Concept constructing in the description logic SROIQ based on minimal RDF reasoning

被引：0

作者：

Kanciwa K. ^{[1
]}

Nagai T. ^{[2
]}

机构：

[1] Department of Computer and Network Engineering, Graduate School of Informatics and Engineering, The University of Electro-Communications

[2] Electrical Engineering and Computer Science, Graduate School of Engineering, Iwate University

来源：

Transactions of the Japanese Society for Artificial Intelligence | 2020年 / 35卷 / 01期

基金：

日本学术振兴会;

关键词：

Closed world assumption; Description logic; Minimal reasoning; RDF schema;

D O I：

10.1527/tjsai.B-J62

中图分类号：

学科分类号：

摘要：

In the area of the Semantic Web, the expressive description logic SROIQ corresponding to OWL2 provides us rich reasoning and learning tasks for ontologies, e.g., inference engine, query-answering system, and concept learning. However, unlike simple ontologies in RDF graphs, it is not easy for users to build ontologies using the logical and complex expressions of SROIQ. In this paper, we propose (i) minimal model reasoning in the description logic SROIQ for RDF graphs and (ii) a SROIQ-concept constructing algorithm for the classes, properties and individuals in each RDF graph. In the minimal models of RDF graphs based on the closed world assumption (CWA), we prove the completeness, soundness and complexity of the minimal model r0065asoning in the description logic SROIQ. We define decidable SROIQ-concept constructing in a unique interpretation of SROIQ-conc&pts based on the mini-mal model reasoning. For infinite SROIQ-concept combinations constructed by classes, properties and individuals (even less expressive description logic concepts), our const|ructing method removes semantically identifying concepts, e.g., An A, AnAnA,…if concept name A exists, in the minimal models. As a main theoretical result, we show the decidability and complexity of the concept constructing algorithm. We formalize two applications to the concept constructing algorithm as a SROIQ-concept query system and SROIQ-concepl learning for RDF graphs. The query system for RDF graphs returns the answers of expressive SROIQ queries including concept variables. The concept learning enables us to logically induce SROIQ-concepts from positive and negative examples in knowledge bases. © 2020, Japanese Society for Artificial Intelligence. All rights reserved.

引用

共 17 条

[1]

Analyti A., Antoniou G., Damasio C.V., Wagner G., Extended RDF as a semantic foundation of rule markup languages, Journal of Artificial Intelligence Research, 32, 1, pp. 37-94, (2008)

[2]

Baader F., Calvanese D., McGuinness D.L., Nardi D., Patel-Schneider P.F., The Description Logic Handbook: Theory, Implementation, and Applications, (2003)

[3]

Baader F., Gil O.F., Marantidis P., Matching in the description logic TCo with respect to general TBoxes, Proceedings of the 2Nd International Conference on Logic for Programming, pp. 76-94, (2018)

[4]

Brickley D., Guha R., RDF Schema 1.1, W3C Recommendation, (2014)

[5]

Buhmann L., Lehmann J., Westphal P., DL-Learner-A framework for inductive learning on the semantic web, J. Web Semant., 39, pp. 15-24, (2016)

[6]

Buhmann L., Lehmann J., Westphal P., Bin S., DL-Learner-structured machine learning on semantic web data, Companion Proceedings of the Web Conference 2018 (WWW 2018), pp. 467-471, (2018)

[7]

Cyganiak R., Wood D., Lanthaler M., RDF 1.1 Concepts and Abstract Syntax, (2014)

[8]

Hayes P.J., Patel-Schneider P.F., RDF 1.1 Semantics, (2014)

[9]

Hitzler P., Krotzsch M., Parsia B., Patel-Schneider P.F., Rudolph S., OWL2 Web Ontology Lan-Guage Primer, (2012)

[10]

Horrocks I., Kutz O., Sattler U., The even mote irresistible SROIQ, International Conference on Principles of Know Ledge Representation and Reasoning, pp. 57-67, (2006)

← 1 2 →