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Scalable Semantic Retrieval through Summarization and Refinement

Scalable Semantic Retrieval through Summarization and Refinement,Julian Dolby,Achille Fokoue,Aditya Kalyanpur,Aaron Kershenbaum,Edith Schonberg,Kavith

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Scalable Semantic Retrieval through Summarization and Refinement   (Citations: 21)
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Query processing of OWL-DL ontologies is intractable in the worst case, but we present a novel technique that in practice allows for efficient querying of ontologies with large Aboxes in secondary storage. We focus on the processing of instance retrieval queries, i.e., queries that retrieve individuals in the Abox which are instances of a given concept C. Our tech- nique uses summarization and refinement to reduce instance retrieval to a small relevant subset of the original Abox. We demonstrate the effectiveness of this technique in Aboxes with up to 7 million assertions. Our results are applicable to the very expressive description logic SHIN , which cor- responds to OWL-DL minus nominals and datatypes.
Conference: National Conference on Artificial Intelligence - AAAI , pp. 299-304, 2007
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    • ...SHER [27] stores instance data in the database, and creates a small summary ABox from the original ABox for query processing...

    Chen Zhou, et al. Ontology Management in an Event-Triggered Knowledge Network

    • ... small set S of axioms that is responsible for an inconsistency discovered by a single consistency test, and 2) performing additional |S| consistency check on KBs of size at most |S |− 1 to remove extraneous elements from S. In our previous work [4], we presented a scalable approach to efficiently compute a large number of − but not all − justifications in large and expressive KBs through the technique of summarization and refinement [5]...
    • ...SHER was chosen for its unique ability to scale reasoning to very large and expressive KBs [5], and to efficiently detect large number of inconsistency justifications in a scalable way [4]...
    • ...Fig. 4. Trust under 90% PuMS attack (No duplication) Fig. 5. Trust under single PuMS attack (25% duplication) Assessing Trust in Uncertain Information 221 of summarization and refinement [5]...

    Achille Fokoue, et al. Assessing Trust in Uncertain Information

    • ...In our previous work [7], we have shown that a summarization and refinement approach enables tableau-based SHIN reasoners to scale to very large knowledge base such as one encoding the current state of the world for our security application...
    • ...We use the same summarization and refinement approach (see section 3.1.2), which is responsible for SHER unique scalability over large knowledge bases as established in [1, 7, 8]. 4 We further require that if x is a variable appearing in C, then it must also appear in one of the Pi...
    • ...We have shown that using this approach, one can support scalable DL reasoning that not only encodes sophisticated spatial-temporal sensitivity decay and situation-aware access control policies but also supports explanations [7] indicating why an object is shareable or not...

    Achille Fokoue, et al. A decision support system for secure information sharing

    • ...For example, in [4], ontologies are automatically constructed as an output from text mining, and it is possible for the resulting ontologies can be inconsistent...

    Matthew Horridge, et al. Explaining Inconsistencies in OWL Ontologies

    • ...In our current implementation, SOR ontology repository [17] is used to store ontologies and rules, and SHER engine [15] for ontology classification reasoning...
    • ...Under this category, we further classify the reasoning as the following two types: Reasoning based on Description Logic (DL): We apply IBM SHER engine [15] to support all concept assertions...
    • ...It is reported in [15] that SHER can process ABox queries with up to 7.4 million assertions efficiently...

    Li Ma, et al. Semantic Enhancement for Enterprise Data Management

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