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We address the problem of semantic coordination, namely finding an agreement between the meanings of heterogeneous semantic models. We propose a new approximation method to discover and assess the “strength” (preciseness) of semantic mappings between different concept hierarchies. We apply this method in the music domain. We present the results of tests on mapping two music concept hierarchies from actual sites on the Internet.

(Semi-)automatic mapping — also called (semi-)automatic alignment — of ontologies is a core task to achieve interoperability when two agents or services use different ontologies. In the existing literature, the focus has so far been on improving the quality of mapping results. In Peer-to-Peer systems, however, we frequently encounter the situation where large ontological structures must be mapped onto each other in a few seconds or less in order to achieve practical feasibility of semantic translation between peers. We here present QOM (acronym for Quick Ontology Mapping), an approach that follows Herb Simon’s model of men, where he argues that human decision making is not aiming at optimality, but at satisfying the decision maker by achieving a sufficient degree of quality. We show that QOM has lower run-time complexity than existing approaches. Then, we show in experiments that this theoretical investigation translates into practical benefits. While QOM gives up some of the possibilities for producing high-quality results in favor of efficiency, our experiments show that this loss of quality is marginal, hence satisficing (=satisfying + sufficient).

Research on the Semantic Web has focused on reasoning about data that is semantically annotated in the RDF data model, with concepts and properties specified in rich ontology languages such as OWL. However, to flourish, the Semantic Web needs to provide interoperability both between sites with different ontologies and with existing, non-RDF data and the applications operating on them. To achieve this, we are faced with two problems. First, most of the world’s data is available not in RDF but in XML; XML and the applications consuming it rely not only on the domain structure of the data, but also on its document structure. Hence, to provide interoperability between such sources, we must map between both their domain structures and their document structures. Second, data management practitioners often prefer to exchange data through local point-to-point data translations, rather than mapping to common mediated schemas or ontologies. In this chapter, we present the Piazza system, which addresses the challenges of mediating between data sources on the Semantic Web by mapping both the domain structure and document structure. A key aspect of Piazza is its support for mapping between XML data and RDF data that is accompanied by OWL ontology definitions. Mappings in are provided at a local scale between small sets of nodes, and Piazza’s query answering algorithm is able to chain sets mappings together to obtain relevant data from across the system. We describe our experiences with the prototype Piazza system and a data sharing scenario implemented using it.

Until recently, most data integration techniques revolved around central approaches, e.g., global schemas, to enable transparent access to heterogeneous databases. However, with the advent of the Internet and the democratization of tools facilitating knowledge elicitation in machine-processable formats, the situation is quickly evolving. One cannot rely on global, centralized schemas anymore as knowledge creation and consumption are getting increasingly dynamic and decentralized. Peer Data Management Systems (PDMS) address this problem by eliminating centralization and instead applying compositions of local, pair-wise mappings to propagate queries among databases. We present a method to foster global semantic interoperability in PDMS settings in a totally decentralized way based on the analysis of the semantic graph linking data sources with pairwise semantic mappings. We describe how quality measures for the mappings can automatically be derived by analyzing transitive closures of mapping operations. The information obtained from these analyses are then used by the peers to route queries in a network of semantically heterogeneous sources, and to iteratively correct erroneous mappings in a self-organizing way. Additionally, we present heuristics to analyze semantic interoperability in large and heterogeneous communities. Finally, we describe Grid- Vine which implements our approach and provides a semantic overlay to demonstrate how our approach can be deployed in a practical setting.

While Knowledge Management solutions designed for the traditional organization achieve knowledge transfer by establishing central repositories of information and global procedures for knowledge flow, Distributed Knowledge Management aims to support the local processes of knowledge creation and puts the emphasis on limited, ad-hoc co-operations based on shared goals instead of global control. Peer-to-peer architectures offer an ideal technological match for the theory of DKM and may be the only choice in many practical cases, e.g. when dealing with personal knowledge that needs to be controlled locally. In the following we present the case for DKM and the methodology that was used in the SWAP project to develop and deploy the P2P applications described elsewhere in this book.

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.

Distributed Knowledge Management is an approach to Knowledge Management based on the principle that the multiplicity (and heterogeneity) of perspectives within complex organizations should not be viewed as an obstacle to knowledge exploitation, but rather as an opportunity that can foster innovation and creativity. Despite a wide agreement on this principle, most current KM systems are based on the idea that all perspectival aspects of knowledge should be eliminated in favor of an objective and general representation of knowledge. In this chapter we propose a Peer-to-Peer architecture (called KEx), which embodies the principle above in a quite straightforward way: (i) each peer (called a K-peer) provides all the services needed to create and organize “local” knowledge from an individual’s or a group’s perspective, and (ii) social structures and protocols of meaning negotiation are defined to achieve semantic coordination among autonomous peers (e.g., when searching documents from other K-peers).

Tourists’ demands are increasingly exigent in terms of quality product and environment. Thus, the tourism industry in the Balearic Islands depends on a preserved environment and on keeping local identity and quality of life (so-called Sustainable Development). Sustainable Development will be achieved after a process of effective communication and co-operation among the main actors of the tourism industry. The IST project SWAP (IST-2001-34103), financed by the EU, brought the opportunity to implement an effective and reliable knowledge-based network to enable communication and co-operation among the main players of the tourism industry in the Balearic Islands, as a supporting tool to direct current social and economical challenges towards Sustainable Development. In this chapter we will describe XAROP, a semantic and P2P based knowledge management solution developed in SWAP project to satisfy the requirements of the case study.

This chapter describes the design, implementation, and evaluation of Bibster, a Peer-to-Peer system for exchanging bibliographic data among researchers. Bibster exploits ontologies in data-storage, query formulation, query-routing and answer presentation: When bibliographic entries are made available for use in Bibster, they are structured and classified according to two different ontologies. This ontological structure is then exploited to help users formulate their queries. Subsequently, the ontologies are used to improve query routing across the Peer-to-Peer network. Finally, the ontologies are used to post-process the returned answers in order to do duplicate detection. The chapter describes each of these ontology-based aspects of Bibster.