Conceptual querying through ontologies
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Cited by (20)
The fuzzy ontology reasoner fuzzyDL
2016, Knowledge-Based SystemsCitation Excerpt :Fuzzy set theory and fuzzy logic [99] have proved to be suitable formalisms to handle these types of knowledge. Therefore, fuzzy ontologies emerge as useful in several applications, such as information retrieval [3,23,55,93], recommendation systems [25,51,64,97], image interpretation [32,33], the Semantic Web and the Internet [30,66,71], ambient intelligence [35,54], ontology merging [27,88], matchmaking [68], decision making [79], summarisation [50], robotics [36], and many others [6,37,45,49,52,58,59,65,69,73]. The interested reader is referred to Section 5 for details about some of these applications.
A fuzzy extension of the semantic Building Information Model
2015, Automation in ConstructionCitation Excerpt :Fuzzy ontologies are represented by using fuzzy ontology languages, and can be queried by using fuzzy ontology reasoners, such as DeLorean [14]. Although fuzzy ontologies have been used in different information science research areas – e.g., information retrieval [15], knowledge merge and summarization [16–19], recommender systems [20,21], and decision-making [22] – , to the best of our knowledge they have not been yet applied to solve industrial problems in practice. The overarching objective of this paper is to present the fundamental characteristics and the applications of fuzzy ontologies that can be of interest to the BIM users.
Data summarization ontology-based query processing
2013, Expert Systems with ApplicationsCitation Excerpt :Ontologies provide rich semantic concept and can be a base for contextual query frameworks (Liu & Yu, 2004). An ontology-based query construct defines query operations that operate the resources represented by the domain ontology to retrieve the interested information (Andreasen & Bulskov, 2009; Knappe, Bulskov, & Andreasen, 2007). An ontology-based query system is powered with semantic information formalized in the ontology so that it is capable to integrate all computational resources in information retrieval (Savvas & Bassiliades, 2009).
Aggregation operators for fuzzy ontologies
2013, Applied Soft Computing JournalCitation Excerpt :Fuzzy set theory and fuzzy logic [69] have proved to be suitable formalisms to handle these types of knowledge. Therefore fuzzy ontologies emerge as useful in several applications, such as information retrieval [1,12,53,62], image interpretation [21,22,34], the Semantic Web and the Internet [19,47,51], decision making [13], recommendation [39], summarization [38], ontology merging [17], and many others [23,24,42,46,48,59]. So far, several fuzzy extensions of DLs can be found in the literature (see the survey in [40]).
A recommendation system based on domain ontology and SWRL for anti-diabetic drugs selection
2012, Expert Systems with ApplicationsCitation Excerpt :Ontology is expressed by the aggregation of conceptualizations and a systematic description. Choi et al. classify ontology as three types in ontology: Global Ontology, Local Ontology, and Domain Ontology (Andreasen & Bulskov, 2009; Chen, Liang, & Pan, 2008; Choi, Song, & Han, 2006). In this paper, we will construct an domain ontology.
A semantic-based approach for hepatitis C virus prediction and diagnosis using a fuzzy ontology and a fuzzy Bayesian network
2023, Journal of Intelligent and Fuzzy Systems