An Approach to Parallel Class Expression Learning

Tran, An C.; Dietrich, Jens; Guesgen, Hans W.; Marsland, Stephen

doi:10.1007/978-3-642-32689-9_25

Cited by 13 publications

(9 citation statements)

References 13 publications

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“…e DL-Learner application [58], an open source application that implements some CEL algorithms, has been used for this task. More precisely, we have employed the CELOE and the OCEL algorithms implementation in the experiment [59], with 0% of noise percentage and FastIn-stanceChecker (FIC) as the reasoner. e FIC reasoner is a special kind of reasoner, specifically developed for the DL-Learner application that basically makes some violations of the open world assumptions in OWL to improve the efficiency of the reasoning mechanism.…”

Section: Methodsmentioning

confidence: 99%

“…In addition to this operator, it is also necessary to establish a search strategy in S that maximizes the searched area and avoid the analysis of already visited areas. In literature, we can find several proposed search strategies [42,43], which are usually based on graph exploration algorithms. ere are also some of them that are based on computational intelligence, like genetic algorithms [41], where the refinement operator consists of the combination of existing classes in the knowledge base.…”

Section: Definition 4 (Properties Of DL Refinement Operators)mentioning

confidence: 99%

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Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

Salguero

Delatorre

Medina

et al. 2019

Scientific Programming

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The miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms.

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Section: Methodsmentioning

confidence: 99%

Section: Definition 4 (Properties Of DL Refinement Operators)mentioning

confidence: 99%

Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques

Salguero

Delatorre

Medina

et al. 2019

Scientific Programming

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Section: Concept Learning With Dlsmentioning

confidence: 99%

“…These works are necessary to the MCL methods presented here because an integral part of the task involves learning a single concept definition 7 , which is learned using one of the existing methods. Other previous works focus on divide-to-conquer strategies to learn single concepts by combining partial solutions built either from a subset of the positive and negative examples, or from different data sources (Tran et al ., 2017; Gao et al ., 2018; Konys, 2018). In a future work, a study of the underlying bias of this part of the process should be done, because it appears that each method has a slightly different focus, either on the general to specific direction (Fanizzi et al ., 2008; Lehmann & Hitzler, 2010) or in the specific to general (Iannone et al ., 2007).…”

Section: Related Workmentioning

confidence: 99%

Learning multiple concepts in description logic through three perspectives

Melo¹,

Revoredo²,

Paes³

2021

The Knowledge Engineering Review

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An ontology formalises a number of dependent and related concepts in a domain, encapsulated as a terminology. Manually defining such terminologies is a complex, time-consuming and error-prone task. Thus, there is great interest for strategies to learn terminologies automatically. However, most of the existing approaches induce a single concept definition at a time, disregarding dependencies that may exist among the concepts. As a consequence, terminologies that are difficult to interpret may be induced. Thus, systems capable of learning all concepts within a single task, respecting their dependency, are essential for reaching concise and readable ontologies. In this paper, we tackle this issue presenting three terminology learning strategies that aim at finding dependencies among concepts, before, during or after they have been defined. Experimental results show the advantages of regarding the dependencies among the concepts to achieve readable and concise terminologies, compared to a system that learns a single concept at a time. Moreover, the three strategies are compared and analysed towards discussing the strong and weak points of each one.

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“…Generally, such methods tackle the mere classification problem, disregarding the comprehensibility of the resulting model. Conversely, an interesting class of different methods and models that descend from the well-established fields of Inductive Logic Programming (ILP), can be adapted to learn analogous models, based on concept descriptions expressed in DLs and complying their semantics [7,8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%