A Framework for Tackling Myopia in Concept Learning on the Web of Data

Rizzo, Giuseppe; Fanizzi, Nicola; d’Amato, Claudia; Esposito, Floriana

doi:10.1007/978-3-030-03667-6_22

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…Both of them may suffer of ending up in sub-optimal solutions. In order to overcome such issue, DL-FOCL [27], PARCEL [28] and SPACEL [29] have been proposed. DL-FOCL is an optimized version of DL-FOIL, implementing a base greedy covering learner.…”

Section: Concept Learning For Ontology Enrichmentmentioning

confidence: 99%

Machine Learning for the Semantic Web: Lessons learnt and next research directions

d’Amato

2020

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Machine Learning methods have been introduced in the Semantic Web for solving problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level). Whilst initially mainly focussing on symbol-based solutions, recently numeric-based approaches have received major attention, motivated by the need to scale on the very large Web of Data. In this paper, the most representative proposals, belonging to the aforementioned categories are surveyed, jointly with the analysis of their main peculiarities and drawbacks. Afterwards the main envisioned research directions for further developing Machine Learning solutions for the Semantic Web are presented.

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Section: Concept Learning For Ontology Enrichmentmentioning

confidence: 99%

Machine Learning for the Semantic Web: Lessons learnt and next research directions

d’Amato

2020

Self Cite

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“…DL-FOIL, which is based on a mixture of upward and downward refinement of class expressions, employs a similar approach [46]. Some extension of the latter have been proposed for dealing with fuzzy extensions of DL [47] or to avoid suboptimal solutions due to the kind of refinement operators being used [48]. Another approach to concept learning is based on bisimulation [49,50].…”

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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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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Mining the Semantic Web with Machine Learning: Main Issues that Need to Be Known

d’Amato

2022

Reasoning Web. Declarative Artificial Intelligence

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A Framework for Tackling Myopia in Concept Learning on the Web of Data

Cited by 7 publications

References 15 publications

Machine Learning for the Semantic Web: Lessons learnt and next research directions

Machine Learning for the Semantic Web: Lessons learnt and next research directions

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

Mining the Semantic Web with Machine Learning: Main Issues that Need to Be Known

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