Towards automated knowledge-based mapping between individual conceptualisations to empower personalisation of Geospatial Semantic Web

Geospatial domain is characterised by vagueness, especially in the semantic disambiguation of the concepts in the domain, which makes defining universally accepted geo- ontology an onerous task. This is compounded by the lack of appropriate methods and techniques where the individual semantic conceptualisations can be captured and compared to each other. With multiple user conceptualisations, efforts towards a reliable Geospatial Semantic Web, therefore, require personalisation where user diversity can be incorporated. The work presented in this paper is part of our ongoing research on applying commonsense reasoning to elicit and maintain models that represent users' conceptualisations. Such user models will enable taking into account the users' perspective of the real world and will empower personalisation algorithms for the Semantic Web. Intelligent information processing over the Semantic Web can be achieved if different conceptualisations can be integrated in a semantic environment and mismatches between different conceptualisations can be outlined. In this paper, a formal approach for detecting mismatches between a user's and an expert's conceptual model is outlined. The formalisation is used as the basis to develop algorithms to compare models defined in OWL. The algorithms are illustrated in a geographical domain using concepts from the SPACE ontology developed as part of the SWEET suite of ontologies for the Semantic Web by NASA, and are evaluated by comparing test cases of possible user misconceptions

Applying spatial reasoning to topographical data with a grounded geographical ontology

Grounding an ontology upon geographical data has been pro- posed as a method of handling the vagueness in the domain more effectively. In order to do this, we require methods of reasoning about the spatial relations between the regions within the data. This stage can be computationally expensive, as we require information on the location of points in relation to each other. This paper illustrates how using knowledge about regions allows us to reduce the computation required in an efficient and easy to understand manner. Further, we show how this system can be implemented in co-ordination with segmented data to reason abou

Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects

OntoTouTra: tourist traceability ontology based on big data analytics

Tourist traceability is the analysis of the set of actions, procedures, and technical measures that allows us to identify and record the space–time causality of the tourist’s touring, from the beginning to the end of the chain of the tourist product. Besides, the traceability of tourists has implications for infrastructure, transport, products, marketing, the commercial viability of the industry, and the management of the destination’s social, environmental, and cultural impact. To this end, a tourist traceability system requires a knowledge base for processing elements, such as functions, objects, events, and logical connectors among them. A knowledge base provides us with information on the preparation, planning, and implementation or operation stages. In this regard, unifying tourism terminology in a traceability system is a challenge because we need a central repository that promotes standards for tourists and suppliers in forming a formal body of knowledge representation. Some studies are related to the construction of ontologies in tourism, but none focus on tourist traceability systems. For the above, we propose OntoTouTra, an ontology that uses formal specifications to represent knowledge of tourist traceability systems. This paper outlines the development of the OntoTouTra ontology and how we gathered and processed data from ubiquitous computing using Big Data analysis techniquesThis research was financially supported by the Ministry of Science, Technology, and Innovation of Colombia (733-2015) and by the Universidad Santo Tomás Seccional Tunja

Requirements-oriented methodology for evaluating ontologies

Ontologies play key roles in many applications today. Therefore, whether using a newly-specified ontology or an existing ontology for use in its target application, it is important to determine the suitability of an ontology to the application at hand. This need is addressed by carrying out ontology evaluation, which determines qualities of an ontology using methodologies, criteria or measures. However, for addressing the ontology requirements from a given application, it is necessary to determine what the appropriate set of criteria and measures are. In this thesis, we propose a Requirements-Oriented Methodology for Evaluating Ontologies (ROMEO). ROMEO outlines a methodology for determining appropriate methods for ontology evaluation that incorporates a suite of existing ontology evaluation criteria and measures. ROMEO helps ontology engineers to determine relevant ontology evaluation measures for a given set of ontology requirements by linking these requirements to existing ontology evaluation measures through a set of questions. There are three main parts to ROMEO. First, ontology requirements are elicited from a given application and form the basis for an appropriate evaluation of ontologies. Second, appropriate questions are mapped to each ontology requirement. Third, relevant ontology evaluation measures are mapped to each of those questions. From the ontology requirements of an application, ROMEO is used to determine appropriate methods for ontology evaluation by mapping applicable questions to the requirements and mapping those questions to appropriate measures. In this thesis, we perform the ROMEO methodology to obtain appropriate ontology evaluation methods for ontology-driven applications through case studies of Lonely Planet and Wikipedia. Since the mappings determined by ROMEO are dependent on the analysis of the ontology engineer, the validation of these mappings is needed. As such, in addition to proposing the ROMEO methodology, a method for the empirical validation of ROMEO mappings is proposed in this thesis. We report on two empirical validation experiments that are carried out in controlled environments to examine the performance of the ontologies over a set of tasks. These tasks vary and are used to compare the performance of a set of ontologies in the respective experimental environment. The ontologies used vary on a specific ontology quality or measure being examined. Empirical validation experiments are conducted for two mappings between questions and their associated measures, which are drawn from case studies of Lonely Planet and Wikipedia. These validation experiments focus on mappings between questions and their measures. Furthermore, as these mappings are application-independent, they may be reusable in subsequent applications of the ROMEO methodology. Using a ROMEO mapping from the Lonely Planet case study, we validate a mapping of a coverage question to the F-measure. The validation experiment carried out for this mapping was inconclusive, thus requiring further analysis. Using a ROMEO mapping from the Wikipedia case study, we carry out a separate validation experiment examining a mapping between an intersectedness question and the tangledness measure. The results from this experiment showed the mapping to be valid. For future work, we propose additional validation experiments for mappings that have been identified between questions and measures

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Implementation of a Knowledge Management Methodology based on Ontologies :Case of Tourism

in this paper, we suggest a methodology of knowledge management that makes use of the new possibilities offered by semantic web technologies and covers the various stages of the project life cycle. In fact, with this new vision of ontologies and semantic web, it is important to provide a strong methodological support in order to develop complex ontology-based systems

Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research

This paper reviews the published articles on eTourism in the past 20 years. Using a wide variety of sources, mainly in the tourism literature, this paper comprehensively reviews and analyzes prior studies in the context of Internet applications to Tourism. The paper also projects future developments in eTourism and demonstrates critical changes that will influence the tourism industry structure. A major contribution of this paper is its overview of the research and development efforts that have been endeavoured in the field, and the challenges that tourism researchers are, and will be, facing