Effective method for detecting error causes from incoherent biological ontologies

Yu Zhang; Haitao Wu; Jinfeng Gao; Yongtao Zhang; Ruxian Yao; Yuxiang Zhu; Yu Zhang; Haitao Wu; Jinfeng Gao; Yongtao Zhang; Ruxian Yao; Yuxiang Zhu

doi:10.3934/mbe.2022349

Mathematical Biosciences and Engineering

2022, Volume 19, Issue 7: 7388-7409. doi: 10.3934/mbe.2022349

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Effective method for detecting error causes from incoherent biological ontologies

1.
College of Information Engineering, Huanghuai University, Zhumadian 463000, China
2.
Henan Key Laboratory of Smart Lighting, Zhumadian 463000, China
3.
Henan Joint International Research Laboratory of Behavior Optimization Control for Smart Robots, Zhumadian 463000, China
4.
Department of Information and Electronic Engineering, Shangqiu Institute of Technology, Shangqiu 476000, China

Academic Editor: Xiangtao Li

Received: 05 December 2021 Revised: 02 April 2022 Accepted: 08 April 2022 Published: 19 May 2022

Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies based on four patterns. Each pattern is represented as a directed graph and the depth-first search strategy is used to find the axiom paths relevant to the MinAs of the unsatisfiable class. However, DOBP is inefficient when a debugging large incoherent ontology with a lot of unsatisfiable classes. To solve the problem, we first extract a module responsible for the erroneous classes and then compute the MinAs based on the extracted module. The basic idea of module extraction is that rather than computing MinAs based on the original ontology $ \mathcal{O} $, they are computed based on a module $ \mathcal{M} $ extracted from $ \mathcal{O} $. $ \mathcal{M} $ provides a smaller search space than $ \mathcal{O} $ because $ \mathcal{M} $ is considerably smaller than $ \mathcal{O} $. The experimental results on biological ontologies show that the module extracted using the Module-DOBP method is smaller than the original ontology. Lastly, our proposed approach optimized with the module extraction algorithm is more efficient than the DOBP method both for large-scale ontologies and numerous unsatisfiable classes.
- minimal axioms sets,
- unsatisfiable class,
- incoherent ontology,
- DOBP,
- module-DOBP
Citation: Yu Zhang, Haitao Wu, Jinfeng Gao, Yongtao Zhang, Ruxian Yao, Yuxiang Zhu. Effective method for detecting error causes from incoherent biological ontologies[J]. Mathematical Biosciences and Engineering, 2022, 19(7): 7388-7409. doi: 10.3934/mbe.2022349

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Abstract

Computing the minimal axiom sets (MinAs) for an unsatisfiable class is an important task in incoherent ontology debugging. Ddebugging ontologies based on patterns (DOBP) is a pattern-based debugging method that uses a set of heuristic strategies based on four patterns. Each pattern is represented as a directed graph and the depth-first search strategy is used to find the axiom paths relevant to the MinAs of the unsatisfiable class. However, DOBP is inefficient when a debugging large incoherent ontology with a lot of unsatisfiable classes. To solve the problem, we first extract a module responsible for the erroneous classes and then compute the MinAs based on the extracted module. The basic idea of module extraction is that rather than computing MinAs based on the original ontology $ \mathcal{O} $, they are computed based on a module $ \mathcal{M} $ extracted from $ \mathcal{O} $. $ \mathcal{M} $ provides a smaller search space than $ \mathcal{O} $ because $ \mathcal{M} $ is considerably smaller than $ \mathcal{O} $. The experimental results on biological ontologies show that the module extracted using the Module-DOBP method is smaller than the original ontology. Lastly, our proposed approach optimized with the module extraction algorithm is more efficient than the DOBP method both for large-scale ontologies and numerous unsatisfiable classes.

References

[1]	F. Baader, D. Calvanese, D. McGuinness, D. Nardi, P. F. Patel-Schneider, The Description Logic Handbook: Theory, Implementation, and Applications, Cambridge University Press, 2003.
[2]	I. Horrocks, P. F. Patel-Schneider, F. van Harmelen, From SHIQ and RDF to OWL: The making of a web ontology language, J. Web Semantics, 1 (2003), 7–26. https://doi.org/10.1016/j.websem.2003.07.001 doi: 10.1016/j.websem.2003.07.001
[3]	J. S. C. Lam, D. Sleeman, J. Z. Pan, W. Vasconcelos, A fine-grained approach to resolving unsatisfiable ontologies, J. Data Semantics X, 10 (2008), 62–95. https://doi.org/10.1007/978-3-540-77688-8_3 doi: 10.1007/978-3-540-77688-8_3
[4]	L. Qiu, Y. Liu, Y. Song, B. Zhang, A conflict diagnosis approach of changing sequences in gene ontology evolution, Int. J. Control Autom., 7 (2014), 269–284.
[5]	X. W. Zhao, X. T. Li, Z. Q. Ma, M. H. Yin, Identify DNA-binding proteins with optimal Chou's amino acid composition, Proteins Pept. Lett., 19 (2012), 398–405. https://doi.org/10.2174/092986612799789404 doi: 10.2174/092986612799789404
[6]	J. Zhang, Y. Zhang, Z. Ma, In silico prediction of human secretory proteins in plasma based on discrete firefly optimization and application to Cancer biomarkers identification, Front. Genet., 10 (2019), 542. https://doi.org/10.3389/fgene.2019.00542 doi: 10.3389/fgene.2019.00542
[7]	J. Zhang, H. Chai, G. Yang, Z. Ma, Prediction of bioluminescent proteins by using sequence-derived features and lineage-specific scheme, BMC Bioinf., 18 (2017), 1–13. https://doi.org/10.1186/s12859-017-1709-6 doi: 10.1186/s12859-017-1709-6
[8]	S. Schlobach, Z. Huang, R. Cornet, F. Harmelen, Debugging incoherent terminologies, J. Autom. Reasoning, 39 (2007), 317–349. https://doi.org/10.1007/s10817-007-9076-z doi: 10.1007/s10817-007-9076-z
[9]	Y. Zhang, D. Ouyang, Y. Ye, Glass-box debugging algorithm based on unsatisfiable dependent paths, IEEE Access, 5 (2017), 18725–18736. https://doi.org/10.1109/ACCESS.2017.2753381 doi: 10.1109/ACCESS.2017.2753381
[10]	F. Simančík, B. Motik, I. Horrocks, Consequence-based and fixed-parameter tractable reasoning in description logics, Artif. Intell., 209 (2014), 29–77. https://doi.org/10.1016/j.artint.2014.01.002 doi: 10.1016/j.artint.2014.01.002
[11]	Z. Zhou, G. Qi, B. Suntisrivaraporn, A new method of finding all justifications in OWL 2 EL, in 2013 IEEE/WIC/ACM International Conferences on Web Intelligence, (2013), 213–220. https://doi.org/10.1109/WI-IAT.2013.31
[12]	X. Fu, G. Qi, Y. Zhang, Z. Zhou, Graph-based approaches to debugging and revision of terminologies in DL-Lite, Knowl. Based Syst., 100 (2016), 1–12. https://doi.org/10.1016/j.knosys.2016.01.039 doi: 10.1016/j.knosys.2016.01.039
[13]	B. C. Grau, I. Horrocks, Y. Kazakov, U. Sattler, A logical framework for modularity of ontologies, in Proceedings of the 20th International Joint Conference on Artificial Intelligence, (2007), 298–303.
[14]	B. Grau, I. Horrocks, Y. Kazakov, U. Sattler, Just the right amount: extracting modules from ontologies, in Proceedings of the 16th international conference on World Wide Web, (2007), 717–726. https://doi.org/10.1145/1242572.1242669
[15]	B. Cuenca Grau, C. Halaschek-Wiener, Y. Kazakov, History matters: incremental ontology reasoning using modules, in Proceedings of the 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, (2007), 183–196. https://doi.org/10.1007/978-3-540-76298-0_14
[16]	A. Kalyanpur, B. Parsia, M. Horridge, E. Sirin, Finding all justifications of OWL DL entailments, in Proceedings of 6th International Semantic Web Conference, ISWC 2007 and 2nd Asian Semantic Web Conference, (2007), 267–280. https://doi.org/10.1007/978-3-540-76298-0_20
[17]	M. Horridge, Justification Based Explanation in Ontologies, Ph.D thesis, University of Manchester in Manchester, 2011.
[18]	B. Suntisrivaraporn, Module Extraction and Incremental Classification: A pragmatic approach for $\mathcal EL$+ ontologies, in Proceedings of the 5th European Semantic Web Conference, (2008), 230–244. https://doi.org/10.1007/978-3-540-68234-9_19
[19]	J. Du, G. Qi, Q. Ji, Goal-directed module extraction for explaining OWL DL entailments, in Proceedings of the 8th International Semantic Web Conference, (2009), 163–179. https://doi.org/10.1007/978-3-642-04930-9_11
[20]	J. Du, G. Qi, Decomposition-Based Optimization for Debugging of Inconsistent OWL DL Ontologies, in Proceedings of the 4th International Conference on the Knowledge Science, Engineering and Management, (2010), 88–100. https://doi.org/10.1007/978-3-642-15280-1_11
[21]	M. Gao, Y. Ye, D. Ouyang, B. Wang, Finding justifications by approximating core for large-scale ontologies, in Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI2019), (2019), 6432–6433.
[22]	Y. Zhang, R. Yao, D. Ouyang, J. Gao, F. Liu, Debugging incoherent ontology by extracting a clash module and identifying root unsatisfiable concepts, Knowl. -Based Syst., 223 (2021), 107043. https://doi.org/10.1016/j.knosys.2021.107043 doi: 10.1016/j.knosys.2021.107043
[23]	Y. Zhang, D. Ouyang, Y. Ye, An optimization strategy for debugging incoherent terminologies in dynamic environments, IEEE Access, 5 (2017), 24284–24300. https://doi.org/10.1109/ACCESS.2017.2758521 doi: 10.1109/ACCESS.2017.2758521
[24]	Q. Ji, Z. Gao, Z. Huang, Study of ontology debugging approaches based on the criterion set BLUEI2CI, in Proceedings of the 6th Chinese Semantic Web Symposium and 1st Chinese Web Science Conference, (2013), 251–264. https://doi.org/10.1007/978-1-4614-6880-6_22
[25]	Q. Ji, Z. Gao, Z. Huang, M. Zhu, Measuring effectiveness of ontology debugging systems, Knowl. -Based Syst., 71 (2014), 169–186. https://doi.org/10.1016/j.knosys.2014.07.023 doi: 10.1016/j.knosys.2014.07.023
[26]	Y. Ye, X. Cui, D. Ouyang, Extracting a justification for OWL ontologies by critical axioms, Front. Comput. Sci., 14 (2020), 55–64. https://doi.org/10.1007/s11704-019-7267-5 doi: 10.1007/s11704-019-7267-5
[27]	J. Gao, D. Ouyang, Y. Ye, Exploring duality on ontology debugging, Appl. Intell., 50 (2020), 620–633. https://doi.org/10.1007/s10489-019-01528-y doi: 10.1007/s10489-019-01528-y
[28]	J. Du, G. Qi, X. Fu, A practical fine-grained approach to resolving incoherent OWL 2 DL terminologies, in Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, (2014), 919–928. https://doi.org/10.1145/2661829.2662046
[29]	D. Fleischhacker, C. Meilicke, J. Völker, M. Niepert, Computing incoherence explanations for learned ontologies, in Proceedings of the 7th International Conference on the Web Reasoning and Rule Systems, (2013), 80–94. https://doi.org/10.1007/978-3-642-39666-3_7
[30]	G. Flouris, Z. Huang, J. Z. Pan, D. Plexousakis, H. Wache, Inconsistencies, negations and changes in ontologies, in Proceedings of the Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference, (2006), 1295–1300.
[31]	E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, Y. Katz, Pellet: a practical owl-dl reasoner, J. Web Semantics, 5 (2007), 51–53. https://doi.org/10.1016/j.websem.2007.03.004 doi: 10.1016/j.websem.2007.03.004
[32]	R. Shearer, B. Motik, I. Horrocks, HermiT: a highly-efficient OWL reasoner, in Proceedings of the Fifth OWLED Workshop on OWL: Experiences and Directions, collocated with the 7th International Semantic Web Conference (ISWC-2008), (2008), 1–10.
[33]	D. Tsarkov, I. Horrocks, FaCT++ description logic reasoner: system description, in International joint conference on automated reasoning, (2006), 292–297. https://doi.org/10.1007/11814771_26
[34]	F. Baader, B. Suntisrivaraporn, Debugging SNOMED CT using axiom pinpointing in the description logic EL, in Proceedings of the 3rd International Conference on Knowledge Representation in Medicine, 410 (2008), 1–7.
[35]	B. C. Grau, I. Horrocks, Y. Kazakov, U. Sattler, Modular reuse of ontologies: theory and practice, J. Artif. Intell. Res., 31 (2008), 273–318. https://doi.org/10.1613/jair.2375 doi: 10.1613/jair.2375
[36]	H. Wang, M. Horridge, A. Rector, N. Drummond, J. Seidenberg, Debugging OWL-DL ontologies: a heuristic approach, in Proceedings of the 4th International Semantic Web Conference, (2005), 745–757. https://doi.org/10.1007/11574620_53
[37]	Q. Ji, Z. Gao, Z. Huang, M. Zhu, An efficient approach to debugging ontologies based on patterns, in Proceedings of the Semantic Web-Joint International Semantic Technology Conference, (2011), 425–433. https://doi.org/10.1007/978-3-642-29923-0_33
[38]	Ó. Corcho, C. Roussey, L. M. Vilches-Blázquez, I. Perez, Pattern-based OWL ontology debugging guidelines, in Proceedings of the Workshop on Ontology Patterns (WOP 2009), (2009), 1–15.

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