Dr. Yevgeny Kazakov

My main research interests are knowledge representation and automated reasoning, and specifically, reasoning support for description logics, ontology languages, such as OWL, and decidable fragments of first-order logic. I have been actively working on the topic of modularity in ontologies together with Bernardo Cuenca-GrauIan Horrocks, and Uli Sattler, and also did some theoretical research on extensions of description logics, modal logics, and ontology languages with expressive features, such as complex role inclusion axioms, graded modalities / counting, role conjunctions, and conjunctive queries. Studying theoretical properties of various reasoning tasks is an exciting research area, but it is even more satisfying to develop procedures that work in practice. I have been involved in the development of a few ontology reasoning systems, such as CBConDOR, and ELK, which implement optimized consequence-based reasoning procedures, and I am interested in almost all aspects of algorithm optimizations, such as efficient data structures, goal-directed, incremental reasoning, and concurrency.

Projects

Organisational Activities

  • General co-chair of the Description Logic Workshop 2013
  • Guest Editor of the JAIR special track on Description Logics
  • PC co-chair of the Description Logic Workshop 2012
  • PC member of conferences IJCAI 2016, KR 2016, IJCAI 2015, ISWC 2014, KR 2014, IJCAI 2013, IJCAR 2012, KR 2012, IJCAI 2011, AAAI 2010, IJCAI 2009, ESWC 2009, ESWC 2008, ECAI 2008, ISWC 2007, and workshops DL 2016, PAAR 2016, DL 2015, IWIL 2015, ORE 2015, PAAR 2014, DL 2014, ORE 2014, ORE 2013, AIW 2012, DL 2012, IWIL 2012, PAAR 2012, DL 2011, DL 2010, DL 2009, DL 2007.
  • Invited referee of Artificial Intelligence Journal (AIJ), Journal of Applied Logic (JAL), Journal of Artificial Intelligence Research (JAIR), Journal of Automated Reasoning (JAR), Journal of Logic and Computation (JLC), Journal of Data Semantics (JODS), Journal of Symbolic Computation (JSC), Journal of Web Semantics (JWS), SIAM Journal of Computing (SICOMP), Journal of Theoretical Computer Science (TCS), Journal o Theory and Practice of Logic Programming (TPLP), conferences IJCAR 2016, LICS 2015, TABLEAUX 2015, AAAI 2014, LICS 2013, ISWC 2012, AIMSA 2010, IJCAR 2010, KR 2010, TCS 2010, LICS 2009, WWW 2009, KR 2008, RTA 2008, WWW 2008, IJCAI 2007, LPAR 2007, TABLEAUX 2007, IJCAR 2006, KI 2005, LPAR 2006, TABLEAUX 2005, LICS 2004, CSL 2004, LPAR 2003, and workshops DL 2008, M4M 2005.

Teaching

SS 2016: Algorithms for Knowledge Representation

SS 2015: Algorithms for Knowledge Representation

SS 2014: Algorithms for Knowledge Representation

SS 2013: Automated Theorem Proving

Doctoral Students

Contact

  • email: yevgeny.kazakov(at)uni-ulm.de
  • phone: +49 (0)731/50-24110
  • fax:     +49 (0)731/50-24119
  • Postal Address

  • Yevgeny Kazakov
  • University of Ulm
  • Institute of Artificial Intelligence
  • D-89069 Ulm
  • Office

  • James-Franck-Ring
  • building O27, level 4
  • room 423

Publications

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Show keywords Show abstracts

62.
pdf
Glimm, Birte; Kazakov, Yevgeny; Tran, Trung-Kien
Ontology Materialization by Abstraction Refinement in Horn SHOIF
Proceedings of the 29th International Workshop on Description Logics (DL 2016) Volume 1577 of CEUR Workshop Proceedings
Publisher: CEUR-WS.org,
2016

Tags: AutomatedReasoning

61.
pdf
Glimm, Birte; Kazakov, Yevgeny; Kollia, Ilianna; Stamou, Giorgos
Lower and Upper Bounds for SPARQL Queries over OWL Ontologies
Proceedings of the 28th International Workshop on Description Logics (DL 2015)
Publisher: CEUR Workshop Proceedings,
2015

Keywords: Description Logics, Query Answering, Semantic Web, SPARQL

Tags: AutomatedReasoning

60.
pdf
Kazakov, Yevgeny; Klinov, Pavel
Advancing {ELK}: Not Only Performance Matters
In Diego Calvanese and Boris Konev, editor, DL Volume 1350 of CEUR Workshop Proceedings
Publisher: CEUR-WS.org,
2015

Tags: KnowledgeModeling, ELK, LiveOntologies

Abstract: This paper reports on the recent development of ELK, a consequence-based reasoner for $\mathcal{EL^+_\bot}$ ontologies. It covers novel reasoning techniques which aim at improving efficiency and providing foundation for new reasoning services. On the former front we present a simple optimization for handling of role composition axioms, such as transitivity, which substantially reduces the number of rule applications. For the latter, we describe a new rule application strategy that takes advantage of concept definitions to avoid many redundant inferences without making rules dependent on derived conclusions. This improvement is not visible to the end user but considerably simplifies implementation for incremental reasoning and proof generation. We also present a rewriting of low-level inferences used by ELK to higher-level proofs that can be defined in the standard DL syntax, and thus be used for automatic verification of reasoning results or (visual) ontology debugging. We demonstrate the latter capability using a new ELK Prot\'eg\'e plugin.

59.
pdf
Glimm, Birte; Kazakov, Yevgeny; Kollia, Ilianna; Stamou, Giorgos
Lower and Upper Bounds for SPARQL Queries over OWL Ontologies
Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015)
Publisher: AAAI Press,
2015

Keywords: Description Logics, Query Answering, Semantic Web, SPARQL

Tags: AutomatedReasoning

58.
pdf
Kazakov, Yevgeny; Krötzsch, Markus; Simančík, František
The Incredible {ELK}: From Polynomial Procedures to Efficient Reasoning with {EL} Ontologies
JAR, 53(1):1-61
2014

Tags: KnowledgeModeling, ELK, LiveOntologies

Abstract: EL is a simple tractable Description Logic that features conjunctions and existential restrictions. Due to its favorable computational properties and relevance to existing ontologies, EL has become the language of choice for terminological reasoning in biomedical applications, and has formed the basis of the OWL EL profile of the Web ontology language OWL. This paper describes ELK---a high performance reasoner for OWL EL ontologies---and details various aspects from theory to implementation that make ELK one of the most competitive reasoning systems for EL ontologies available today.

57.
pdf
Glimm, Birte; Kazakov, Yevgeny; Liebig, Thorsten; Tran, Trung-Kien; Vialard, Vincent
Abstraction Refinement for Ontology Materialization
Proceedings of the 13th International Semantic Web Conference (ISWC 2014) Volume 8797 of Lecture Notes in Computer Science , page 180-195.
Publisher: Springer-Verlag,
2014

Keywords: Reasoning, Description Logics, Optimisations, Optimizations

Tags: AutomatedReasoning

56.
pdf
Glimm, Birte; Kazakov, Yevgeny; Liebig, Thorsten; Tran, Trung-Kien; Vialard, Vincent
Abstraction Refinement for Ontology Materialization
Proceedings of the 27th International Workshop on Description Logics (DL 2014) Volume 1193 of CEUR Workshop Proceedings , page 180-195.
Publisher: CEUR-WS.org,
2014

Keywords: Reasoning, Description Logics, Optimisations, Optimizations

Tags: AutomatedReasoning

55.
pdf
Kazakov, Yevgeny; Klinov, Pavel
Goal-Directed Tracing of Inferences in {EL} Ontologies
ISWC Volume 8797 of Lecture Notes in Computer Science , page 196--211.
Publisher: Springer,
2014

Tags: KnowledgeModeling, ELK, LiveOntologies

Abstract: EL is a family of tractable Description Logics (DLs) that is the basis of the OWL 2 EL profile. Unlike for many expressive DLs, reasoning in EL can be performed by computing a deductively-closed set of logical consequences of some specific form. In some ontology-based applications, e.g., for ontology debugging, knowing the logical consequences of the ontology axioms is often not sufficient. The user also needs to know from which axioms and how the consequences were derived. Although it is possible to record all inference steps during the application of rules, this is usually not done in practice to avoid the overheads. In this paper, we present a goal-directed method that can generate inferences for selected consequences in the deductive closure without re-applying all rules from scratch. We provide an empirical evaluation demonstrating that the method is fast and economical for large EL ontologies. Although the main benefits are demonstrated for EL reasoning, the method can be potentially applied to many other procedures based on deductive closure computation using fixed sets of rules.

54.
pdf
Kazakov, Yevgeny; Klinov, Pavel
Bridging the Gap between Tableau and Consequence-Based Reasoning
In Meghyn Bienvenu and Magdalena Ortiz and Riccardo Rosati and Mantas Simkus, editor, DL Volume 1193 of CEUR Workshop Proceedings , page 579-590.
Publisher: CEUR-WS.org,
2014

Tags: KnowledgeModeling, ELK, LiveOntologies

Abstract: We present a non-deterministic consequence-based procedure for the description logic ALCHI. Just like the similar style (deterministic) procedures for EL and Horn-SHIQ, our procedure explicitly derives subsumptions between concepts, but due to non-deterministic rules, not all of these subsumptions are consequences of the ontology. Instead, the consequences are only those subsumptions that can be derived regardless of the choices made in the application of the rules. This is similar to tableau-based procedures, for which an ontology is inconsistent if every expansion of the tableau eventually results in a clash. We report on a preliminary experimental evaluation of the procedure using a version of SNOMED CT with disjunctions, which demonstrates some promising potential.

53.
pdf
Kazakov, Yevgeny; Klinov, Pavel
Goal-Directed Tracing of Inferences in {EL} Ontologies
In Meghyn Bienvenu and Magdalena Ortiz and Riccardo Rosati and Mantas Simkus, editor, DL Volume 1193 of CEUR Workshop Proceedings , page 221-232.
Publisher: CEUR-WS.org,
2014

Tags: KnowledgeModeling, ELK, LiveOntologies

Abstract: EL is a family of tractable Description Logics (DLs) that is the basis of the OWL 2 EL profile. Unlike for many expressive DLs, reasoning in EL can be performed by computing a deductively-closed set of logical consequences of some specific form. In some ontology-based applications, e.g., for ontology debugging, knowing the logical consequences of the ontology axioms is often not sufficient. The user also needs to know from which axioms and how the consequences were derived. Although it is possible to keep track of all inferences applied during reasoning, this is usually not done in practice to avoid the overheads. In this paper, we present a goal-directed method that can generate inferences for selected consequences in the deductive closure without re-applying all rules from scratch. We provide an empirical evaluation demonstrating that the method is fast and economical for large EL ontologies. Although the main benefits are demon- strated for EL reasoning, the method can be easily extended to other procedures based on deductive closure computation using fixed sets of rules.

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