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Analyzing Minerva 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
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
Analyzing Minerva 8
TBOX OWL-DL
TBOX inference
DL Reasoner:
•Pellet
•Racer
•Structural Subsumption
Working Schema 1/2
Inferred results: named properties and classes subsumption
relationships
τDHL DLP
ABOX OWL-DL
ABOX Inference Engine
(IBM code)
DB
SPARQL
SQL
Original assertions
Analyzing Minerva 9
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
Analyzing Minerva 10
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 inference
DL Reasoner:
•Pellet
•Racer
•Structural Subsumption
Inferred results: named properties and classes subsumption
relationships
τDHL DLP
ABOX OWL-DL
ABOX Inference Engine
(IBM code)
Original assertions
?
Analyzing Minerva 11
DLP (ref. Description Logic Programs: Combining Logic Programs with Description Logic):
DHL:
DLP & DHL 1/3
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
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 ?
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
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
Analyzing Minerva 20
“…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
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