Large knowledge collider: a service-oriented platform for large-scale semantic reasoning

ABSTRACT

Recent advances in the Semantic Web community have yielded a variety of reasoning methods used to process and exploit semantically annotated data. However, most of those methods have only been approved for small, closed, trustworthy, consistent, and static domains. Still, there is a deep mismatch between the requirements for reasoning on a Web scale and the existing efficient reasoning algorithms over restricted subsets. This paper describes the pilot implementation of LarKC -- the Large Knowledge Collider, a platform, which focuses on supporting large-scale reasoning over billions of structured data in heterogeneous data sets. The architecture of LarKC allows for an effective combination of techniques coming from different Semantic Web domains by following a service-oriented approach, supplied by sustainable infrastructure solutions.

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Index Terms

• Computing methodologies

  • Artificial intelligence

    • Knowledge representation and reasoning

      • Semantic networks

• Information systems

  • Information retrieval

    • Search engine architectures and scalability

      • Distributed retrieval

      • Peer-to-peer retrieval

  • Information storage systems

    • Storage architectures

      • Distributed storage

• Theory of computation

  • Theory and algorithms for application domains

    • Machine learning theory

      • Inductive inference