Modular Structures and Atomic Decomposition in Ontologies

Vescovo, Chiara Del; Horridge, Matthew; Parsia, Bijan; Sattler, Ulrike; Schneider, Thomas; Zhao, Haoruo

doi:10.1613/jair.1.12151

Cited by 18 publications

(32 citation statements)

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“…To have a realistic mix of CIs and assertions that reflects the typical shape of the background ontology, observations in (K) were generated by selecting the given number of axioms from the background ontology, which were then removed from the background. Since in large ontologies, a fully random selection would result in an observation of unrelated axioms, we first extracted a subset of at least 100 axioms by combining randomly selected genuine modules: genuine modules are small subsets of the ontology that contain a given axiom and preserve all entailments over the signature of the subset, and thus contain only axioms that in some way interact with each other [Vescovo et al, 2011]. From these subsets of the ontology, which contained between 100 and 20,979 axioms (median 199), we generated the observations by random selection.…”

Section: Discussionmentioning

confidence: 99%

Signature-Based Abduction for Expressive Description Logics

Koopmann

Del-Pinto

Tourret

et al. 2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

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Signature-based abduction aims at building hypotheses over a specified set of names, the signature, that explain an observation relative to some background knowledge. This type of abduction is useful for tasks such as diagnosis, where the vocab- ulary used for observed symptoms differs from the vocabulary expected to explain those symptoms. We present the first complete method solving signature-based abduction for observations expressed in the expressive description logic ALC, which can include TBox and ABox axioms. The method is guaranteed to compute a finite and complete set of hypotheses, and is evaluated on a set of realistic knowledge bases.

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Section: Discussionmentioning

confidence: 99%

Signature-Based Abduction for Expressive Description Logics

Koopmann

Del-Pinto

Tourret

et al. 2020

Proceedings of the Seventeenth International Conference on Principles of Knowledge Representation and Reasoning

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“…Inseparability relations and their robustness properties have been introduced and studied as fundamental requirements to modules by Konev et al (2009). Further module properties have been defined and discussed in various places in the literature, mostly in the context of a specific module notion, such as self-containment and depletingness (Kontchakov et al 2009;Sattler, Schneider, and Zakharyaschev 2009;Kontchakov, Wolter, and Zakharyaschev 2010;Nortjé, Britz, and Meyer 2013a;Gatens, Konev, and Wolter 2014;Armas Romero et al 2016;Del Vescovo et al 2020), justification-preservation (Armas Romero et al 2016Peñaloza et al 2017;Chen, Ludwig, and Walther 2018;Chen et al 2019;Koopmann and Chen 2020). Del Vescovo et al (2020) identified module properties required by AD and briefly discussed those for LBMs, MEX, DBMs, and RBMs.…”

Section: Related Workmentioning

confidence: 99%

“…Decomposition aims at computing the modular structure of a TBox-a representative subset of all modules together with their logical interactions. This structure can be used to better understand the TBox, aid its collaborative design, and optimize tool support (Cuenca Grau et al 2006;Del Vescovo et al 2020). Among the available techniques, atomic decomposition (AD) (Del Vescovo et al 2011) stands out by its efficiency and genericness: the underlying algorithm is based on a linear number of module extractions, for a suitable module notion.…”

Section: Introductionmentioning

confidence: 99%

“…This structure can be used to better understand the TBox, aid its collaborative design, and optimize tool support (Cuenca Grau et al 2006;Del Vescovo et al 2020). Among the available techniques, atomic decomposition (AD) (Del Vescovo et al 2011) stands out by its efficiency and genericness: the underlying algorithm is based on a linear number of module extractions, for a suitable module notion. Originally based on locality-based modules (LBMs) (Cuenca Grau et al 2008), the AD framework was recently shown to work with any module extraction function m that yields uniquely determined Σ-inseparable subsets of the input TBox which satisfy certain module properties, among them self-containment (Del Vescovo et al 2020).…”

Section: Introductionmentioning

confidence: 99%

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Properties of Module Notions and Atomic Decomposition

Nolte

Schneider

2021

Proceedings of the Eighteenth International Conference on Principles of Knowledge Representation and Reasoning

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Various properties of ontology modules have been studied, such as coverage, self-containment, depletingness, monotonicity, preservation of justifications. These properties are important from a theoretical and practical point of view because they ensure, e.g., that modules have meaningful interfaces, can be used for ontology debugging, or are suitable for computing a meaningful modular structure of an ontology, such as via atomic decomposition (AD). Given one of the many existing module notions, it is not always obvious whether it satisfies a given property, particularly when the module extraction procedure is based on normalization. We investigate several module properties from an abstract point of view with an emphasis on properties relevant for AD. We examine their interrelations, their relation with iterated module extraction, their preservation in normalization-based module notions, and the adjustment of the latter to the requirements of AD. As a case study, we apply our results to modules based on Datalog reasoning (DBMs), which comprise a large family of normalization-based module notions that provide logical guarantees of varying strengths and are thus suitable to a wide range of use cases. This makes DBMs ready to be used for AD and thereby opens AD to new applications.

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“…The modularization process is motivated by the problem of dealing with complex and large-scale ontologies by decomposing them into modules. Therefore, many ontology modularization approaches have been proposed, and several prototypes have been developed [7,8,17,43,61,63]. These approaches can be classified into two main categories: ontology module extraction and ontology partitioning.…”

Section: Ontology Modularizationmentioning

confidence: 99%

Ontology Modularization with OAPT

et al. 2020

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Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool (OAPT). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.

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Modular Structures and Atomic Decomposition in Ontologies

Cited by 18 publications

References 0 publications

Signature-Based Abduction for Expressive Description Logics

Signature-Based Abduction for Expressive Description Logics

Properties of Module Notions and Atomic Decomposition

Ontology Modularization with OAPT

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