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Using the TBox to Optimise SPARQL Queries. Birte Glimm Yevgeny Kazakov Ilianna Kollia and Giorgos Stamou. CS 848 Paper Critique Vishnu Prathish. Preliminaries. Description Logic SHIQ Notations:
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Using the TBox to Optimise SPARQL Queries BirteGlimm YevgenyKazakov IliannaKollia and GiorgosStamou CS 848 Paper Critique Vishnu Prathish
Preliminaries • Description Logic SHIQ • Notations: • A concept atom is an expression A(x) and a role atom is an expression r(x, y) • Concept axiom templates and role axiom templates
Conjunctive Instance and Complex Queries • A conjunctive instance query q is a non-empty set of (concept or role) atoms. • concept templates - set of SHIQ concepts, where a concept variable can be used in place of a concept name, and a role variable in place of a role name. • Axiom templates – • role axiom template where • Concept axiom template has the form with c, d concept templates. • A finite set of role axiom templates, concept axiom templates, and (concept or role) atoms is called a complex query • Var(q) - set of variables in q. and |Var(q)| is called the arity of q.
Key Contributions • An optimization that is applicable to conjunctive instance queries. We show that one can compute an equivalent query qˆ for a given query q by replacing the variables in q with fresh individual names. Then perform realization. • Query optimization exploiting the polarity of variable occurrences in the query and the concept and role hierarchies.
Mapping functions and answers to query Mapping function: A mapping function is a certain answer for q if, denotes all the set of certain answers of q
Query Answering Via Approximate Instance retrieval • Using approximate reasoning algorithms to answer query • Either Sound and incomplete or Incomplete and Sound • Rewrite the KB into a simpler logic and run query over it to obtain desired level of approximation
Approximate Instance Retrieval and Query answering algorithms
Restricting atoms A query atom restricts restricts a query if, Eg: B(x) in the example explained. • To preserve certain answers, we should use restricting atoms that do not change the answers of q. • Can be used to prune set of possible answers of query
Query Extension • Find a way of computing restricting atoms • Based on chase technique in relational database theory • Basic Idea: • For a Abox,Query and a bijective function, • Compute(rewrite) an extended ABox and Query (using chase ) • Using approx. inst. retrieval algo., check if any atom of the new query restricts the initial query • Reduce the set of possible answers using query restriction
Polarity Based optimization • Choose the next binding to test by traversing the concept hierarchy top down • Based on the polarity of concept variable in the query the concept hierarchy can be safely pruned. • Can not be used when a variable occurs both positively and negatively
Evaluation • On custom set of queries based on GALEN (Biomedical KB – SHIF expressivity) FBbt_XP ontology (SHI) • Sys Config: Mac OS X Lion machine with a 2.53 GHz Intel Core i7 processor and Java 1.6 allowing 1GB of Java heap space.
Conclusion • A TBOX based optimization of SPARQL queries • Equivalent queries which can be exploited to produce reduce the set of mapping for conjunctive queries • Polarity based pruning for queries that go beyond conjunctive queries • Evaluation which shows that this optimization can reduce key evaluation times upto two orders of magnitude.