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Analyzing Minerva

Analyzing Minerva. AUTORI: Antonello Ercoli Alessandro Pezzullo. CORSO: Seminari di Ingegneria del SW DOCENTE: Prof. Giuseppe De Giacomo. Minerva .

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Analyzing Minerva

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  1. Analyzing Minerva AUTORI: Antonello Ercoli Alessandro Pezzullo CORSO: Seminari di Ingegneria del SW DOCENTE: Prof. Giuseppe De Giacomo Analyzing Minerva

  2. Minerva • Abstract: “…storage and inference system for large scale OWL ontologies on top of relational databases. The method combines Description Logic reasoners for the Tbox inference with logic rules for Abox inference… ” Analyzing Minerva

  3. Goals of Minerva • “…it aims to meet scalability requirements of real applications and provide pratical reasoning capability as well as high query performance…” • “…the effective integration of ontology inference and storage is expected to improve reasoning effeciency, while querying without run-time inference guarantees satisfactory response time…” (ref. Minerva:A Scalable OWL Ontology Storage and Inference System) Analyzing Minerva

  4. Our Goals • Studying the architecture and understanding the functionalities of the system. • Simulating its run-time functioning. • Analizing the quality of the system, namely the Soundness and Completeness of inference  is it OWL-DL Sound and Complete? Analyzing Minerva

  5. Architecture: A general schema Analyzing Minerva

  6. The Component Diagram of Minerva Analyzing Minerva

  7. The components • Import Module: Consists of : • An Owl parser from OWL-document to EODM model • Two translators  DB translator: Abox assertions into DB •  TBOX translator: • Tbox axioms into DL reasoner • Inference results from DL reasoner to DB • Inference Module: • DL reasoner  complete subsumption relationship • between classes and properties • Rule Inference engine Abox inference based on the DLP rules Query Module: SPARQL for retrieving inferred results from the DB using SQL statements Storage Module: store both the original and inferred assertion by DL reasoner and rule inference engine Analyzing Minerva

  8. Working Schema 1/2 SPARQL TBOX OWL-DL DB Original assertions • TBOX inference • DL Reasoner: • Pellet • Racer • Structural Subsumption ABOX OWL-DL SQL ABOX Inference Engine (IBM code) DHL DLP Inferred results: named properties and classes subsumption relationships τ Analyzing Minerva

  9. Working Schema 2/2 • Precomputation step  Inference and materialization of results in a back-end DB • Query step  no-runtime inference: inferred results are retrieved directly from DB Analyzing Minerva

  10. Inference TBOX OWL-DL ABOX INFERENCE: Rule engine conducts Abox inference based on DLP Rules  sound & complete with respect to the semantics of DHL • TBOX INFERENCE: • sound & complete inference (Pellet e Racer) • Structural Subsumption Algorithm  sound but not always complete • TBOX inference • DL Reasoner: • Pellet • Racer • Structural Subsumption Original assertions ABOX OWL-DL ABOX Inference Engine (IBM code) ? DHL DLP Inferred results: named properties and classes subsumption relationships τ Analyzing Minerva

  11. DLP & DHL 1/3 • DLP (ref. Description Logic Programs: Combining Logic Programs with Description Logic): • DHL: Analyzing Minerva

  12. DLP & DHL 2/3 ? • “…DLP is the Horn fragment of OWL-DL...” • “…Horn fragment refers to a syntactic fragment of FOL, while OWL-DL is commonly perceived as a semantic fragment of FOL..”  “…DLP is the syntactic Horn fragment (in the sense of FOL syntax) of something (namely OWL-DL) which isn’t in FOL syntax but can semantically be mapped to a syntactic fragment of FOL..” • “…an OWL-DL statement is in DLP iff it can be written – semantically equivalently - as a set of Horn clauses in FOL ...” (ref. Description Logic Programs: A Pratical Choice for the Modelling of Ontologies) Analyzing Minerva

  13. DLP & DHL 3/3 • Constructors which can be used freely in OWL ontology without running the risk of leaving DLP disjointWith ? allValuesFrom ? someValuesFrom ? hasValue ? unionOf ? complementOf ? oneOf ? Analyzing Minerva

  14. τ-mapping • Based on DLP-fusion: the bidirectional translation from premises and inferences from DHL-fragment on DL to DLP and viceversa • It allows us to build rules on top of ontologies so we can use a rule inference engine and materialize inference results into DB Analyzing Minerva

  15. DLP rules from DHL axioms The mapping converts all concept and property instances into facts of two predicates,TypeOf and Relationship, and ontology axioms into facts of some predefined predicates (e.g. SubClassOf and SubPropertyOf). Analyzing Minerva

  16. Storage into RDBMS • To support both original and inferred assertions by the DL reasoner and rule inference engine, Minerva designs a specific RDBMS Schema. • Minerva categorizes table of DB schema into 4 types: atomic tables, TBox axioms tables, Abox fact tables and class constructor tables The focus of the DB Schema is that all predicates in the DLP rules have corresponding tables into DB  these rules can be easily translated into sequences of relational algebra operations, so we need simple SQL Select and Join operations among the previous tables. Analyzing Minerva

  17. Querying: SPARQL • SPARQL is a query language based on matching graph patterns  triple pattern: is like an RDF triple (resource, property, value) but with the possibility of variables in any position. • Query answering algorithm: simple retrieval of the materialized data from DB • Query Module: • SPARQL query parser • SQL translator Analyzing Minerva

  18. Conclusion 1/3 • Why DLP?: • “…existing avalaible ontologies often use very few constructs outside the DLP language fragment..” (ref.Description Logic Programs: A Pratical Choice for the Modelling of Ontologies) • “…DLP enjoys polynomial data complexity and exptime combined complexity…” (ref.Description Logic Programs: A Pratical Choice for the Modelling of Ontologies) • “…Inferencing in def-LP is thus tractable (worst case polynomial time)…DLs are generally not tractable (typically ExpTime or even NExpTime complexity)...” (ref.Description Logic Programs: Combining Logic Programs with Description Logic) Analyzing Minerva

  19. Conclusion 2/3 • Why precomputation?: • “…querying without runtime inference guarantees satisfactory response time..” (ref. Minerva:A Scalable OWL Ontology Storage and Inference System) • “…The inferred results are materialized in the database so that queries can be evaluated efficiently. Our approach is to trade space for time...” (ref. Minerva:A Scalable OWL Ontology Storage and Inference System) Analyzing Minerva

  20. Conclusion 3/3 • “…Based on the thoretically proved mapping from Description Logic to Logic Programs, we can claim that our system is sound and complete on DHL ontologies…” (ref. Minerva:A Scalable OWL Ontology Storage and Inference System) Analyzing Minerva

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