OWL本体中完整性约束的验证方法研究
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摘要
知识库中的完整性约束验证是自动推理领域近年来研究的热点，对于语义Web数据的应用具有重要意义。完整性约束用于保证知识库中的合理状态，只允许满足约束的数据加入到知识库中。本文从完整性约束在本体知识库中的语义建模、可满足性验证以及完整性约束验证优化等几个方面进行了研究。
1.提出了基于基限定的OWL本体中完整性约束验证方法，基于基限定的语义能够正确捕捉DL知识库中的完整性约束，并成功结合了开放世界假设下的标准推理与封闭世界假设下的完整性约束验证。
2.提出了利用失败即否定转换的普适性完整性约束验证方法，这种方法中知识库以及完整性约束的描述逻辑语言不受限制，适合于大多数的描述逻辑语言，可以直接利用现有推理机制进行本体知识库的完整性约束验证。
3.提出了轻量级本体中完整性约束的验证优化方法，研究完整性约束验证问题到知识库查询回答问题的转换，并进一步利用重构技术将其转换为数据库中的实例查询问题，能大幅提高轻量级本体知识库的完整性约束验证效率。
4.提出了基于模块化的完整性约束验证优化方法，利用局部化和结构相关性研究本体中完整性约束相关模块的抽取方法，将大规模本体上的完整性约束验证问题转换为与之等价小模块上的完整性约束的验证问题，减少了待测本体的规模，提高了完整性约束的验证效率。
Tim Berners-Lee has put forward the concept of Semantic Web in1998, in which datahave been given the computer-readable semantics for the human-computer interaction.Ontology is the core of the Semantic Web and is used for representing the conceptual modelof Semantic Web data. Web Ontology Language (OWL) is the endorsed standard ontologydefinition language for the World Wide Web Consortium (W3C). As the logical foundation ofOWL, Description Logics(DLs) have expressive languages and provide sound and completereasoning algorithms for standard reasoning tasks, such as concept satisfaction, consistency ofontology, and ontology debugging etc..
In data-centric applications, besides avoiding the logical conflicts in ontology data, usersprefer to the integrity constraint validation in ontology, which means the ontology data shouldcomplete and satisfy certain conditions for user-requirements. Integrity constraints wereoriginally proposed in database and artificial intelligence knowledge representation languagesto guarantee the legal states that are considered acceptable by knowledge bases. Integrityconstraints can be added into the ontology to guarantee the integrity of ontology. For example,in the development of information systems, integrity constraints are required to represent theapplication requirements in the ontology. Furthermore, in the translation from a databasemodel to an OWL model, they are also required to identify the ontology elements andrestrictions on these elements. By doing the validation of integrity constraint satisfaction, it ispossible to guarantee the integrity of ontology data. Thus, how to validate the integrityconstraints in ontology is an essential issue in the ontology engineering.
There are four issues should be considered in integrity constraint validation. Firstly, themain challenge of modeling integrity constraints in ontology is the difference in semantics.The integrity constraint validation should adhere to the closed world assumption (CWA),whereas the standard semantics of OWL follows the open world assumption (OWA). Thestandard OWL semantics makes data in the semantic Web faithfully capture the information inthe real world. However, due to the appearance of uncertain information, it is difficult tomodel such constraints under the OWA. Therefore, we are required to model the integrityconstraints under the CWA. Secondly, according to the application requirements, it isnecessary to gurantee that the ontology data should be minimized w.r.t. integrity constraints.Thirdly, we should consider the combination of standard reasoning and integrity constraintvalidation. Since the ontology has the ability of inferring implicit information, the standardreasoning is also essential and required to be considered during the integrity constraintvalidation process. Last but not least, we also need to consider the efficiency of integrity constraint validation. At the basis of the ensurance of integrity constraint validation, how toimprove the efficiency of integrity constraint validation in an ontology is an important issue.Especially, when face to the scalable Web data, the efficiency may directly affect the variousapplications in ontology engineering.
With the analysis of existing works, we study integrity constraint validation from thefollowing three aspects, contains the semantics of integrity constraint in ontology, thecombination of standard reasoning and integrity constraint validation, and the optimization ofintegrity constraint validation.
1. Firstly, we model the semantics of integrity constraints in OWL ontology using groundedcircumscription and weak unique name assumption in this paper. By restricting theextension of predicates in integrity constraint axioms, we define the semantics ofintegrity constraint axioms which is under closed world assumption. Furthermore,according to the minimal idea of grounded circumscription, the satisfaction of integrityconstraint is also defined. Finally, in order to get more intuitive sense of integrityconstraint semantics, we additionally apply the weak unique name assumption. Underthis semantics, the reasoning algorithm may well combine the standard reasoning underthe open world assumption and integrity constraint validation under the closed worldassumption, more appropriate to the user requirements.
2. In order to well combine the standard reasoning in description logic knowledge bases andintegrity constraint validation, we validate the integrity constraint by modifying tableaualgorithm and introducing negation as failure respectively in this paper. The formermethod mainly focuses on modifying the expansion rules in tableau algorithmsaccording to the defined integrity constraint semantics. The latter method firstlypreprocesses integrity constraint axioms with negation operators and then validate theintegrity constraint knowledge bases entailments which using the tableau algorithm.Each of these methods has its advantage: the former has high efficiency and the lattermethod is a universal approach which is suitable for the most of description logiclanguages.
3. We optimize the integrity constraint validation by reformulating and modularization.Firstly, we consider the validation of integrity constraints for tractable DL-Lite ontology.Using reformulation technology, the satisfaction of integrity constraint axioms can bereduced to the query answering over databases. By doing this, we can directly using thequery optimization mechanisms to optimize the efficiency of integrity constraintvalidation, which may more appropriate to the scalable ontology data. Additionally, weuse the modularization to extract the modules, which are logically related with integrityconstraint axioms, from scalable ontology data, and further check the satisfaction ofintegrity constraint axioms in these modules. In this way, it is no longer necessary to dothe integrity constraint validation in the whole ontology, and get the same results for the integrity constraint validation.
1.提出了基于基限定的OWL本体中完整性约束验证方法，基于基限定的语义能够正确捕捉DL知识库中的完整性约束，并成功结合了开放世界假设下的标准推理与封闭世界假设下的完整性约束验证。
2.提出了利用失败即否定转换的普适性完整性约束验证方法，这种方法中知识库以及完整性约束的描述逻辑语言不受限制，适合于大多数的描述逻辑语言，可以直接利用现有推理机制进行本体知识库的完整性约束验证。
3.提出了轻量级本体中完整性约束的验证优化方法，研究完整性约束验证问题到知识库查询回答问题的转换，并进一步利用重构技术将其转换为数据库中的实例查询问题，能大幅提高轻量级本体知识库的完整性约束验证效率。
4.提出了基于模块化的完整性约束验证优化方法，利用局部化和结构相关性研究本体中完整性约束相关模块的抽取方法，将大规模本体上的完整性约束验证问题转换为与之等价小模块上的完整性约束的验证问题，减少了待测本体的规模，提高了完整性约束的验证效率。
Tim Berners-Lee has put forward the concept of Semantic Web in1998, in which datahave been given the computer-readable semantics for the human-computer interaction.Ontology is the core of the Semantic Web and is used for representing the conceptual modelof Semantic Web data. Web Ontology Language (OWL) is the endorsed standard ontologydefinition language for the World Wide Web Consortium (W3C). As the logical foundation ofOWL, Description Logics(DLs) have expressive languages and provide sound and completereasoning algorithms for standard reasoning tasks, such as concept satisfaction, consistency ofontology, and ontology debugging etc..
In data-centric applications, besides avoiding the logical conflicts in ontology data, usersprefer to the integrity constraint validation in ontology, which means the ontology data shouldcomplete and satisfy certain conditions for user-requirements. Integrity constraints wereoriginally proposed in database and artificial intelligence knowledge representation languagesto guarantee the legal states that are considered acceptable by knowledge bases. Integrityconstraints can be added into the ontology to guarantee the integrity of ontology. For example,in the development of information systems, integrity constraints are required to represent theapplication requirements in the ontology. Furthermore, in the translation from a databasemodel to an OWL model, they are also required to identify the ontology elements andrestrictions on these elements. By doing the validation of integrity constraint satisfaction, it ispossible to guarantee the integrity of ontology data. Thus, how to validate the integrityconstraints in ontology is an essential issue in the ontology engineering.
There are four issues should be considered in integrity constraint validation. Firstly, themain challenge of modeling integrity constraints in ontology is the difference in semantics.The integrity constraint validation should adhere to the closed world assumption (CWA),whereas the standard semantics of OWL follows the open world assumption (OWA). Thestandard OWL semantics makes data in the semantic Web faithfully capture the information inthe real world. However, due to the appearance of uncertain information, it is difficult tomodel such constraints under the OWA. Therefore, we are required to model the integrityconstraints under the CWA. Secondly, according to the application requirements, it isnecessary to gurantee that the ontology data should be minimized w.r.t. integrity constraints.Thirdly, we should consider the combination of standard reasoning and integrity constraintvalidation. Since the ontology has the ability of inferring implicit information, the standardreasoning is also essential and required to be considered during the integrity constraintvalidation process. Last but not least, we also need to consider the efficiency of integrity constraint validation. At the basis of the ensurance of integrity constraint validation, how toimprove the efficiency of integrity constraint validation in an ontology is an important issue.Especially, when face to the scalable Web data, the efficiency may directly affect the variousapplications in ontology engineering.
With the analysis of existing works, we study integrity constraint validation from thefollowing three aspects, contains the semantics of integrity constraint in ontology, thecombination of standard reasoning and integrity constraint validation, and the optimization ofintegrity constraint validation.
1. Firstly, we model the semantics of integrity constraints in OWL ontology using groundedcircumscription and weak unique name assumption in this paper. By restricting theextension of predicates in integrity constraint axioms, we define the semantics ofintegrity constraint axioms which is under closed world assumption. Furthermore,according to the minimal idea of grounded circumscription, the satisfaction of integrityconstraint is also defined. Finally, in order to get more intuitive sense of integrityconstraint semantics, we additionally apply the weak unique name assumption. Underthis semantics, the reasoning algorithm may well combine the standard reasoning underthe open world assumption and integrity constraint validation under the closed worldassumption, more appropriate to the user requirements.
2. In order to well combine the standard reasoning in description logic knowledge bases andintegrity constraint validation, we validate the integrity constraint by modifying tableaualgorithm and introducing negation as failure respectively in this paper. The formermethod mainly focuses on modifying the expansion rules in tableau algorithmsaccording to the defined integrity constraint semantics. The latter method firstlypreprocesses integrity constraint axioms with negation operators and then validate theintegrity constraint knowledge bases entailments which using the tableau algorithm.Each of these methods has its advantage: the former has high efficiency and the lattermethod is a universal approach which is suitable for the most of description logiclanguages.
3. We optimize the integrity constraint validation by reformulating and modularization.Firstly, we consider the validation of integrity constraints for tractable DL-Lite ontology.Using reformulation technology, the satisfaction of integrity constraint axioms can bereduced to the query answering over databases. By doing this, we can directly using thequery optimization mechanisms to optimize the efficiency of integrity constraintvalidation, which may more appropriate to the scalable ontology data. Additionally, weuse the modularization to extract the modules, which are logically related with integrityconstraint axioms, from scalable ontology data, and further check the satisfaction ofintegrity constraint axioms in these modules. In this way, it is no longer necessary to dothe integrity constraint validation in the whole ontology, and get the same results for the integrity constraint validation.
引文
[1] Berners-Lee T, Hendler J, Lassila O. The semantic web [J]. Scientific american,2001.284(5):28-37.
[2] Popov B, Kiryakov A, Ognyanoff D, et al. Towards semantic web informationextraction[C]. proceedings of the Human Language Technologies Workshop at the2ndInternational Semantic Web Conference (ISWC2003),2003.
[3] Gon alves M A, Fox E A, Watson L T. Towards a digital library theory: a formal digitallibrary ontology [J]. International Journal on Digital Libraries,2008.8(2):91-114.
[4] Kruk S R, Kruk E, Stankiewicz K. Evaluation of semantic and social technologies fordigital libraries [M]. Semantic Digital Libraries. Springer.2009:203-214.
[5] McIlraith S A, Son T C, Zeng H. Semantic web services [J]. IEEE intelligent systems,2001.16(2):46-53.
[6] Staab S, Studer R. Handbook on ontologies [M]. Springer,2009.
[7] Dean M, Schreiber G, Bechhofer S, et al. OWL web ontology language reference [J].W3C Recommendation February,2004.
[8] Baader F. The description logic handbook: theory, implementation, and applications [M].Cambridge university press,2003.
[9] Doms A, Schroeder M. GoPubMed: exploring PubMed with the gene ontology [J].Nucleic acids research,2005.33(suppl2):783-786.
[10] Cornet R, de Keizer N. Forty years of SNOMED: a literature review [J]. BMC medicalinformatics and decision making,2008.8(Suppl1).
[11] Brickley D, Miller L. FOAF vocabulary specification0.98[J]. Namespace document,2010.9.
[12] Miller E. An introduction to the resource description framework [J]. Bulletin of theAmerican Society for Information Science and Technology,1998.25(1):15-19.
[13] Beckett D, McBride B. RDF/XML syntax specification (revised)[J]. W3Crecommendation,2004.10.
[14] Berners-Lee T, Connolly D. Notation3(N3): A readable RDF syntax [J]. W3C TeamSubmission (January2008) http://www w3org/TeamSubmission,1998.(3).
[15] Beckett D, Barstow A. N-Triples-W3C RDF Core WG Internal Working Draft [J]. URL:http://www w3org/2001/sw/RDFCore/ntriples,2001.
[16] Beckett D, Berners-Lee T. Turtle-terse RDF triple language [J]. W3C Team Submission,2008.14.
[17] Sirin E, Parsia B, Grau B C, et al. Pellet: A practical owl-dl reasoner [J]. Web Semantics:science, services and agents on the World Wide Web,2007.5(2):51-53.
[18] Haarslev V, Müller R. RACER system description [M]. Automated Reasoning. Springer.2001:701-705.
[19] Tsarkov D, Horrocks I. FaCT++description logic reasoner: System description [M].Automated reasoning. Springer.2006:292-297.
[20] Horrocks I, Motik B, Wang Z. The HermiT OWL Reasoner [C]. proceedings of theProceedings of the1st International Workshop on OWL Reasoner Evaluation(ORE-2012), Manchester, UK,2012.
[21] Kremen P, Kouba Z. Ontology-Driven Information System Design [J]. Systems, Man,and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on,2012.42(3):334-344.
[22] Albarrak K M, Sibley E H. Translating relational&object-relational database modelsinto OWL models [C]. proceedings of the Information Reuse&Integration, IRI'09IEEEInternational Conference on,2009. IEEE,336-341.
[23]郑少兰.关系数据库中本体完整性约束验证方法[J].福建电脑,2013.29(10).
[24]金芝.基于本体的需求自动获取[J].计算机学报,2000.23(5):486-492.
[25]李海波,战德臣,徐晓飞.工作流业务规则语义的完整性验证技术[J].计算机研究与发展,2009.(7):1143-1151.
[26]王莉,梁冰,白海燕.基于本体的科技文献检索框架与技术实现[J].数字图书馆论坛,2012.(7):37-44.
[27] Kowalski R. Logic for data description [M]. Springer,1978.
[28] Lloyd J W, Topor R W. A basis for deductive database systems [J]. The Journal of LogicProgramming,1985.2(2):93-109.
[29] Reiter R. On integrity constraints [C]. proceedings of the Proceedings of the2ndconference on Theoretical aspects of reasoning about knowledge,1988. MorganKaufmann Publishers Inc.,97-111.
[30]Levesque H J. All I know: a study in autoepistemic logic [J]. Artificial Intelligence,1990.42(2):263-309.
[31] Donini F M, Lenzerini M, Nardi D, et al. An epistemic operator for description logics [J].Artificial Intelligence,1998.100(1):225-274.
[32] Donini F M, Nardi D, Rosati R. Description logics of minimal knowledge and negationas failure [J]. ACM Transactions on Computational Logic (TOCL),2002.3(2):177-225.
[33] Calvanese D, De Giacomo G, Lembo D, et al. EQL-Lite: Effective First-Order QueryProcessing in Description Logics [C]. proceedings of the IJCAI,2007.274-279.
[34] Mehdi A, Rudolph S, Grimm S. Epistemic querying of OWL knowledge bases [M]. TheSemantic Web: Research and Applications. Springer.2011:397-409.
[35]梅婧.语义Web本体与规则[D],2007.
[36] Eiter T, Ianni G, Lukasiewicz T, et al. Combining answer set programming withdescription logics for the semantic web [J]. Artificial Intelligence,2008.172(12):1495-1539.
[37] Motik B, Rosati R. Reconciling description logics and rules [J]. Journal of the ACM(JACM),2010.57(5):1-62.
[38] Grosof B N, Horrocks I, Volz R, et al. Description logic programs: Combining logicprograms with description logic [C]. proceedings of the Proceedings of the12thinternational conference on World Wide Web,2003. ACM,48-57.
[39] Rosati R. DL+log: Tight Integration of Description Logics and Disjunctive Datalog [J].KR,2006.6:68-78.
[40] Motik B, Horrocks I, Sattler U. Bridging the gap between OWL and relational databases[J]. Web Semantics: Science, Services and Agents on the World Wide Web,2009.7(2):74-89.
[41] Jeffrey A, Patel-Schneider P F. Integrity Constraints for Linked Data [C]. proceedings ofthe Description Logics,2011.
[42] Tao J, Sirin E, Bao J, et al. Integrity Constraints in OWL [C]. proceedings of the AAAI,2010.
[43] Angles R, Gutierrez C. The expressive power of SPARQL [M]. The SemanticWeb-ISWC2008. Springer.2008:114-129.
[44] Heymans S, McKennirey M, Phillips J. Semantic validation of the use of SNOMED CTin HL7clinical documents [J]. J Biomedical Semantics,2011.2:1-16.
[45] Cyganiak R, Jentzsch A. Linking open data cloud diagram [J]. LOD Community(http://lod-cloud net/),2011.
[46] Wache H, Serafini L, Garc a-Castro R. D2.1.1survey of scalability techniques forreasoning with ontologies[R]. Technical report, Knowledge Web,2004.
[47] Baader F, Brandt S, Lutz C. Pushing the EL envelope [C]. proceedings of the IJCAI,2005.364-369.
[48] Baader F, Brandt S, Lutz C. Pushing the EL envelope further [J].2008.
[49] Calvanese D, De Giacomo G, Lembo D, et al. Tractable reasoning and efficient queryanswering in description logics: The DL-Lite family [J]. Journal of Automated reasoning,2007.39(3):385-429.
[50] Rosati R, Almatelli A. Improving Query Answering over DL-Lite Ontologies [J]. KR,2010.10:290-300.
[51] Kontchakov R, Lutz C, Toman D, et al. The Combined Approach to Query Answering inDL-Lite [C]. proceedings of the KR,2010.
[52] Pérez-Urbina H, Horrocks I, Motik B. Efficient query answering for OWL2[M]. TheSemantic Web-ISWC2009. Springer.2009:489-504.
[53] Hitzler P, Kr tzsch M, Parsia B, et al. OWL2web ontology language primer [J]. W3Crecommendation,2009.27:1-123.
[54] Pan J Z, Thomas E, Zhao Y. Completeness Guaranteed Approximations for OWL-DLQuery Answering [J]. Description Logics,2009.477.
[55] Tserendorj T, Rudolph S, Kr tzsch M, et al. Approximate OWL-reasoning with Screech[M]. Web Reasoning and Rule Systems. Springer.2008:165-180.
[56] Fokoue A, Kershenbaum A, Ma L, et al. The summary abox: Cutting ontologies down tosize [M]. The Semantic Web-ISWC2006. Springer.2006:343-356.
[57] Dolby J, Fokoue A, Kalyanpur A, et al. Scalable semantic retrieval throughsummarization and refinement [C]. proceedings of the AAAI,2007.299-304.
[58] Dolby J, Fokoue A, Kalyanpur A, et al. Scalable highly expressive reasoner (SHER)[J].Web Semantics: Science, Services and Agents on the World Wide Web,2009.7(4):357-361.
[59] Noy N F, Musen M A. The PROMPT suite: interactive tools for ontology merging andmapping [J]. International Journal of Human-Computer Studies,2003.59(6):983-1024.
[60] Grau B C, Parsia B, Sirin E, et al. Modularity and Web Ontologies [C]. proceedings ofthe KR,2006.198-209.
[61] Guo Y, Heflin J. A scalable approach for partitioning OWL knowledge bases [C].proceedings of the Proceedings of the2nd International Workshop on Scalable SemanticWeb Knowledge Base Systems,2006.636-641.
[62] Wandelt S, M ller R. Towards ABox modularization of semi-expressive descriptionlogics [J]. Applied Ontology,2012.7(2):133-167.
[63] Kr tzsch M, Simancik F, Horrocks I. A description logic primer [J]. arXiv preprintarXiv:12014089,2012.
[64] Horrocks I, Kutz O, Sattler U. The Even More Irresistible SROIQ [J]. KR,2006.6:57-67.
[65] Rudolph S. Foundations of description logics [M]. Reasoning Web SemanticTechnologies for the Web of Data. Springer.2011:76-136.
[66] Baader F, Sattler U. An overview of tableau algorithms for description logics [J]. StudiaLogica,2001.69(1):5-40.
[67] Calvanese D, Carbotta D, Ortiz M. A practical automata-based technique for reasoning inexpressive description logics [C]. proceedings of the Proceedings of the Twenty-Secondinternational joint conference on Artificial Intelligence-Volume Volume Two,2011.AAAI Press,798-804.
[68] Horrocks I, Patel-Schneider P F. Reducing OWL entailment to description logicsatisfiability [M]. The Semantic Web-ISWC2003. Springer.2003:17-29.
[69] Kazakov Y, Motik B. A Resolution-Based Decision Procedure for\boldsymbol {\mathcal {SHOIQ}}[J]. Journal of Automated Reasoning,2008.40(2-3):89-116.
[70] Motik B, Studer R. KAON2–A scalable reasoning tool for the semantic web [C].proceedings of the Proceedings of the2nd European Semantic Web Conference(ESWC’05), Heraklion, Greece,2005.
[71] Antoniou G, Van Harmelen F. Web ontology language: Owl [M]. Handbook onontologies. Springer.2004:67-92.
[72] Group W C O W. OWL2web ontology language document overview [J]. W3CRecommendation,2009.27:1205-1214.
[73] Horrocks I, Patel-Schneider P F, Van Harmelen F. From SHIQ and RDF to OWL: Themaking of a web ontology language [J]. Web semantics: science, services and agents onthe World Wide Web,2003.1(1):7-26.
[74] Lutz C. Description logics with concrete domains--a survey [J].2003.
[75] Artale A, Calvanese D, Kontchakov R, et al. The DL-Lite family and relations [J].Journal of Artificial Intelligence Research,2009.36(1):1-69.
[76] Sirin E, Tao J. Towards Integrity Constraints in OWL [C]. proceedings of the OWLED,2009.
[77] Motik B, Horrocks I, Sattler U. Adding Integrity Constraints to OWL [C]. proceedings ofthe OWLED,2007.
[78] Sengupta K, Krisnadhi A A, Hitzler P. Local closed world semantics: Groundedcircumscription for OWL [M]. The Semantic Web–ISWC2011. Springer.2011:617-632.
[79] McCarthy J. Circumscription—a form of non-monotonic reasoning [J]. Artificialintelligence,1980.13(1):27-39.
[80] Bonatti P A, Lutz C, Wolter F. Description Logics with Circumscription [C]. proceedingsof the KR,2006.400-410.
[81] Bonatti P A, Lutz C, Wolter F. The complexity of circumscription in description logic [J].Journal of Artificial Intelligence Research,2009.35(2):717-773.
[82] Yang Q, You J-H, Feng Z. Integrating Rules and Description Logics by Circumscription[C]. proceedings of the AAAI,2011.
[83] Di Noia T, Di Sciascio E, Donini F M. Semantic Matchmaking as Non-MonotonicReasoning: A Description Logic Approach [J]. J Artif Intell Res(JAIR),2007.29:269-307.
[84] Ouyang D, Cui X, Ye Y. Integrity constraints in OWL ontologies based on groundedcircumscription [J]. Frontiers of Computer Science,2013.7(6):812-821.
[85] Baader F, Sattler U. Expressive number restrictions in description logics [J]. Journal oflogic and computation,1999.9(3):319-350.
[86] Horrocks I, Sattler U. A Tableau Decision Procedure for\mathcal {SHOIQ}[J]. Journalof Automated Reasoning,2007.39(3):249-276.
[87] Ortiz M, Calvanese D, Eiter T. Data complexity of query answering in expressivedescription logics via tableaux [J]. Journal of Automated Reasoning,2008.41(1):61-98.
[88] Lifschitz V. Foundations of logic programming [J]. Principles of knowledgerepresentation,1996.3:69-127.
[89] Guo Y, Pan Z, Heflin J. LUBM: A benchmark for OWL knowledge base systems [J].Web Semantics: Science, Services and Agents on the World Wide Web,2005.3(2):158-182.
[90] Calvanese D, De Giacomo G, Lembo D, et al. DL-Lite: Tractable description logics forontologies [C]. proceedings of the AAAI,2005.602-607.
[91] Acciarri A, Calvanese D, De Giacomo G, et al. QUONTO: querying ontologies [C].proceedings of the AAAI,2005.1670-1671.
[92] Lloyd J W, Topor R W. Making Prolog more expressive [J]. The Journal of LogicProgramming,1984.1(3):225-240.
[93] Horrocks I, Tessaris S. A conjunctive query language for description logic aboxes [C].proceedings of the AAAI/IAAI,2000.399-404.
[94] Glimm B, Horrocks I, Lutz C, et al. Conjunctive query answering for the descriptionlogic [J]. J Artif Intell Res,2008.31:157-204.
[95] Glimm B, Horrocks I, Sattler U. Unions of Conjunctive Queries in SHOQ [C].proceedings of the KR,2008.252-262.
[96] Glimm B, Rudolph S. Conjunctive Query Entailment: Decidable in Spite of O, I, and Q[C]. proceedings of the Description Logics,2009.
[97] Calvanese D, Eiter T, Ortiz M. Answering regular path queries in expressive descriptionlogics: An automata-theoretic approach [C]. proceedings of the AAAI,2007.391-396.
[98] Eiter T, Lutz C, Ortiz M, et al. Query answering in description logics: The knotsapproach [M]. Logic, Language, Information and Computation. Springer.2009:26-36.
[99] Johnson D S, Klug A. Testing containment of conjunctive queries under functional andinclusion dependencies [J]. Journal of Computer and system Sciences,1984.28(1):167-189.
[100] Kazakov Y. Extracting Modules from Ontologies: Theory and Practice [J].2007.
[101] Grau B C, Horrocks I, Kazakov Y, et al. Just the right amount: extracting modulesfrom ontologies [C]. proceedings of the Proceedings of the16th international conferenceon World Wide Web,2007. ACM,717-726.
[102] Hopcroft J E, Tarjan R E. Efficient algorithms for graph manipulation [J].1971.
[103] Wandelt S, M ller R. Island reasoning for ALCHI ontologies [C]. proceedings of theProceedings of the2008conference on Formal Ontology in Information Systems,2008.164-177.
[104] Wandelt S, Müller R. Sound and Complete SHI Instance Retrieval for1Billion ABoxAssertions [C]. proceedings of the10th Int Semantic Web Conference on ScalableSemantic Web Knowledge Base Systems Workshop,2011.
[2] Popov B, Kiryakov A, Ognyanoff D, et al. Towards semantic web informationextraction[C]. proceedings of the Human Language Technologies Workshop at the2ndInternational Semantic Web Conference (ISWC2003),2003.
[3] Gon alves M A, Fox E A, Watson L T. Towards a digital library theory: a formal digitallibrary ontology [J]. International Journal on Digital Libraries,2008.8(2):91-114.
[4] Kruk S R, Kruk E, Stankiewicz K. Evaluation of semantic and social technologies fordigital libraries [M]. Semantic Digital Libraries. Springer.2009:203-214.
[5] McIlraith S A, Son T C, Zeng H. Semantic web services [J]. IEEE intelligent systems,2001.16(2):46-53.
[6] Staab S, Studer R. Handbook on ontologies [M]. Springer,2009.
[7] Dean M, Schreiber G, Bechhofer S, et al. OWL web ontology language reference [J].W3C Recommendation February,2004.
[8] Baader F. The description logic handbook: theory, implementation, and applications [M].Cambridge university press,2003.
[9] Doms A, Schroeder M. GoPubMed: exploring PubMed with the gene ontology [J].Nucleic acids research,2005.33(suppl2):783-786.
[10] Cornet R, de Keizer N. Forty years of SNOMED: a literature review [J]. BMC medicalinformatics and decision making,2008.8(Suppl1).
[11] Brickley D, Miller L. FOAF vocabulary specification0.98[J]. Namespace document,2010.9.
[12] Miller E. An introduction to the resource description framework [J]. Bulletin of theAmerican Society for Information Science and Technology,1998.25(1):15-19.
[13] Beckett D, McBride B. RDF/XML syntax specification (revised)[J]. W3Crecommendation,2004.10.
[14] Berners-Lee T, Connolly D. Notation3(N3): A readable RDF syntax [J]. W3C TeamSubmission (January2008) http://www w3org/TeamSubmission,1998.(3).
[15] Beckett D, Barstow A. N-Triples-W3C RDF Core WG Internal Working Draft [J]. URL:http://www w3org/2001/sw/RDFCore/ntriples,2001.
[16] Beckett D, Berners-Lee T. Turtle-terse RDF triple language [J]. W3C Team Submission,2008.14.
[17] Sirin E, Parsia B, Grau B C, et al. Pellet: A practical owl-dl reasoner [J]. Web Semantics:science, services and agents on the World Wide Web,2007.5(2):51-53.
[18] Haarslev V, Müller R. RACER system description [M]. Automated Reasoning. Springer.2001:701-705.
[19] Tsarkov D, Horrocks I. FaCT++description logic reasoner: System description [M].Automated reasoning. Springer.2006:292-297.
[20] Horrocks I, Motik B, Wang Z. The HermiT OWL Reasoner [C]. proceedings of theProceedings of the1st International Workshop on OWL Reasoner Evaluation(ORE-2012), Manchester, UK,2012.
[21] Kremen P, Kouba Z. Ontology-Driven Information System Design [J]. Systems, Man,and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on,2012.42(3):334-344.
[22] Albarrak K M, Sibley E H. Translating relational&object-relational database modelsinto OWL models [C]. proceedings of the Information Reuse&Integration, IRI'09IEEEInternational Conference on,2009. IEEE,336-341.
[23]郑少兰.关系数据库中本体完整性约束验证方法[J].福建电脑,2013.29(10).
[24]金芝.基于本体的需求自动获取[J].计算机学报,2000.23(5):486-492.
[25]李海波,战德臣,徐晓飞.工作流业务规则语义的完整性验证技术[J].计算机研究与发展,2009.(7):1143-1151.
[26]王莉,梁冰,白海燕.基于本体的科技文献检索框架与技术实现[J].数字图书馆论坛,2012.(7):37-44.
[27] Kowalski R. Logic for data description [M]. Springer,1978.
[28] Lloyd J W, Topor R W. A basis for deductive database systems [J]. The Journal of LogicProgramming,1985.2(2):93-109.
[29] Reiter R. On integrity constraints [C]. proceedings of the Proceedings of the2ndconference on Theoretical aspects of reasoning about knowledge,1988. MorganKaufmann Publishers Inc.,97-111.
[30]Levesque H J. All I know: a study in autoepistemic logic [J]. Artificial Intelligence,1990.42(2):263-309.
[31] Donini F M, Lenzerini M, Nardi D, et al. An epistemic operator for description logics [J].Artificial Intelligence,1998.100(1):225-274.
[32] Donini F M, Nardi D, Rosati R. Description logics of minimal knowledge and negationas failure [J]. ACM Transactions on Computational Logic (TOCL),2002.3(2):177-225.
[33] Calvanese D, De Giacomo G, Lembo D, et al. EQL-Lite: Effective First-Order QueryProcessing in Description Logics [C]. proceedings of the IJCAI,2007.274-279.
[34] Mehdi A, Rudolph S, Grimm S. Epistemic querying of OWL knowledge bases [M]. TheSemantic Web: Research and Applications. Springer.2011:397-409.
[35]梅婧.语义Web本体与规则[D],2007.
[36] Eiter T, Ianni G, Lukasiewicz T, et al. Combining answer set programming withdescription logics for the semantic web [J]. Artificial Intelligence,2008.172(12):1495-1539.
[37] Motik B, Rosati R. Reconciling description logics and rules [J]. Journal of the ACM(JACM),2010.57(5):1-62.
[38] Grosof B N, Horrocks I, Volz R, et al. Description logic programs: Combining logicprograms with description logic [C]. proceedings of the Proceedings of the12thinternational conference on World Wide Web,2003. ACM,48-57.
[39] Rosati R. DL+log: Tight Integration of Description Logics and Disjunctive Datalog [J].KR,2006.6:68-78.
[40] Motik B, Horrocks I, Sattler U. Bridging the gap between OWL and relational databases[J]. Web Semantics: Science, Services and Agents on the World Wide Web,2009.7(2):74-89.
[41] Jeffrey A, Patel-Schneider P F. Integrity Constraints for Linked Data [C]. proceedings ofthe Description Logics,2011.
[42] Tao J, Sirin E, Bao J, et al. Integrity Constraints in OWL [C]. proceedings of the AAAI,2010.
[43] Angles R, Gutierrez C. The expressive power of SPARQL [M]. The SemanticWeb-ISWC2008. Springer.2008:114-129.
[44] Heymans S, McKennirey M, Phillips J. Semantic validation of the use of SNOMED CTin HL7clinical documents [J]. J Biomedical Semantics,2011.2:1-16.
[45] Cyganiak R, Jentzsch A. Linking open data cloud diagram [J]. LOD Community(http://lod-cloud net/),2011.
[46] Wache H, Serafini L, Garc a-Castro R. D2.1.1survey of scalability techniques forreasoning with ontologies[R]. Technical report, Knowledge Web,2004.
[47] Baader F, Brandt S, Lutz C. Pushing the EL envelope [C]. proceedings of the IJCAI,2005.364-369.
[48] Baader F, Brandt S, Lutz C. Pushing the EL envelope further [J].2008.
[49] Calvanese D, De Giacomo G, Lembo D, et al. Tractable reasoning and efficient queryanswering in description logics: The DL-Lite family [J]. Journal of Automated reasoning,2007.39(3):385-429.
[50] Rosati R, Almatelli A. Improving Query Answering over DL-Lite Ontologies [J]. KR,2010.10:290-300.
[51] Kontchakov R, Lutz C, Toman D, et al. The Combined Approach to Query Answering inDL-Lite [C]. proceedings of the KR,2010.
[52] Pérez-Urbina H, Horrocks I, Motik B. Efficient query answering for OWL2[M]. TheSemantic Web-ISWC2009. Springer.2009:489-504.
[53] Hitzler P, Kr tzsch M, Parsia B, et al. OWL2web ontology language primer [J]. W3Crecommendation,2009.27:1-123.
[54] Pan J Z, Thomas E, Zhao Y. Completeness Guaranteed Approximations for OWL-DLQuery Answering [J]. Description Logics,2009.477.
[55] Tserendorj T, Rudolph S, Kr tzsch M, et al. Approximate OWL-reasoning with Screech[M]. Web Reasoning and Rule Systems. Springer.2008:165-180.
[56] Fokoue A, Kershenbaum A, Ma L, et al. The summary abox: Cutting ontologies down tosize [M]. The Semantic Web-ISWC2006. Springer.2006:343-356.
[57] Dolby J, Fokoue A, Kalyanpur A, et al. Scalable semantic retrieval throughsummarization and refinement [C]. proceedings of the AAAI,2007.299-304.
[58] Dolby J, Fokoue A, Kalyanpur A, et al. Scalable highly expressive reasoner (SHER)[J].Web Semantics: Science, Services and Agents on the World Wide Web,2009.7(4):357-361.
[59] Noy N F, Musen M A. The PROMPT suite: interactive tools for ontology merging andmapping [J]. International Journal of Human-Computer Studies,2003.59(6):983-1024.
[60] Grau B C, Parsia B, Sirin E, et al. Modularity and Web Ontologies [C]. proceedings ofthe KR,2006.198-209.
[61] Guo Y, Heflin J. A scalable approach for partitioning OWL knowledge bases [C].proceedings of the Proceedings of the2nd International Workshop on Scalable SemanticWeb Knowledge Base Systems,2006.636-641.
[62] Wandelt S, M ller R. Towards ABox modularization of semi-expressive descriptionlogics [J]. Applied Ontology,2012.7(2):133-167.
[63] Kr tzsch M, Simancik F, Horrocks I. A description logic primer [J]. arXiv preprintarXiv:12014089,2012.
[64] Horrocks I, Kutz O, Sattler U. The Even More Irresistible SROIQ [J]. KR,2006.6:57-67.
[65] Rudolph S. Foundations of description logics [M]. Reasoning Web SemanticTechnologies for the Web of Data. Springer.2011:76-136.
[66] Baader F, Sattler U. An overview of tableau algorithms for description logics [J]. StudiaLogica,2001.69(1):5-40.
[67] Calvanese D, Carbotta D, Ortiz M. A practical automata-based technique for reasoning inexpressive description logics [C]. proceedings of the Proceedings of the Twenty-Secondinternational joint conference on Artificial Intelligence-Volume Volume Two,2011.AAAI Press,798-804.
[68] Horrocks I, Patel-Schneider P F. Reducing OWL entailment to description logicsatisfiability [M]. The Semantic Web-ISWC2003. Springer.2003:17-29.
[69] Kazakov Y, Motik B. A Resolution-Based Decision Procedure for\boldsymbol {\mathcal {SHOIQ}}[J]. Journal of Automated Reasoning,2008.40(2-3):89-116.
[70] Motik B, Studer R. KAON2–A scalable reasoning tool for the semantic web [C].proceedings of the Proceedings of the2nd European Semantic Web Conference(ESWC’05), Heraklion, Greece,2005.
[71] Antoniou G, Van Harmelen F. Web ontology language: Owl [M]. Handbook onontologies. Springer.2004:67-92.
[72] Group W C O W. OWL2web ontology language document overview [J]. W3CRecommendation,2009.27:1205-1214.
[73] Horrocks I, Patel-Schneider P F, Van Harmelen F. From SHIQ and RDF to OWL: Themaking of a web ontology language [J]. Web semantics: science, services and agents onthe World Wide Web,2003.1(1):7-26.
[74] Lutz C. Description logics with concrete domains--a survey [J].2003.
[75] Artale A, Calvanese D, Kontchakov R, et al. The DL-Lite family and relations [J].Journal of Artificial Intelligence Research,2009.36(1):1-69.
[76] Sirin E, Tao J. Towards Integrity Constraints in OWL [C]. proceedings of the OWLED,2009.
[77] Motik B, Horrocks I, Sattler U. Adding Integrity Constraints to OWL [C]. proceedings ofthe OWLED,2007.
[78] Sengupta K, Krisnadhi A A, Hitzler P. Local closed world semantics: Groundedcircumscription for OWL [M]. The Semantic Web–ISWC2011. Springer.2011:617-632.
[79] McCarthy J. Circumscription—a form of non-monotonic reasoning [J]. Artificialintelligence,1980.13(1):27-39.
[80] Bonatti P A, Lutz C, Wolter F. Description Logics with Circumscription [C]. proceedingsof the KR,2006.400-410.
[81] Bonatti P A, Lutz C, Wolter F. The complexity of circumscription in description logic [J].Journal of Artificial Intelligence Research,2009.35(2):717-773.
[82] Yang Q, You J-H, Feng Z. Integrating Rules and Description Logics by Circumscription[C]. proceedings of the AAAI,2011.
[83] Di Noia T, Di Sciascio E, Donini F M. Semantic Matchmaking as Non-MonotonicReasoning: A Description Logic Approach [J]. J Artif Intell Res(JAIR),2007.29:269-307.
[84] Ouyang D, Cui X, Ye Y. Integrity constraints in OWL ontologies based on groundedcircumscription [J]. Frontiers of Computer Science,2013.7(6):812-821.
[85] Baader F, Sattler U. Expressive number restrictions in description logics [J]. Journal oflogic and computation,1999.9(3):319-350.
[86] Horrocks I, Sattler U. A Tableau Decision Procedure for\mathcal {SHOIQ}[J]. Journalof Automated Reasoning,2007.39(3):249-276.
[87] Ortiz M, Calvanese D, Eiter T. Data complexity of query answering in expressivedescription logics via tableaux [J]. Journal of Automated Reasoning,2008.41(1):61-98.
[88] Lifschitz V. Foundations of logic programming [J]. Principles of knowledgerepresentation,1996.3:69-127.
[89] Guo Y, Pan Z, Heflin J. LUBM: A benchmark for OWL knowledge base systems [J].Web Semantics: Science, Services and Agents on the World Wide Web,2005.3(2):158-182.
[90] Calvanese D, De Giacomo G, Lembo D, et al. DL-Lite: Tractable description logics forontologies [C]. proceedings of the AAAI,2005.602-607.
[91] Acciarri A, Calvanese D, De Giacomo G, et al. QUONTO: querying ontologies [C].proceedings of the AAAI,2005.1670-1671.
[92] Lloyd J W, Topor R W. Making Prolog more expressive [J]. The Journal of LogicProgramming,1984.1(3):225-240.
[93] Horrocks I, Tessaris S. A conjunctive query language for description logic aboxes [C].proceedings of the AAAI/IAAI,2000.399-404.
[94] Glimm B, Horrocks I, Lutz C, et al. Conjunctive query answering for the descriptionlogic [J]. J Artif Intell Res,2008.31:157-204.
[95] Glimm B, Horrocks I, Sattler U. Unions of Conjunctive Queries in SHOQ [C].proceedings of the KR,2008.252-262.
[96] Glimm B, Rudolph S. Conjunctive Query Entailment: Decidable in Spite of O, I, and Q[C]. proceedings of the Description Logics,2009.
[97] Calvanese D, Eiter T, Ortiz M. Answering regular path queries in expressive descriptionlogics: An automata-theoretic approach [C]. proceedings of the AAAI,2007.391-396.
[98] Eiter T, Lutz C, Ortiz M, et al. Query answering in description logics: The knotsapproach [M]. Logic, Language, Information and Computation. Springer.2009:26-36.
[99] Johnson D S, Klug A. Testing containment of conjunctive queries under functional andinclusion dependencies [J]. Journal of Computer and system Sciences,1984.28(1):167-189.
[100] Kazakov Y. Extracting Modules from Ontologies: Theory and Practice [J].2007.
[101] Grau B C, Horrocks I, Kazakov Y, et al. Just the right amount: extracting modulesfrom ontologies [C]. proceedings of the Proceedings of the16th international conferenceon World Wide Web,2007. ACM,717-726.
[102] Hopcroft J E, Tarjan R E. Efficient algorithms for graph manipulation [J].1971.
[103] Wandelt S, M ller R. Island reasoning for ALCHI ontologies [C]. proceedings of theProceedings of the2008conference on Formal Ontology in Information Systems,2008.164-177.
[104] Wandelt S, Müller R. Sound and Complete SHI Instance Retrieval for1Billion ABoxAssertions [C]. proceedings of the10th Int Semantic Web Conference on ScalableSemantic Web Knowledge Base Systems Workshop,2011.