On updates of hybrid knowledge bases composed of ontologies and rules

Slota, Martin; Leite, João; Swift, Terrance

doi:10.1016/j.artint.2015.07.008

Cited by 19 publications

(22 citation statements)

References 42 publications

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“…Future work includes investigating syntactic operators defined over traditional logic programs for the semantics in [13], that correspond to weaker versions of (SP), as well as investigating how forgetting relates to other operations such as updating , and how it translates to hybrid knowledge representation formalisms [17,22,3].…”

Section: Discussionmentioning

confidence: 99%

Forgetting in Answer Set Programming with Anonymous Cycles

Berthold

Gonçalves

Knorr

et al. 2019

Progress in Artificial Intelligence

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It is now widely accepted that the operation of forgetting in the context of Answer Set Programming [10,18] is best characterized by the so-called strong persistence, a property that requires that all existing relations between the atoms not to be forgotten be preserved. However, it has been shown that strong persistence cannot always be satisfied. What happens if we must nevertheless forget? One possibility that has been explored before is to consider weaker versions of strong persistence, although not without a cost: some relations between the atoms not to be forgotten are broken in the process. A different alternative is to enhance the logical language so that all such relations can be maintained after the forgetting operation. In this paper, we borrow from the recently introduced notion of fork [1]-a conservative extension of Equilibrium Logic and its monotonic basis, the logic of Here-and-There-which has been shown to be sufficient to overcome the problems related to satisfying strong persistence. We map this notion into the language of logic programs, enhancing it with so-called anonymous cycles, and we introduce a concrete syntactical forgetting operator over this enhanced language that we show to always obey strong persistence.

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Section: Discussionmentioning

confidence: 99%

Forgetting in Answer Set Programming with Anonymous Cycles

Berthold

Gonçalves

Knorr

et al. 2019

Progress in Artificial Intelligence

Self Cite

View full text Add to dashboard Cite

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“…Closest related work concerns itself with belief update, a relative of belief revision, in hybrid MKNF knowledge bases [21], which combine first-order theories and logic programs in a different semantic composition to the one considered here. That approach takes the combination of an initial HKB and an updating HKB and splits it into hierarchical levels, similar to the process of logic program stratification, such that updating information is given priority.…”

Section: Related Workmentioning

confidence: 99%

Three Methods for Revising Hybrid Knowledge Bases

Binnewies

Zhuang

Wang

2017

Rules and Reasoning

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Contemporary approaches for the Semantic Web include hybrid knowledge bases that combine ontologies with rule-based languages. Despite a number of existing combination approaches, little attention has been given to change mechanisms for hybrid knowledge bases that can appropriately handle the dynamics of information on the Web. We present here three methods for revising hybrid knowledge bases in light of new information. We show by means of representation theorems that two of them fit properly into the classic belief change framework and that each of the two generalises the third method.

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“…Its strength lies in its ability to deal with imperfect data and to classify. Knowledge hidden in information systems may be unraveled and expressed in the form of decision rules [8], [29]- [34]. The extracted rules can be used for making predictions in different domains.…”

Section: Introductionmentioning

confidence: 99%

Knowledge Acquisition Approach Based on Incremental Objects From Data With Missing Values

2019

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Knowledge acquisition is the process of extracting useful knowledge from data sets to analyze data in areas of data mining and knowledge discovery. Most current knowledge acquisition work mainly focuses on static data. However, due to the dynamic characteristics of data, the objects grow at an unprecedented rate in real-world data sets. The incremental objects with a dynamic environment significantly affect knowledge updating. To maintain the effectiveness of knowledge from the dynamic data, it is necessary to update the knowledge timely. So far, there are relatively few studies on knowledge acquisition for the data with missing feature values, i.e., incomplete data. To handle with this issue, an incremental updating manner of the accuracy matrix and coverage matrix are first proposed on the basis of the computations of the tolerance classes in incomplete data, which plays an important role in the knowledge acquisition process. Then, an incremental knowledge acquisition algorithm is proposed when some new objects added to the data with missing values. Finally, some numerical experiments are conducted to evaluate the efficiency of the proposed algorithm.

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On updates of hybrid knowledge bases composed of ontologies and rules

Cited by 19 publications

References 42 publications

Forgetting in Answer Set Programming with Anonymous Cycles

Forgetting in Answer Set Programming with Anonymous Cycles

Three Methods for Revising Hybrid Knowledge Bases

Knowledge Acquisition Approach Based on Incremental Objects From Data With Missing Values

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