Abstract. While current approaches to ontology mapping produce good results by mainly relying on label and structure based similarity measures, there are several cases in which they fail to discover important mappings. In this paper we describe a novel approach to ontology mapping, which is able to avoid this limitation by using background knowledge. Existing approaches relying on background knowledge typically have one or both of two key limitations: 1) they rely on a manually selected reference ontology; 2) they suffer from the noise introduced by the use of semi-structured sources, such as text corpora. Our technique circumvents these limitations by exploiting the increasing amount of semantic resources available online. As a result, there is no need either for a manually selected reference ontology (the relevant ontologies are dynamically selected from an online ontology repository), or for transforming background knowledge in an ontological form. The promising results from experiments on two real life thesauri indicate both that our approach has a high precision and also that it can find mappings, which are typically missed by existing approaches.

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

Abstract: The technology in the field of digital media generates huge amounts of textual information every day, so mechanisms to retrieve relevant information are needed. Under these circumstances, many times current web search engines do not provide users with the information they seek, because these search tools mainly use syntax based techniques. However, search engines based on semantic and context information can help overcome some of the limitations of current alternatives. In this paper, we propose a system that takes as input a list of plain keywords provided by a user and translates them into a query expressed in a formal language without ambiguity. Our system discovers the semantics of user keywords by consulting the knowledge represented by many (heterogeneous and distributed) ontologies. Then, context information is used to remove ambiguity and build the most probable query. Our experiments indicate that our system discovers the user’s information need better than traditional search engines when the semantics of the request is not the most popular on the Web.

Abstract. One of the current bottlenecks for automating ontology evolution is resolving the right links between newly arising information and the existing knowledge in the ontology. Most of existing approaches mainly rely on the user when it comes to capturing and representing new knowledge. Our ontology evolution framework intends to reduce or even eliminate user input through the use of background knowledge. In this paper, we show how various sources of background knowledge could be exploited for relation discovery. We perform a relation discovery experiment focusing on the use of WordNet and Semantic Web ontologies as sources of background knowledge. We back our experiment with a thorough analysis that highlights various issues on how to improve and validate relation discovery in the future, which will directly improve the task of automatically performing ontology changes during evolution. 1

Abstract. Ontologies form the core of Semantic Web systems, and as such, they need to evolve to meet the changing needs of the system and its users. Most of current ontology evolution systems require user input during their processes. We propose Evolva, an ontology evolution framework, aiming to substantially reduce or even eliminate user input through exploiting various background knowledge sources. In this paper we present our ontology evolution approach, as well as our preliminary outcomes and future directions. 1

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.

Abstract. We present a demo of SCARLET, a technique for discovering relations between two concepts by harvesting the Semantic Web, i.e., automatically finding and exploring multiple and heterogeneous online ontologies. While we have primarily used SCARLET’s relation discovery functionality to support ontology matching and enrichment tasks, it is also available as a stand alone component that can potentially be integrated in a wide range of applications. This demo will focus on presenting SCARLET’s functionality and its different parametric settings that can influence the trade-off between its accuracy and time performance. 1

In the context of the emerging Semantic Web, a great effort has been done in the construction of ontologies. An increasing number of them is becoming available on the Web, in order to share the knowledge that they represent. In this paper we propose an automatic mechanism that accesses, extracts and semantically merges the knowledge contained in a pool of ontologies available on the Web. In particular, we have focused on the problem of discovering the set of candidate meanings for a given keyword (or keywords). First, the different senses are obtained from different ontology libraries; second, redundant senses are automatically integrated when the system determines that they can be considered synonyms; the previous two steps are repeated until all the ontologies are visited or a specified amount of time is spent. This method proposed to manage keyword senses can be used for different purposes, such as annotation

Abstract. Ontology evolution is increasingly gaining momentum in the area of Semantic Web research. Current approaches target the evolution in terms of either content, or change management, without covering both aspects in the same framework. Moreover, they are slowed down as they heavily rely on user input. We tackle the aforementioned issues by proposing Evolva, a comprehensive ontology evolution framework, which handles a complete ontology evolution cycle, and makes use of background knowledge for decreasing user input. 1 Problem and Methodology Ontologies form the basis of Semantic Web systems. As such, they need to be kept up-to-date for the dependent systems to remain usable. With the increase of complexity and changes occurring in the represented domains, ontology evolution becomes a painstaking and time-consuming process. Thus research has witnessed an increased interest in ontology evolution. We regard ontology evolution as the “timely adaptation of an ontology to the arisen changes and the