Konstantinos Kotis

Human-centered ontology engineering: The HCOME methodology

The fast emergent and continuously evolving areas of the Semantic Web and Knowledge Management make the incorporation of ontology engineering tasks in knowledge-empowered organizations and in the World Wide Web more than necessary. In such environments, the development and evolution of ontologies must be seen as a dynamic process that has to be supported through the entire ontology life cycle, resulting to living ontologies. The aim of this paper is to present the Human-Centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day activities and shape their information space by being actively involved in the ontology life cycle. The paper also demonstrates the Human Centered ONtology Engineering Environment, HCONE, which can effectively support this methodology.

The HCONE approach to ontology merging

Existing efforts on ontology mapping, alignment and merging vary from methodological and theoretical frameworks, to methods and tools that support the semi-automatic coordination of ontologies. However, only latest research efforts ”touch” on the mapping/merging of ontologies using the whole breadth of available knowledge. This paper aims to thoroughly describe the HCONE approach on ontology merging. The approach described is based on (a) capturing the intended informal interpretations of concepts by mapping them to WordNet senses using lexical semantic indexing, and (b) exploiting the formal semantics of concepts’ definitions by means of description logics’ reasoning services.

HCOME: a tool-supported methodology for engineering living ontologies

The fast emergent areas of the Semantic Web and knowledge management push researchers to new efforts concerning ontology engineering. The development of ontologies must be seen as a dynamic process that in most of the cases starts with an initial rough ontology that is later revised, refined, enriched, populated and filled in with details. Ontology evolution has to be supported through the entire ontology lifecycle, resulting to a living ontology. The aim of this paper is to present the Human-Centered Ontology Engineering Methodology (HCOME) for the development and evaluation of living ontologies in the context of communities of knowledge workers. The methodology aims to empower knowledge workers to continuously manage their formal conceptualizations in their day-to-day tasks. We conjecture that this methodology can only be effectively supported by eclectic human-centered ontology management environments, such as the HCONE and SharedHCONE.

Ontology Matching: Current Status, Dilemmas and Future Challenges

Ontology matching is still one of the hottest topics of Semantic Web research. The aim of this position statement is three-fold. Firstly, to briefly update the research community about the ldquowhere are we nowrdquo in ontology matching. Secondly, to trigger discussion on dilemmas and critical questions as these were recently identified in the latest ontology-matching-related research events. Thirdly, to comment on visible challenges that may influence the future of this hot topic and consequently the Semantic Web research in general, pointing on the ldquowhere shall we gordquo in the near future.

Capturing Semantics Towards Automatic Coordination of Domain Ontologies

Existing efforts on ontology mapping, alignment and merging vary from methodological and theoretical frameworks, to methods and tools that support the semi-automatic coordination of ontologies. However, only latest research efforts “touch” on the mapping /merging of ontologies using the whole breadth of available knowledge. Addressing this issue, the work presented in this paper is based on the HCONE-merge approach that makes use of the intended informal interpretations of concepts by mapping them to WordNet senses using lexical semantic indexing (LSI). Our aim is to explore the level of human involvement required for mapping concepts of the source ontologies to their intended interpretations. We propose a series of methods for ontology mapping/merging with varying degrees of human involvement and evaluate them experimentally. We conclude that, although an effective fully automated process is not attainable, we can reach a point where the process of ontology mapping/merging can be carried out efficiently with minimum human involvement.

Facilitating dialogue - using semantic web technology for eparticipation

In this paper we describe the application of various Semantic Web technologies and their combination with emerging Web 2.0 use patterns in the eParticipation domain and show how they are used in an operational system for the Regional Government of the Prefecture of Samos, Greece. We present parts of the system that are based on Semantic Web technology and how they are merged with a Web 2.0 philosophy and explain the benefits of this approach, as showcased by applications for annotating, searching, browsing and cross-referencing content in eParticipation communities.

On Supporting HCOME-3O Ontology Argumentation Using Semantic Wiki Technology

To support the sharing of consistently evolved and living ontologies within and across different communities, HCOME-3O framework has been recently proposed. The framework introduces a set of three (meta-) ontologies for capturing the meta-information that is necessary for interlinking, sharing, and combining knowledge among the parties involved in a collaborative (domain) ontology engineering process. Although a prototype software (namely HCONE) based on this framework has being developed, collaborative tasks embedded in the HCOME methodology such as the ontology argumentation could be alternatively designed using open and Web community-driven (collective intelligence-based) technologies. In this short paper we state our position that the existing technology used to develop a Semantic Wiki (and its extensions) can be re-used in HCOME-3O-based tools in order to support Web community-driven collaborative ontology engineering tasks.

Aligning Smart and Control Entities in the IoT

In our latest work, the problem of semantic interoperability for interconnected and semantically coordinated smart/control entities in a Semantic Web of Things has been explicated and a framework for Semantic Smart Gateways (SSGF) has been proposed. This paper aims to report on a proof-of-concept implementation of the core component of this framework which is the automatic alignment of smart/control entities’ semantic descriptions. More specific, the paper reports on a recent approach towards implementing a configurable, multilingual and synthesis-based ontology alignment tool that has been evaluated by the SEALS and OAEI initiatives.

Automatic generation of tests from domain and multimedia ontologies

The aim of this article is to present an approach for generating tests in an automatic way. Although other methods have been already reported in the literature, the proposed approach is based on ontologies, representing both domain and multimedia knowledge. The article also reports on a prototype implementation of this approach, which automatically creates tests using the Semantic Web standard technology OWL (Ontology Web Language) as well as proper annotations of images. The proposed approach is independent of specific domain characteristics, since question items are generated according to generic ontology-based strategies. In the presented prototype implementation, simple natural language generation techniques are used to project the items in the tests.

Learning Useful Kick-off Ontologies from Query Logs: HCOME Revised

Ontology engineering has been fully or partially practiced by knowledge engineers or knowledge workers, towards delivering either fully fledged conceptualizations of domains or providing lightweight ontology versions for less demanding but more frequent knowledge tasks. Domain-specific information can be shaped into ontologies either manually or (semi-)automatically using ontology learning techniques. The aim of the paper is to present a novice ontology learning approach that automatically constructs kick-off and useful ontologies from query logs. We place tasks related to the proposed learning approach in all phases of an ontology engineering life-cycle. By providing knowledge workers a useful kick-off ontology that is automatically built from “their needs (i. e. users’ search interests)” in order to address “their needs (i. e. use of the kick-off ontology to query data precisely)”, an approach that contributes as an incentive in the semantic content creation bottleneck is introduced.