Logic-Based Roughification

Nguyen, Linh Anh; Szałas, Andrzej

doi:10.1007/978-3-642-30344-9_19

Cited by 22 publications

(73 citation statements)

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“…This work extends the concept learning method of [22] for richer DLs and provides experimental results. It combines and revises the results reported in our conference papers [30,31] to give a full picture on the current state of bisimulation-based concept learning for information systems in DLs.…”

Section: Introductionmentioning

confidence: 98%

“…Nguyen and Szałas [22] proposed to use bisimulation for concept learning in DLs. They also studied concept approximation using bisimulation and Pawlak's rough set theory [23,24].…”

Section: Introductionmentioning

confidence: 99%

“…They defined terminological roughification to be any technique that uses the largest auto-bisimulation relations as the equivalence relation for defining approximations. The learning algorithm proposed in [22] works for information systems that are either explicitly given as an interpretation or specified by an acyclic knowledge base with closed world assumption. This is similar the traditional setting of machine learning, and a bit different from those of [4,11,15,18,19], where learning algorithms work for (cyclic) knowledge base with open world assumption.…”

Section: Introductionmentioning

confidence: 99%

“…It combines and revises the results reported in our conference papers [30,31] to give a full picture on the current state of bisimulation-based concept learning for information systems in DLs. 1 In comparison with [22], we take attributes as basic elements of the language. Each attribute may be discrete or numeric.…”

Section: Introductionmentioning

confidence: 99%

“…A Boolean attribute is treated as a concept name. One can simulate a discrete attribute by concepts as shown in [22], but this is inconvenient. A more serious problem concerns numeric attributes.…”

Section: Introductionmentioning

confidence: 99%

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Bisimulation-based concept learning for information systems in description logics

Tran

Nguyen

Hoang

2015

Vietnam J Comput Sci

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In description logic-based information systems, objects are described not only by attributes but also by binary relations between them. This work studies concept learning in such information systems. It extends the bisimulationbased concept learning method of Nguyen and Szałas (Rough sets and intelligent systems. Springer, Berlin, pp 517-543, 2013). We take attributes as basic elements of the language. Each attribute may be discrete or numeric. A Boolean attribute is treated as a concept name. This approach is more general and suitable for practical information systems based on description logic than the one of Nguyen and Szałas (Rough sets and intelligent systems. Springer, Berlin, pp 517-543, 2013). As further extensions, we also allow data roles and the concept constructors "functionality" and "unqualified number restrictions". We formulate and prove an important theorem on basic selectors. We also present a domain partitioning method based on information gain that has been used for our implementation of the method. Apart from basic selectors and simple selectors, we introduce a new kind of selectors, called extended selectors. The evaluation

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Section: Introductionmentioning

confidence: 98%

“…Nguyen and Szałas [22] proposed to use bisimulation for concept learning in DLs. They also studied concept approximation using bisimulation and Pawlak's rough set theory [23,24].…”

Section: Introductionmentioning

confidence: 99%