Abstract. As the Semantic Web is gaining momentum, more and more semantic data is available online. The second generation of Semantic Web applications already exploit this phenomenon by relying on this huge amount of semantic content. Looking at the requirements of these applications, we show that there is a need for an efficient access point to the Semantic Web, designed to take into account the semantic nature of the knowledge available online. However, because they rely on “classical Web ” techniques, existing solutions fail to fulfill this need. In this paper, we describe the design of Watson, a gateway for the Semantic Web, which has been guided by the requirements of Semantic Web applications and by lessons learnt from previous systems. We show how Watson exploits the strengths of semantic technologies to provide fundamental functionalities for a more suitable access to online knowledge. We also report on using these functionalities to analyze some of the characteristics of the content of the Semantic Web.

Abstract. The increased availability of online knowledge has led to the design of several algorithms that solve a variety of tasks by harvesting the Semantic Web, i.e., by dynamically selecting and exploring a multitude of online ontologies. Our hypothesis is that the performance of such novel algorithms implicitly provides an insight into the quality of the used ontologies and thus opens the way to a task-based evaluation of the Semantic Web. We have investigated this hypothesis by studying the lessons learnt about online ontologies when used to solve three tasks: ontology matching, folksonomy enrichment, and word sense disambiguation. Our analysis leads to a suit of conclusions about the status of the Semantic Web, which highlight a number of strengths and weaknesses of the semantic information available online and complement the findings of other analysis of the Semantic Web landscape. 1

In this paper we present a new version of OntoGen system for semi-automatic data-driven ontology construction. The system is based on a novel ontology learning framework which formalizes and extends the role of machine learning and text mining algorithms used in the previous version. List of new features includes extended number of supported ontology formats (RDFS and OWL), supervised methods for concept discovery (based on Active Learning), adding of new instances to ontology and improved user interface (based on comments from the users).

The increasing number of ontologies on the Web and the appearance of large scale ontology repositories has brought the topic of ontology selection in the focus of the semantic web research agenda. Our view is that ontology evaluation is core to ontology selection and that, because ontology selection is performed in an open Web environment, it brings new challenges to ontology evaluation.

To achieve better interoperability among intelligent applications, and to relieve knowledge engineers from the burden of developing ontologies from scratch, it is critical to reuse ontologies. However, there are two main reasons why the reuse of ontologies is rare: (1) current ontology repositories allow only simple keyword-based search facilities, and (2) even when a user finds an ontology, the information about the ontology quality and (re)usability is not available.

Knowledge artifacts that have been labeled as ontologies have many different qualities and intended outcomes. This is particularly true of bio-ontologies where high demand has led to a rapid growth in the number of these artifacts. Good communication between the human agents involved in the life cycle of ontologies is essential for the ontologist to encode the right knowledge in the ontology. Not only this, but it should be encoded such that subsequent retrieval of the knowledge from the ontology by any agent can be clear and precise. The ontologist can encode ontological statements, for interpretation by a computer agent, or meta-ontological statements, for interpretation by human agents. We consider how the current communication between agents and ontologies produces drawbacks that add to the considerable overheads associated with ontology development. We describe the processes of communication between human agents and ontologies as Ontology Comprehension. We then suggest how these processes could be augmented, particularly with the use of Web 2.0 ideas. By exposing and enhancing the social interactions involved in ontology comprehension, development overheads are potentially reduced and the prospect of ontology sharing and reuse is improved.

In this paper we explore the need to understand the human-behavioral factors within an organization's information security management processes. We frame this investigation around development of an information security ontology. This ontology is intended for use within organizations that aim not only to maintain compliance with external standards, but also to consider and adjust the attitude towards security as exhibited by those within the organization. We provide an ontology that combines information security standards (in this case ISO27002) and representation of the human-behavioral implications of information security management decisions. Our ontology explicitly represents the human-behavioral concerns attached to specific security processes and policy decisions. As such it encourages consideration of the security behavior of individuals towards technical security controls. We demonstrate use of our ontology with an applied example concerning management of an organization's password policy. This example illustrates how password configuration may be perceived by individuals within the organization, and how this perception alters their behavior and consequently the attitude to information security in the workplace.

Abstract: The paper presents PSI 1 Meta-Ontology – an upper level lightweight descriptive model for the set of the Core ontologies of PSI Suite. While PSI Suite is an interlinked modular library of ontologies describing the domain of engineering design performance in microelectronics, PSI Meta-Ontology is more domain-independent. It is an upper-level model of stateful creative dynamic processes, pro-active agents, and objects situated in nested dynamic environments based on formal representation of time, events, and happenings. It may be used as an upper-level theory for domain ontologies in different application domains having common features. PSI Meta-Ontology is designed ontological foundations and common sense. It is also used as semantic “glue” for bridging PSI ontologies with other theories, widely accepted in the domains where processes, states, and participating objects are the major entities. These mappings and semantic bridges are supposed to ease the commitment of potential users to PSI Suite. PSI Meta-Ontology is also used as a “proxy ” for different kinds of evaluation of PSI ontologies in frame of our “shaker modeling ” methodology for ontology refinement. 1

Abstract: The paper presents the results of mapping of PSI Ontologies family to the foundational ontologies: WordNet, SUMO and DOLCE. The two main outcomes of the presented research are: reported manual technique may be used as initial evaluation of ontology claiming to be gold standard for a new domain; and: usage of mentioned foundational ontologies for alignment of ontologies family of a given domain has shown differences between SUMO and DOLCE, two formal upper-level ontologies of common sense knowledge. The research reported was performed in the frame of our PSI project 1. 1

Abstract. Mapping and merging of multiple ontologies to produce consistent, coherent and correct merged global ontology is an essential process to enable heterogeneous multi-vendors semantic-based systems to communicate with each other. To generate such a global ontology automatically, the individual ontologies must be free of (all types of) errors. We have observed that the present error classification does not include all the errors. This paper extends the existing error classification (Inconsistency, Incompleteness and Redundancy) and provides a discussion about the consequences of these errors. We highlight the problems that we faced while developing our DKP-OM, ontology merging system and explain how these errors became obstacles in efficient ontology merging process. It integrates the ontological errors and design anomalies for content evaluation of ontologies under one framework. This framework helps ontologists to build semantically correct ontology free from errors that enables effective and automatic ontology mapping and merging with lesser user intervention.