Rough Ontology: Extension of Ontologies by Rough Sets

Ishizu, Syohei; Gehrmann, Andreas; Nagai, Yoshimitsu; Inukai, Yusei

doi:10.1007/978-3-540-73345-4_52

Cited by 14 publications

(9 citation statements)

References 2 publications

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“…This is a major difference when compared with the fuzzy set theory which requires probability assignments and membership values respectively. Ishizu et al [6] formulated a concept of rough ontology, which is an extended concept of the rough set, and define extended concepts of rough ontology. In this way, rough set theory using the concept of ontology enables us to use flexible information system in ontological description.…”

Section: Related Workmentioning

confidence: 99%

“…The Rough set theory incorporates with the existing ontology concepts and can provide possibilities to quantify a degree of accuracy of knowledge,. It can also a concept of rough ontology, which is an extended concept of the Rough set [6]. The unique advantage offered by the theory is that it enables us to use flexible information system in the form of rough ontology.…”

Section: Introductionmentioning

confidence: 99%

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A Semantic Information Retrieval Approach Based on Rough Ontology

Hu¹,

Lu²,

Guan³

2014

TOCSJ

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With the emergence of the semantic web, ontology has attracted a great deal of attention in field of information retrieval. But the conceptual formalism supported by typical ontology is not sufficient for handling incomplete information that is confronted in the real world knowledge. To tackle this problem, a semantic information retrieval approach based on a rough ontology is proposed. Rough ontology in this paper is in the form of an ontology information system. Given a keyword based query, our approach infers the individuals and properties correlated to the query through a procedure of association searches in the rough ontology, and takes properties as equivalence relations to construct an approximation space of rough ontology. Afterward, an algorithm of computing similarity in rough ontology is presented, and approximation space is employed to compute similarity for ranking documents in semantic document indexing space. The proposed approach has been compared with two other information retrieval techniques, and the experiments conducted on CNKI collections, support the better efficacy which results from our approach.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Semantic Information Retrieval Approach Based on Rough Ontology

Hu¹,

Lu²,

Guan³

2014

TOCSJ

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“…In the works of Pancerz (2012b;2013b), ontologies were incorporated into information (decision) systems, i.e., attribute values were considered in the ontological (semantic) spaces. Similar approaches have been considered in the literature, e.g., DAG-decision systems (Midelfart and Komorowski, 2002), the dominance-based rough set approach (DRSA) (Greco et al, 2001), rough ontology (Ishizu et al, 2007), etc. In our approach, we replace, in a classic definition…”

Section: Introductionmentioning

confidence: 99%

Ant-based extraction of rules in simple decision systems over ontological graphs

Pancerz¹,

Lewicki

Tadeusiewicz

2015

International Journal of Applied Mathematics and Computer Science

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In the paper, the problem of extraction of complex decision rules in simple decision systems over ontological graphs is considered. The extracted rules are consistent with the dominance principle similar to that applied in the dominancebased rough set approach (DRSA). In our study, we propose to use a heuristic algorithm, utilizing the ant-based clustering approach, searching the semantic spaces of concepts presented by means of ontological graphs. Concepts included in the semantic spaces are values of attributes describing objects in simple decision systems.

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“…the tuple notation in [6], use both R and D for the 'attributes' of the concepts (cf. R only in [5,8]), include the properties of the indistinguishability relation (cf. their omission in [7] or a similarity relation [3]), and adhere to proper declaration of C, C, and C in that they all have the same collection of properties from R ∪ D (cf.…”

Section: Prospects For Rough Owl Ontologiesmentioning

confidence: 99%

“…To support such usage of ontologies, one needs a language with which one can represent, at least, rough concepts as the intensional representation of the corresponding rough set and a way to persistently relate the instance data to the rough concepts. Various extensions of Description Logics (DL) and OWL languages have been proposed for rough ontologies [3][4][5][6][7][8], which diverge in commitment as to which aspects of rough sets are included in the ontology language and they concern theory instead of demonstrating successful use of the rough ontology in ontology engineering. As it turns out, there is no perfect DL language, reasoner, and ontology development tool that does it all with respect to the semantics of rough sets, nor will there be if one adheres to the hard requirement of staying within the decidable fragment of first order logic, let alone within the tractable zone.…”

Section: Introductionmentioning

confidence: 99%

Ontology Engineering with Rough Concepts and Instances

Keet

2010

Lecture Notes in Computer Science

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Abstract. A scenario in ontology development and its use is hypothesis testing, such as finding new subconcepts based on the data linked to the ontology. During such experimentation, knowledge tends to be vague and the associated data is often incomplete, which OWL ontologies normally do not consider explicitly. To fill this gap, we use OWL 2 and their application infrastructures together with rough sets. Although OWL 2 QL is insufficient to represent most of rough set's semantics, the mapping layer of its Ontology-Based Data Access framework that links concepts in the ontology to queries over the data source suffice to ascertain if a concept is rough, which subsequently can be modelled more precisely in an OWL 2 DL ontology. We summarise the trade-offs and validate it with the HGT ontology and its 17GB genomics database and with sepsis, which demonstrates it is an encouraging step toward comprehensive and usable rough ontologies.

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Rough Ontology: Extension of Ontologies by Rough Sets

Cited by 14 publications

References 2 publications

A Semantic Information Retrieval Approach Based on Rough Ontology

A Semantic Information Retrieval Approach Based on Rough Ontology

Ant-based extraction of rules in simple decision systems over ontological graphs

Ontology Engineering with Rough Concepts and Instances

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