knOWLearn: a reuse-based approach for building ontologies in a semi-automatic way

Abstract. In this poster paper we present an overview of knOWLearn, a novel approach for building domain ontologies in a semi-automatic fashion. Keywords: Domain ontology building, ontology learning, ontology reuse 1 Introduction Ontologies are useful mechanism for representing knowledge, containing concepts and relationships about the domain of interest. Developing ontologies in a manual fashion is a complex and time consuming process, which implies the participation of domain experts and ontology engineers. For this reason, the definition of approaches to semi-automatically build domain ontologies, what is called ontology learning, is one of the main research topics in Ontology Engineering. However, current ontology learning approaches from textual documents have results not completely satisfactory The knOWLearn Approach Our approach to build domain ontologies consists of five main phases (see (1) Term Extraction, this phase performs the extraction of relevant domain terms from text documents. FTC algorithm [2], to cluster documents, has been extended to obtain simple domain terms (of a single word). When the terms have been obtained, the most frequent n-grams (n={2,3}) containing such terms are searched in the input documents. These n-grams are the most relevant multi-words for the domain

Experiential Observations: an Ontology Pattern-based Study on Capturing the Potential Content within Evidences of Experiences

Modelling the knowledge behind human experiences is a complex process: it should take into account, among others, the activities performed, human observations, and the documentation of the evidence. To represent this knowledge in a declarative way means to support data interoperability in the context of cultural heritage artefacts, as linked datasets on experience documentation have started to appear. With this objective in mind, we describe a study based on an Ontology Design Pattern for modelling experiences through observations, which are considered indirect evidence of a mental process (i.e., the experience). This pattern highlights the structural differences between types of experiential documentation, such as diaries and social media, providing a guideline for the comparability between different domains and for supporting the construction of heterogeneous datasets based on an epistemic compatibility. We have performed not only a formal evaluation over the pattern, but also an assessment through a series of case studies. This approach includes a) the analysis of interoperability among two case studies (reading through social media and historical sources); b) the development of an ontology for collecting evidences of reading, which reuses the proposed pattern; and c) the inspection of experience in humanities datasets

Knowledge Engineering and Knowledge Management - EKAW 2016 Satellite Events, EKM and Drift-an-LOD, Bologna, Italy, November 19\u201323, 2016, Revised Selected Papers

This book contains the best selected papers of two Satellite Events held at the 20th International Conference on Knowledge Engineering and Knowledge Management, EKAW 2016, in November 2016 in Bologna, Italy: The Second International Workshop on Educational Knowledge Management, EKM 2016, and the First Workshop: Detection, Representation and Management of Concept Drift in Linked Open Data, Drift-an-LOD 2016. The 6 revised full papers included in this volume were carefully reviewed and selected from the 13 full papers that were accepted for presentation at the conference from the initial 82 submissions. This volume also contains the 37 accepted contributions for the EKAW 2016 tutorials, demo and poster sessions, and the doctoral consortium. The special focus of this year's EKAW was "evolving knowledge", which concerns all aspects of the management and acquisition of knowledge representations of evolving, contextual, and local models. This includes change management, trend detection, model evolution, streaming data and stream reasoning, event processing, time-and space dependent models, contextual and local knowledge representations with a special emphasis on the evolvability and localization of knowledge and the correct usage of these limits

Trends in Practical Applications of Agents and Multiagent Systems11th International Conference on Practical Applications of Agents and Multi-Agent Systems /

XVI, 210 p. 63 illus.online resource