• Journal of KIISE:Software and Applications
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• v.34 no.7
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• pp.655-666
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• 2007

Reasoners using typical Tableaux algorithm such as RacerPro, Pellet have a problem in Tableaux algorithm large ABox reasoning. Researches to solve these Problems are dealt with Instance Store of University of Manchester which uses Tableaux algorithm based reasoner and DBMS and KAON2 of University of Karlsruhe using Disjunctive Datalog approach. An evaluation experiment for present reasoners is the experiment of TBox reasoning in most of Tableaux algorithm based one. The most of benchmarking tests in reasoning systems haven't done with ABox reasoning based Tableaux Algorithm but done with TBox reasoning based Tableaux Algorithm. Especially, rarely reported benchmarking tests in reasoners have been issued nowadays. Therefore, this thesis evaluates systems with theory of each reasoners for large ABox reasoning that becomes issues recently with typical reasoners. The large AoBx reasoning engine will be analyzed using Instance Store and KAON2 of Manchester University for large ABox processing. At the analysing method, LUBM(Lehigh University BenchMark), benchmarking test method, and it's test system will be introduced. In conclusion, I recommend appropriate reasoner in various environment with experiment result and characteristic of algorithm used for each reasoner.

• Journal of KIISE
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• v.42 no.7
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• pp.852-859
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• 2015

As the amount of knowledge information significantly increases, a lot of progress has been made in the studies focusing on how to reason large scale ontology effectively at the level of RDFS or OWL. These reasoning methods are divided into TBox classifications and ABox realizations. A TBox classification mainly deals with integrity and dependencies in schema, whereas an ABox realization mainly handles a variety of issues in instances. Therefore, the ABox realization is very important in practical applications. In this paper, we propose a realization method for analyzing the constraint of the specified class, so that the reasoning system automatically infers the classes to which instances belong. Unlike conventional methods that take advantage of the object oriented language based distributed file system, we propose a large scale ontology reasoning method using spark, which is a functional programming-based in-memory system. To verify the effectiveness of the proposed method, we used instances created from the Wine ontology by W3C(120 to 600 million triples). The proposed system processed the largest 600 million triples and generated 951 million triples in 51 minutes (696 K triple / sec) in our largest experiment.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.530-538
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• 2007

Most existing deductive engines study for optimization of TBox based on Tableaux algorithm. However, in order to deduce mass-storing ABox in reality, it can't be decided in finite time. Therefore, for the efficiency of the deductive engine, there needs to be reasoning technique optimized for ABox. This paper uses the method that changes OWL-DL based Ontology to the form of Rule like Datalog in order to interlock store device such as RDBMS. Ultimately, it tries to in circumstance of real world. Therefor, using Axiom that OWL holds, it suggests reasoning method that applies rules including datatype.

• 한국IT서비스학회:학술대회논문집
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• 2009.05a
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• pp.353-356
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• 2009

OWL 온톨로지의 확장 가능한(scalable) 추론(reasoning)에 대한 접근 방법으로 SQL로 구축된 논리 규칙을 관계형 데이터베이스에 저장되어있는 개체(individual)에 대한 사실(facts)과 공리(axioms)들에 적용하는 것이다. 예로서 미네르바(Minerva)는 서술 논리 프로그램(Description Logic Program, DLP)을 적용함으로써 ABox 추론을 수행한다. 본 연구에서는 관계형 데이터베이스를 기반으로 추론을 시도하며, 대규모 논리 규칙 집합을 사용한 추론을 시도한다. 뿐만 아니라, 특정 클래스에 속한 익명(anonymous)의 개체들과 개체들의 묵시적(implicit)인 관계성 추론을 시도하며, 필요한 경우 새로운 개체를 생성함으로써 명시화하여 추론을 시도한다. 더욱이, 추론의 논리 패러다임(paradigm)에서부터 데이터베이스 패러다임에 이르기까지 변화 시켜가면서 카디널리티(cardinality) 제약을 만족하는 개체들에 대한 제약적인 추정 추론을 시도하며, 벤치마크 테스트 결과 향상된 추론 능력을 얻을 수 있음을 보인다.

• Proceedings of the Korean Information Science Society Conference
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• 2006.10b
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• pp.352-356
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• 2006

현존하는 추론 엔진들은 대부분 Tableaux 알고리즘 기반의 TBox의 최적화를 위한 연구를 진행하였다. 하지만 현실에서 대용량의 ABox를 추론하기 위한 유한한 시간 내에 결정 가능성을 보장하지 못한다. 따라서 실용성 있는 추론 엔진 효율을 위해서는 대용량 데이터를 가지는 ABox를 위한 최적화된 추론 기법이 필요하다. 본 논문에서는 OWL-DL 기반의 온톨로지(Ontology)를 데이터로그(Datalog)와 같은 규칙(Rule) 형태로 변형하여 관계형 데이터베이스와 같은 저장 시스템과 연동하기 위한 방법을 이용한다. 최종적으로 실세계의 환경에서의 데이터타입 속성(Datatype Property)이 포함된 SHIQ(D) 구성의 실용적인 추론 시스템을 수행하고자 한다. 따라서 OWL이 가지는 공리(Axiom)를 이용하여 데이터타입 속성이 포함된 규칙을 적용한 추론 방법에 대해서 제안하였다.

• The Journal of the Korea Contents Association
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• v.14 no.10
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• pp.438-453
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• 2014

This paper proposes a system to translate an RDB into an OWL ontology which enables the users to navigate the ontology in GUI. In order to accomplish the goals mentioned previously, the system overcame two difficulties. First, our system defines a new mapping algorithm to map between DB elements and ontology ones. Comparing with existing solutions, our algorithm is able to generate ontologies from more DB structures. Second, our system provides the same data generated by a reasoner to the users. Note that this operation does not load ABox ontology on a reasoner. In addition, Tableau-based reasoners have the tractability problem on a large ABox (e.g., large ABoxes translated from DBs practically cannot be served). To solve this, our system internally runs SQL queries to retrieve the same data as the one from a reasoner every time ABox elements are queried.

• Journal of the Korea Society of Computer and Information
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• v.20 no.3
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• pp.29-45
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• 2015

This paper proposes an implementation of SPARQL-DL, which is a query language for OWL ontologies, for query-answering over the OWL ontologies virtually generated from existing RDBs. The proposed SPARQL-DL processor internally translates input SPARQL-DL queries into SQL queries and then executes the translated queries. There are two advantages in the query processing method. First, another repository to store OWL ontologies generated from RDBs is not required. Second, a large ABox generated from an RDB instance is able to be served without using Tableau algorithm based reasoners which have a problem in large ABox reasoning. Our algorithm for query rewriting is designed to create one corresponding SQL query from one input SPARQL-DL query to minimize the overhead by establishing connections with RDBs.