H. Sofia Pinto

An argumentation ontology for DIstributed, loosely-controlled and evolving engineering processes of oNTologies (DILIGENT)

A prerequisite to the success of the Semantic Web are shared ontologies which enable the seamless exchange of information between different parties. Engineering a shared ontology is a social process. Since its participants have slightly different views on the world, a harmonization effort requires discussing the resulting ontology. During the discussion, participants exchange arguments which may support or object to certain ontology engineering decisions. Experience from software engineering shows that tracking exchanged arguments can help users at a later stage to better understand the assumptions underlying the design decisions. Furthermore, as the constructed ontology becomes larger, ontology engineers might argue in a contradictory way without knowing so. In this paper we present an ontology which formalizes the main concepts which are used in an DILIGENT ontology engineering discussion and thus enables tracking arguments and allows for inconsistency detection. We provide an example which is drawn from experiments in an ontology engineering process to construct an ontology for knowledge management in our institute. Having constructed the ontology we also show how automated ontology learning algorithms could be taken as participants in the OE discussion. Hence, we enable the integration of manual, semi-automatic and automatic ontology creation approaches.

Cooking an ontology

An effective solution to the problem of extending a dialogue system to new knowledge domains requires a clear separation between the knowledge and the system: as ontologies are used to conceptualize information, they can be used as a means to improve the separation between the dialogue system and the domain information. This paper presents the development of an ontology for the cooking domain, to be integrated in a dialog system. The ontology comprehends four main modules covering the key concepts of the cooking domain – actions, food, recipes, and utensils – and three auxiliary modules – units and measures, equivalencies and plate types.

Ontology Engineering and Evolution in a Distributed World Using DILIGENT

Existing mature ontology engineering approaches are based on some basic assumptions that are often neglected in practice.

THE NEXT GENERATION OF SIMILARITY MEASURES THAT FULLY EXPLORE THE SEMANTICS IN BIOMEDICAL ONTOLOGIES

There is a prominent trend to augment and improve the formality of biomedical ontologies. For example, this is shown by the current effort on adding description logic axioms, such as disjointness. One of the key ontology applications that can take advantage of this effort is the conceptual (functional) similarity measurement. The presence of description logic axioms in biomedical ontologies make the current structural or extensional approaches weaker and further away from providing sound semantics-based similarity measures. Although beneficial in small ontologies, the exploration of description logic axioms by semantics-based similarity measures is computational expensive. This limitation is critical for biomedical ontologies that normally contain thousands of concepts. Thus in the process of gaining their rightful place, biomedical functional similarity measures have to take the journey of finding how this rich and powerful knowledge can be fully explored while keeping feasible computational costs. This manuscript aims at promoting and guiding the development of compelling tools that deliver what the biomedical community will require in a near future: a next-generation of biomedical similarity measures that efficiently and fully explore the semantics present in biomedical ontologies.

Ontology engineering revisited: an iterative case study

Existing mature ontology engineering approaches are based on some basic assumptions that are often violated in practice, in particular in the Semantic Web. Ontologies often need to be built in a decentralized way, ontologies must be given to a community in a way such that individuals have partial autonomy over them and ontologies have a life cycle that involves an iteration back and forth between construction/modification and use. While recently there have been some initial proposals to consider these issues, they lack the appropriate rigor of mature approaches. i.e. these recent proposals lack the appropriate depth of methodological description, which makes the methodology usable, and they lack a proof of concept by a long-lived case study. In this paper, we revisit mature and new ontology engineering methodologies. We provide an elaborate methodology that takes decentralization, partial autonomy and iteration into account and we demonstrate its proof-of-concept in a real-world cross-organizational case study.

Modeling contexts for business process oriented knowledge support

In this paper, we propose an organizational model to describe the execution of business activities. The model offers a dynamic, actor centered, context based and business process oriented perspective of the organization that explicitly addresses the information and collaboration requirements derived from human multi-tasking capabilities. Actors are approached as a network of contexts managed by an “operating system”. Three kinds of actors are defined; human, business process and organization actor. Two context types are introduced. An action context defines the specific behavior and information needs of a human actor performing a task under a given role, at particular time intervals. Interaction contexts support and regulate activity-related interactions among action contexts. This modeling approach seeks to facilitate a personalized, proactive and timely knowledge support to human business actors. We illustrate these ideas with working examples.

Distributed Engineering of Ontologies (DILIGENT)

Ontology engineering processes in truly distributed settings like the Semantic Web or global Peer-to-Peer systems may not be adequately supported by conventional, centralized ontology engineering methodologies. In this chapter, we present our work towards the DILIGENT methodology, which is intended to support domain experts in a distributed setting to engineer and evolve ontologies. We show partial results on how the DILIGENT process model has been applied in two case studies, in particular (1) in a computer science department where we investigated a fine-grained methodological approach for argumentation, and (2) in a virtual organizational setting in the tourism domain with the support of a Peer-to-Peer system.

Combining ontology engineering subprocesses to build a time ontology

In the Ontology Engineering area several processes to ease the time consuming and complex task of ontology building have been proposed. In this paper we describe how a Time ontology was built by means of reuse - in our case using a composition/integration approach - following an evolving prototyping life cycle. This particular process involved several complex subprocesses: knowledge acquisition and requirement specification using Natural Language techniques, reverse engineering, knowledge representation translation, technical evaluation. We show how they can all be combined and describe the techniques and best practices that were used. We discuss the interesting features of this process, namely effort required. Finally, we analyze what is changing in ontology building and compare this particular process with a modern software engineering approach, the Rational Unified Process.

Automatic Tag Suggestion Based on Resource Contents

Although social tagging systems are becoming increasingly popular, tagging is still usually a manual process. When publishing on a social tagging system, the user is asked for the tags he wishes to assign to the resource being made available. In this paper, we present an automatic tag suggester, Tess. Our system makes recommendations based only on the textual contents of the resource and is independent of existing tags, thus allowing the emergence of novel tags. The system was evaluated by a group of users and statistical measures were applied to infer its performance. Results show that the system is not only able to suggest many useful tags, but also to discover new and relevant tags, not suggested by any of the human users.

Reusing a Time Ontology

Ontologies are becoming crucial in several disparate areas, such as the Semantic Web or Knowledge Management. Ontology building is still more of an art than an engineering task. None of the available methodologies to build ontologies from scratch has been widely accepted. One cost effective way of building ontologies is by means of reuse. In this article we describe the development of an ontology of Time by means of reuse, following an evolving prototyping life cycle. This process involved several complex subprocesses: knowledge acquisition and requirement specification using Natural Language techniques, reverse engineering, knowledge representation translation, technical evaluation. As far as we know, this is the first time that all these processes have been combined together. We describe the techniques and best practices that were successfully used.