Ronny Siebes

Bibster-a semantics-based bibliographic Peer-to-Peer system

This paper describes Bibster, a Peer-to-Peer system for exchanging bibliographic metadata among researchers. We show how Bibster exploits ontologies in data-representation, query formulation, query routing, and query result presentation. The Bibster system is freely available and is used by researchers across multiple organizations.

Peer Selection in Peer-to-Peer Networks with Semantic Topologies

Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic topology. Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. To calculate our semantic similarity measure we make the simplifying assumption that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments we show how expertise based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages.

MultimediaN e-culture demonstrator

The main objective of the MultimediaN E-Culture project is to demonstrate how novel semantic-web and presentation technologies can be deployed to provide better indexing and search support within large virtual collections of cultural-heritage resources. The architecture is fully based on open web standards, in particular XML, SVG, RDF/OWL and SPARQL. One basic hypothesis underlying this work is that the use of explicit background knowledge in the form of ontologies/vocabularies/thesauri is in particular useful in information retrieval in knowledge-rich domains.

Marvin: Distributed reasoning over large-scale Semantic Web data

Many Semantic Web problems are difficult to solve through common divide-and-conquer strategies, since they are hard to partition. We present Marvin, a parallel and distributed platform for processing large amounts of RDF data, on a network of loosely coupled peers. We present our divide-conquer-swap strategy and show that this model converges towards completeness. Within this strategy, we address the problem of making distributed reasoning scalable and load-balanced. We present SpeedDate, a routing strategy that combines data clustering with random exchanges. The random exchanges ensure load balancing, while the data clustering attempts to maximise efficiency. SpeedDate is compared against random and deterministic (DHT-like) approaches, on performance and load-balancing. We simulate parameters such as system size, data distribution, churn rate, and network topology. The results indicate that SpeedDate is near-optimally balanced, performs in the same order of magnitude as a DHT-like approach, and has an average throughput per node that scales with i for i items in the system. We evaluate our overall Marvin system for performance, scalability, load balancing and efficiency.

SWAP: ontology-based knowledge management with peer-to-peer technology

[BBT02] suggest a distributed approach to Knowledge Management which better fits the true situation in organizations and the processes people are used to. From a technological point of view peer-to-peer (P2P) solutions are particularly well suited, because they make it possible for different participants (organizations, individuals, or departments) to maintain their own knowledge structure while exchanging information. However, today’s P2P solutions are extremely limited (they mostly rely on keyword search) and not appropriate for the high requirements of a KMSs.

The SWAP Data and Metadata Model for Semantics-Based Peer-to-Peer Systems

Peer-to-Peer systems are a new paradigm for information sharing and some systems have successfully been deployed. It has been argued that current Peer-to-Peer systems suffer from the lack of semantics. The SWAP project (Semantic Web and Peer-to-Peer) aims at overcoming this problem by combining the Peer-to-Peer paradigm with Semantic Web technologies. In this paper, we propose a data model for encoding semantic information that combines features of ontologies (concept hierarchies, relational structures) with a flexible description and rating model that allows us to handle heterogeneous and even contradictory views on the domain of interest. We discuss the role of this model in the SWAP environment and describe the model as well as its application.

Expertise-based peer selection in Peer-to-Peer networks

Peer-to-Peer systems have proven to be an effective way of sharing data. Modern protocols are able to efficiently route a message to a given peer. However, determining the destination peer in the first place is not always trivial. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic topology. Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. To calculate our semantic similarity measure, we make the simplifying assumption that the peers share the same ontology. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments complemented with a real-world field experiment, we show how expertise-based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages.

Bibster: a semantics-based bibliographic peer-to-peer system

This paper describes the design and implementation 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 postprocess the returned answers in order to do duplicate detection. The paper describes each of these ontology-based aspects of Bibster. Bibster is a fully implemented open source solution built on top of the JXTA platform.

Massively Scalable Web Service Discovery

The increasing popularity of Web Services (WS) has exemplified the need for scalable and robust discovery mechanisms. Although decentralized solutions for discovering WS promise to fulfill these needs, most make limiting assumptions concerning the number of nodes and the topology of the network and rely on having information on the data apriori (e.g. categorizations or popularity distributions). In addition, most systems are tested via simulations using artificial datasets. In this paper we introduce a lightweight, scalable and robust WSDL discovery mechanism based on real-time calculation of term popularity. In order to evaluate this mechanism, we have collected and analyzed real data from deployed WS and performed a large-scale emulation on the DAS-3 distributed supercomputer. Results show that we can achieve web-scale service discovery based on term search and we also sketch an extension of this mechanism to support a fully-fledged WS query language.

Expertise-based peer selection

Peer-to-Peer systems have proven to be an effective way of sharing data. Finding the data in an efficient and robust manner still is a challenging problem. We propose a model in which peers advertise their expertise in the Peer-to-Peer network. The knowledge about the expertise of other peers forms a semantic overlay network (SON). Based on the semantic similarity between the subject of a query and the expertise of other peers, a peer can select appropriate peers to forward queries to, instead of broadcasting the query or sending it to a random set of peers. We evaluate the model in a bibliographic scenario, where peers share bibliographic descriptions of publications among each other. In simulation experiments complemented with a real-world field experiment we show how expertise based peer selection improves the performance of a Peer-to-Peer system with respect to precision, recall and the number of messages.