Computational Intelligence · 2005 · 85 citations · 27 references
ComputingComputer ScienceInformation TechnologyDatabasesKnowledge MappingKnowledge RepresentationPeer-to-peer NetworkingPeer-to-peer NetworksPeer-to-peer SystemsQuery ProcessingSchema DesignSemantic NetworksData Schema MappingHeterogeneityHeterogeneous IntegrationMapping AlgorithmsQuery RoutingRouting In Peer-to-peer NetworksSchema MappingSemantic Relationships
A semantic link peer‑to‑peer network manages semantic relationships between peers’ data schemas and serves as the semantic layer of a scalable Knowledge Grid. The authors propose an automatic semantic link discovery method, a tool for building and maintaining P2P semantic link networks, a semantic‑based peer similarity measurement for efficient query routing, and schema‑mapping algorithms for query reformulation and heterogeneous data integration. The method enriches schema relationships with semantic links, incorporates XML structure into similarity measurement to forward queries accurately, and handles semantic and structural heterogeneity to enable uniform data exchange among peers. The approach is characterized by three key aspects: enriched schema relationships via semantic links, XML‑aware similarity for efficient query routing, and heterogeneity handling for uniform data integration.
A semantic link peer‐to‐peer (P2P) network specifies and manages semantic relationships between peers' data schemas and can be used as the semantic layer of a scalable Knowledge Grid. The proposed approach consists of an automatic semantic link discovery method, a tool for building and maintaining P2P semantic link networks (P2PSLNs), a semantic‐based peer similarity measurement for efficient query routing, and the schema mapping algorithms for query reformulation and heterogeneous data integration. The proposed approach has three important aspects. First, it uses semantic links to enrich the relationships between peers' data schemas. Second, it considers not only nodes but also the XML structure in measuring the similarity between schemas to efficiently and accurately forward queries to relevant peers. Third, it copes with semantic and structural heterogeneity and data inconsistency so that peers can exchange and translate heterogeneous information within a uniform view.
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