Maria Vargas-Vera

Reflections on the Second Life Platform used in the Development of a Virtual University Campus

This paper presents the authors experiences building a virtual campus named Deep Think designed to support a postgraduate program named MPhil. The MPhil is a formal and recognized Open University degree delivered to a distance. The virtual campus integrates Second Life, Moodle and several Web 2.0 technologies like Elluminate online conferencing tool, MyStuff e-Portfolio Skype and Ning Social network. This integration between second life and web 2.0 technologies has provoked the enthusiasm of tutors at the Open University which saw the benefit of using DeepThink in their courses. Finally, the author discusses experiences on second life and its limitations.

Establishing agent trust for contradictory evidence by means of fuzzy voting model: An ontology mapping case study

This paper introduces a novel trust assessment formalism for contradicting evidence in the context of multi-agent ontology mapping. Evidence combination using the Dempster rule tend to ignore contradictory evidence and the contemporary approaches for managing these conflicts introduce additional computation complexity i.e. increased response time of the system. On the Semantic Web, ontology mapping systems that need to interact with end users in real time cannot afford prolonged computation. In this work, we have made a step towards the formalisation of eliminating contradicting evidence, to utilise the original Dempsters combination rule without introducing additional complexity. Our proposed solution incorporates the fuzzy voting model to the Dempster-Shafer theory. Finally, we present a case study where we show how our approach improves the ontology mapping problem.

Methodology for the Elaboration of Quizzes using Propositional Logic Calculus in an E-Learning Environment

This paper introduces the use of propositional logic calculus in the elaboration of educational quizzes to assess the level understanding of students in a specific theme of their courses. The technique introduced in this paper goes beyond multiple-choice quizzes. The technique requires several steps like a to give a phrase, b to re-order words of the given phrase in order to form a propositional logic formula, c to make use of background knowledge for performing substitutions, d to answer questions from one of the person in the team, e to change synonyms/antonyms if this is feasible, f to perform actions in order to give value to both or at least one operand of the logic formula and g to conclude the final answer of the logic formula true or false depending of the logic values of the operands in the logic formula. As a working example, the author shows a quiz for universal history, however, the same technique could be used to assess students in different courses.

State of the Art on Ontology Alignment

Ontology mapping as a semantic data integration approach has evolved from traditional data integration solutions. The core problems and open issues related to early data integration approaches are also applicable to ontology mapping on the Semantic Web community. Therefore, in this review the authors present the related literature, starting from the traditional data integration approaches, in order to highlight the evolution of data integration from the early approaches. Once the roots of semantic data integration have been presented, the authors proceed to introduce the state-of-the-art of the ontology mappings systems including the early approaches and the systems that can be compared through the Ontology Alignment Initiative OAEI.

Dealing with Contradictory Evidence Using Fuzzy Trust in Semantic Web Data

Term similarity assessment usually leads to situations where contradictory evidence support has different views concerning the meaning of a concept and how similar it is to other concepts. Human experts can resolve their differences through discussion, whereas ontology mapping systems need to be able to eliminate contradictions before similarity combination can achieve high quality results. In these situations, different similarities represent conflicting ideas about the interpreted meaning of the concepts. Such contradictions can contribute to unreliable mappings, which in turn worsen both the mapping precision and recall. In order to avoid including contradictory beliefs in similarities during the combination process, trust in the beliefs needs to be established and untrusted beliefs should be excluded from the combination. In this chapter, we propose a solution for establishing fuzzy trust to manage belief conflicts using a fuzzy voting model.

The Implementation of DSSim: A Multi-Agent Ontology Mapping System

This paper presents the decisions taken during the implementation of DSSim (DSSim stands for Similarity based on Dempster-Shafer) our multi-agent ontology mapping system. It describes several types of agents and their roles in the DSSim architecture. These agents are mapping agents which are able to perform either semantic or syntactic similarity. Our architecture is generic as no mappings need to be learned in advance and it could be easily extended by adding new mapping agents in the framework. The new added mapping agents could run different similarity algorithms (either semantic or syntactic). In this way, DSSim could assess which algorithm has a better performance. Additionally, this paper presents the algorithms used in our ontology alignment system DSSim.

Data Integration Framework: A Children and Parents Cohort Case Study

This paper presents a proposal for a data integration framework. The purpose of the framework is to locate automatically records of participants from the ALSPAC database (Avon Longitudinal Study of Parents and Children) within its counterpart GPRD database (General Practice Research Database). The ALSPAC database is a collection of data from children and parents from before birth to late puberty. This collection contains several variables of interest for clinical researchers but we concentrate in asthma as a golden standard for evaluation of asthma has been made by a clinical researcher. The main component of the framework is a module called Mapper which locates similar records and performs record linkage. The mapper contains a library of similarity measures such Jaccard, Jaro-Winkler, Monge-Elkan, MatchScore, Levenstein and TFIDF similarity. Finally, the author evaluates the approach on quality of the mappings.

Methodology for Record Linkage: A Medical Domain Case Study

This paper presents a methodology for linking records from several sources each source might contain, missing information. This assumption of missing values has been made, without loss of generality, as the authors has observed that missing information is part of the nature of data in the health domain and also in other domains such as social sciences. The authors methodology is an attempt to deal with the linkage of records of the same patient in several databases. The first phase in her methodology is called homogenization. The homogenization of the databases/datasets is performed by applying a method which fills-in the missing values with the predicted values. The second phase of her methodology is called linking of records. It assesses the similarity between records and implements the linkage of the pairs of records with high level of similarity. Finally, the author presents an evaluation of our methodology. The evaluation of the homogenization phase was carried out using multinomial regression while, the evaluation of the aggregated similarities were performed using Jaccard, Jaro-Winkler and Monge-Elkan similarity metrics.

Experiences on the Evaluation of DSSim: A Multi-Agent Ontology Mapping System

This paper presents a comprehensive evaluation of DSSim DSSim stands for Similarity based on Dempster-Shafer, our ontology alignment system. The authors participated several years in the annual evaluation defined by the Ontology Alignment Initiative OAEI. Each year their DSSim was evolved and participated in more difficult tracks defined by the Ontology Alignment Initiative. In fact, DSSim obtained exceptional results in the OAEI-2008 Evaluation. In this evaluation OAEI-2008, DSSim participated on all given tracks namely, benchmark, anatomy, fao, directory, mldirectory, library, very large crosslingual resources and conference. The challenges presented by each track were addressed by the DSSim team.

Challenges in Ontology Alignment and Solution to the Contradictory Evidence Problem

This paper introduces the main challenges when performing ontology mapping. These challenges vary from representational issues to conflicting information. For the latest category of challenges, namely conflicting information, we have designed a method to deal with the uncertainty on mappings. Then, our main contribution is the design and development of novel trust assessment formalism for handling contradicting evidence in the context of ontology mapping. The novelty of our proposed solution relays in the incorporation of the fuzzy voting model to the Dempster-Shafer theory. Finally, we present a case study where we show how our approach improves the ontology mapping problem.