Class Expression Learning for Ontology Engineering

Lehmann, Jens; Auer, Sören; Bühmann, Lorenz; Tramp, Sebastian

doi:10.2139/ssrn.3199507

Cited by 29 publications

(49 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The next section will focus on logical analysis of hypothesis generation. In this research, the main references to logic of induction, inductive reasoning and inductive concept learning paradigm are [12,13,23,14,15,16,17]. Also, [1] is the main reference to Descrip-tion Logics and Concept Languages.…”

Section: Human Concept Learningmentioning

confidence: 99%

On Logical Characterisation of Human Concept Learning based on Terminological Systems

Badie

2017

LLP

View full text Add to dashboard Cite

Abstract. The central focus of this article is the epistemological assumption that knowledge could be generated based on human beings' experiences and over their conceptions of the world. Logical characterisation of human inductive learning over their produced conceptions within terminological systems and providing a logical background for theorising over the Human Concept Learning Problem (HCLP) in terminological systems are the main contributions of this research. In order to make a linkage between 'Logic' and 'Cognition', Description Logics (DLs) will be employed to provide a logical description and analysis of actual human inductive reasoning (and learning). This research connects with the topics 'logic & learning', 'cognitive modelling', and 'terminological knowledge representation'.

show abstract

Section: Human Concept Learningmentioning

confidence: 99%

On Logical Characterisation of Human Concept Learning based on Terminological Systems

Badie

2017

LLP

View full text Add to dashboard Cite

show abstract

“…While statistical approaches usually determine the semantics of textual data in terms of word distributions and co-occurrences, information extraction analyses the inherent semantics of language constructs. With the increasing availability of at least partially structured information on the Web, researchers have started to investigate the use of relational learning approaches to extract conceptual knowledge from partially structured and somewhat heterogeneous information [30,51]. Both, linguistic analysis and learning from data also plays a significant role in the creation of semantic mappings between models and data sets.…”

Section: Semantics From Datamentioning

confidence: 99%

Data Semantics on the Web

Stuckenschmidt

2012

J Data Semant

View full text Add to dashboard Cite

Data Semantics is a wide area that continuously faces new challenges arising from the invention of new information formats and novel applications. An area that is particularly challenging with respect to identifying, representing and using data semantics is the Web. This paper attempts to characterize the nature and challenges of Data Semantics on the Web as an interesting research area to be covered by the Journal on Data Semantics.

show abstract

“…However, most algorithms use refinement operators, which have been analysed in [13]. Based on this analysis, several learning algorithms for ALC have been developed in [14] and later extended to more expressive Description Logics and adapted to the ontology engineering use case in [15]. The latter algorithm, called CELOE (Class Expression Learning for Ontology Engineering), was mainly used in our experiments.…”

Section: Fuzzy Learning Algorithm Componentmentioning

confidence: 99%

“…The closer this degree is to 1, the more 'similar' to a perfect locomotive, carriage, triangle or square, respectively. f uzzyShortCar(x) = lef tShoulder (10,15) <rdfs:Datatype rdf:about="fuzzyShortCar"> <fuzzyOwl2 fuzzyType="datatype"> <Datatype type="leftshoulder" a="10" b="15" /> </fuzzyOwl2> </rdfs:Datatype> ShortCar ≡ Car (∃hasCarLength.f uzzyShortCar) <owl:Class rdf:about="ShortCar"> <rdfs:subClassOf rdf:resource="Car"/> <owl:equivalentClass> <owl:Restriction> <owl:onProperty rdf:resource="hasCarLength"/> <owl:someValuesFrom rdf:resource="fuzzyShortCar"/> </owl:Restriction> </owl:equivalentClass> </owl:Class> 3) Fuzzy role assertion: Relations between individuals can also have a truth degree value. This is the case for the load in carriages.…”

Section: A Semantic Fuzzy Trains Problem Designmentioning

confidence: 99%

Towards integrating fuzzy logic capabilities into an ontology-based Inductive Logic Programming framework

Iglesias

Lehmann

2011

2011 11th International Conference on Intelligent Systems Design and Applications

Self Cite

View full text Add to dashboard Cite

Abstract-Ontologies based on Description Logics (DLs) have proved to be useful in formally sharing knowledge across applications. Recently, several tools have extended ontologies with fuzzy logic capabilities in order to apply ontology-based reasoning to vague and imprecise domains. This paper first analyses the state of the art in tools for fuzzy ontologies management and then describes how some of the most significant ones have been integrated in order to extend an ontologybased Inductive Logic Programming (ILP) system with fuzzy logic capabilities. A fuzzy version of a well-known ILP test case has been developed in order to validate the approach. This research represents a first step towards fuzzy inductive reasoning for OWL ontologies.

show abstract

Class Expression Learning for Ontology Engineering

Cited by 29 publications

References 26 publications

On Logical Characterisation of Human Concept Learning based on Terminological Systems

On Logical Characterisation of Human Concept Learning based on Terminological Systems

Data Semantics on the Web

Towards integrating fuzzy logic capabilities into an ontology-based Inductive Logic Programming framework

Contact Info

Product

Resources

About