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

MINOR HISTORICAL CENTRES ONTOLOGY ENRICHMENT AND POPULATION: AN HAMLET CASE STUDY

The main topic of this work focuses on the semantic, historical and spatial documentation of Minor Historical Centres (MHC) with a focus on (semi-abandoned alpine) hamlets. The key point is the possibility to standardise spatial information in the domain of MHC and their related cultural, architectural, built and landscape heritage. This work analyses the notions of historical centre and ancient area, which took different meanings and evolved over the centuries. MHC are historical part of cities, villages and hamlets (urban, rural, minor or abandoned) with cultural, social and economic values. Thus, MHC need to be preserved, documented and safeguarded. The spatial and semantic documentation is a fundamental tool for increasing their knowledge. In these places, many actors and stakeholders are involved in different activities, and for this reason, they need to share common knowledge and use a unique language. In this regard, spatial ontology is of relevant interest and usability. Ontologies are conceptual structures that formalise specific knowledge and create a unique and standard thesaurus that ensures semantic interoperability. This paper is part of a PhD research targeted at developing an ontology containing helpful information to manage, share and collect data on MHC due to the lack of an interoperable structure to formalise such knowledge. The main aim is to populate and enrich the already developed ontological structure with data of a mountain semi-abandoned hamlet: Pomieri. The methodological workflow is validated, enriching and populating the ontology, adding classes and instances with information and unstructured data of a real data case study

A Domain-Adaptable Heterogeneous Information Integration Platform: Tourism and Biomedicine Domains.

In recent years, information integration systems have become very popular in mashup-type applications. Information sources are normally presented in an individual and unrelated fashion, and the development of new technologies to reduce the negative effects of information dispersion is needed. A major challenge is the integration and implementation of processing pipelines using different technologies promoting the emergence of advanced architectures capable of processing such a number of diverse sources. This paper describes a semantic domain-adaptable platform to integrate those sources and provide high-level functionalities, such as recommendations, shallow and deep natural language processing, text enrichment, and ontology standardization. Our proposed intelligent domain-adaptable platform (IDAP) has been implemented and tested in the tourism and biomedicine domains to demonstrate the adaptability, flexibility, modularity, and utility of the platform. Questionnaires, performance metrics, and A/B control groups’ evaluations have shown improvements when using IDAP in learning environmentspost-print2139 K

Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances

By providing interoperability and shared meaning across actors and domains, lightweight domain ontologies are a cornerstone technology of the Semantic Web. This chapter investigates evidence sources for ontology learning and describes a generic and extensible approach to ontology learning that combines such evidence sources to extract domain concepts, identify relations between the ontology’s concepts, and detect relation labels automatically. An implementation illustrates the presented ontology learning and relation labeling framework and serves as the basis for dis- cussing possible pitfalls in ontology learning. Afterwards, three use cases demonstrate the usefulness of the presented framework and its application to real-world problems

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

From Text to Knowledge with Graphs: modelling, querying and exploiting textual content

This paper highlights the challenges, current trends, and open issues related to the representation, querying and analytics of content extracted from texts. The internet contains vast text-based information on various subjects, including commercial documents, medical records, scientific experiments, engineering tests, and events that impact urban and natural environments. Extracting knowledge from this text involves understanding the nuances of natural language and accurately representing the content without losing information. This allows knowledge to be accessed, inferred, or discovered. To achieve this, combining results from various fields, such as linguistics, natural language processing, knowledge representation, data storage, querying, and analytics, is necessary. The vision in this paper is that graphs can be a well-suited text content representation once annotated and the right querying and analytics techniques are applied. This paper discusses this hypothesis from the perspective of linguistics, natural language processing, graph models and databases and artificial intelligence provided by the panellists of the DOING session in the MADICS Symposium 2022