Analyzing Minerva

Analyzing Minerva 1

Analyzing Minerva

AUTORI:Antonello ErcoliAlessandro Pezzullo

CORSO:Seminari di Ingegneria del SWDOCENTE:Prof. Giuseppe De Giacomo

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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… ”

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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)

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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?

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Architecture: A general schema

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The Component Diagram of Minerva

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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

Storage Module: store both the original and inferred assertion by DL reasoner and rule inference engine

Query Module: SPARQL for retrieving inferred results from the DB using SQL statements

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TBOX OWL-DL

TBOX inferenceDL Reasoner:

•Pellet•Racer

•Structural Subsumption

Working Schema 1/2

Inferred results: named properties and classes subsumption

relationshipsτ

DHL DLP

ABOX OWL-DL

ABOX Inference Engine

(IBM code)

DB

SPARQL

SQL

Original assertions

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Precomputation step Inference and materialization of results in a back-end DB

Query step no-runtime inference: inferred results are retrieved directly from DB

Working Schema 2/2

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Inference TBOX INFERENCE: •sound & complete inference (Pellet e Racer)• Structural Subsumption Algorithm sound but not always complete

ABOX INFERENCE: Rule engine conducts Abox inference based on DLP Rules sound & complete with respect to the semantics of DHL

TBOX OWL-DL

TBOX inferenceDL Reasoner:

•Pellet•Racer

•Structural Subsumption

Inferred results: named properties and classes subsumption

relationshipsτ

DHL DLP

ABOX OWL-DL

ABOX Inference Engine

(IBM code)

Original assertions

?

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DLP (ref. Description Logic Programs: Combining Logic Programs with Description Logic):

DHL:

DLP & DHL 1/3

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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)

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Constructors which can be used freely in OWL ontology without running the risk of leaving DLP

DLP & DHL 3/3

disjointWith ? allValuesFrom ?

someValuesFrom ?hasValue ? unionOf ? complementOf

?oneOf ?

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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

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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).

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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.

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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

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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)

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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)

Conclusion 2/3

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“…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)

Conclusion 3/3