to address this gap. We discuss the support it provides for the development of student community, research dialogue and progress monitoring of distance research students

DSSim-ontology mapping with uncertainty

This paper introduces an ontology mapping system that is used with a multi agent ontology mapping framework in the context of question answering. Our mapping algorithm incorporates the Dempster Shafer theory of evidence into the mapping process in order to improve the correctness of the mapping. Our main objective was to assess how applying the belief function can improve correctness of the ontology mapping through combining the similarities which were originally created by both syntactic and semantic similarity algorithms. We carried out experiments with the data sets of the Ontology Alignment Evaluation Initiative 2006 which served as a test bed to assess both the strong and weak points of our system. The experiments confirm that our algorithm performs well with both concept and property names

DSSim - managing uncertainty on the Semantic Web

Managing uncertainty on the Semantic Web can potentially improve the ontology mapping precision which can lead to better acceptance of systems that operate in this environment. Further ontology mapping in the context of Question Answering can provide more correct results if the mapping process can deal with uncertainty effectively that is caused by the incomplete and inconsistent information used and produced by the mapping process. In this paper we introduce our algorithm called “DSSim” and describe the improvements that we have made compared to OAEI 2006

Ontology Mapping with domain specific agents in the AQUA Question Answering System

This paper describes a domain specific multi-agent ontology-mapping solution in the AQUA query answering system. In order to incorporate uncertainty inherent to the mapping process, the system uses the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how specialized agents with partial local knowledge of the particular domain achieve ontology mapping without creating global or reference ontology. Our approach is particularly fit for a query-answering scenario, where answer needs to be created in real time to satisfy a query posed by the user

Uncertainty handling in the context of ontology mapping for question answering

This paper describes a framework for integrating similarity measures and Dempster-Shafer belief functions for data integration in the context of multi agent ontology mapping. In order to incorporate uncertainty inherent to the ontology mapping process, we propose utilizing the Dempster-Shafer model for dealing with incomplete and uncertain information produced during the mapping. A novel approach is presented how assessing belief can influence the similarities originally created by both syntactic and semantic similarity algorithms. Our approach is an alternative to the classical Bayesian reasoning which has been investigated for improving the efficiency of creating ontology mappings