Towards Practical ABox Abduction in Large Description Logic Ontologies

Pan, Jeff Z.; Du, Jianfeng; Qi, Guilin; Shen, Yi-Dong

doi:10.4018/jswis.2012040101

Cited by 29 publications

(56 citation statements)

References 35 publications

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“…It is based on a reduction to the axiom pinpointing problem which is then solved with automata-based methods. Similar to the approach presented by Du et al [34] the abductive problem also contains a set of abducibles which in this case represent a set of axioms that can appear in solutions.…”

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

“…The approach does not guarantee completeness since the translation to a Datalog program is approximate and in some cases a solution would not be found. The authors extended their work in [35] by allowing arbitrary concepts and roles as abducibles, such as complex concepts, negated roles, etc. In [36] the authors further extend their previous work and deal with an ABox abduction problem where new individuals are allowed in solutions.…”

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

“…A practical approach for ABox abduction, based on abductive logic programming, was proposed in [34]. This approach considers solutions over a finite set of concepts and roles called abducibles which is in contrast to the work in [64] where solutions can be formed with arbitrary concepts.…”

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

“…To deal with this, the authors reduce the number of explanations which need to be computed by considering only representative solutions, that is solutions which are not subsumed by other minimal solutions. The approach was compared to the approach in [34] in an experi-mental evaluation which has shown that computing representative solutions is more efficient than computing minimal solutions.…”

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

See 3 more Smart Citations

Completing the Is-a Structure in Description Logics Ontologies

Dragisic¹

2014

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The World Wide Web contains large amounts of data and in most cases this data is without any explicit structure. The lack of structure makes it difficult for automated agents to understand and use such data. A step towards a more structured World Wide Web is the idea of the Semantic Web which aims at introducing semantics to data on the World Wide Web. One of the key technologies in this endeavour are ontologies which provide means for modeling a domain of interest.Developing and maintaining ontologies is not an easy task and it is often the case that defects are introduced into ontologies. This can be a problem for semanticallyenabled applications such as ontology-based querying. Defects in ontologies directly influence the quality of the results of such applications as correct results can be missed and wrong results can be returned.This thesis considers one type of defects in ontologies, namely the problem of completing the is-a structure in ontologies represented in description logics. We focus on two variants of description logics, the EL family and ALC, which are often used in practice.The contributions of this thesis are as follows. First, we formalize the problem of completing the is-a structure as a generalized TBox abduction problem (GTAP) which is a new type of abduction problem in description logics. Next, we provide algorithms for solving GTAP in the EL family and ALC description logics. Finally, we describe two implemented systems based on the introduced algorithms. The systems were evaluated in two experiments which have shown the usefulness of our approach. For example, in one experiment using ontologies from the Ontology Alignment Evaluation Initiative 58 and 94 detected missing is-a relations were repaired by adding 54 and 101 is-a relations, respectively, introducing new knowledge to the ontologies.

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Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

Section: Abductive Reasoning In Description Logicsmentioning

confidence: 99%

See 2 more Smart Citations

Completing the Is-a Structure in Description Logics Ontologies

Dragisic¹

2014

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“…Since the set of solutions can contain some inconsistent and non-minimal solutions additional checks are required. A practical approach for ABox abduction, based on abductive logic programming, was proposed in [7]. In order to use existing abductive logic programming systems it is necessary to do a transformation to a plain Datalog program.…”

Section: Related Workmentioning

confidence: 99%

Get My Pizza Right: Repairing Missing is-a Relations in ${\cal ALC}$ Ontologies

Lambrix

Dragisic

Іванова

2013

Semantic Technology

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Abstract. With the increased use of ontologies in semantically-enabled applications, the issue of debugging defects in ontologies has become increasingly important. These defects can lead to wrong or incomplete results for the applications. Debugging consists of the phases of detection and repairing. In this paper we focus on the repairing phase of a particular kind of defects, i.e. the missing relations in the is-a hierarchy. Previous work has dealt with the case of taxonomies. In this work we extend the scope to deal with ALC ontologies that can be represented using acyclic terminologies. We present algorithms and discuss a system.

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Extending Forgetting-Based Abduction Using Nominals

Del-Pinto

Schmidt

2019

Frontiers of Combining Systems

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Abductive reasoning produces hypotheses to explain new observations with respect to some background knowledge. This paper focuses on ABox abduction in ontologies, where knowledge is expressed in description logics and both the observations and hypotheses are ground statements. The input is expressed in the description logic ALC and the observation can contain any set of ALC concept or role assertions. The proposed approach uses forgetting to produce hypotheses in the form of a disjunctive set of axioms, where each disjunct is an independent explanation for the observation and the overall hypothesis is semantically minimal, i.e., makes the least assumptions required. Previous work on forgetting-based abduction is combined with the semantic forgetting method of the system FAME. The hypotheses produced are expressed in an extension of ALC which uses nominals, role inverses and fixpoints: ALCOIµ(∇). This combination overcomes the inability of the existing forgetting-based approach to allow role assertions in observations and hypotheses, and enables the computation of other previously unreachable hypotheses. An experimental evaluation is performed using a prototype implementation of the method on a corpus of real world ontologies.

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Towards Practical ABox Abduction in Large Description Logic Ontologies

Cited by 29 publications

References 35 publications

Completing the Is-a Structure in Description Logics Ontologies

Completing the Is-a Structure in Description Logics Ontologies

Get My Pizza Right: Repairing Missing is-a Relations in ${\cal ALC}$ Ontologies

Extending Forgetting-Based Abduction Using Nominals

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