Class expression learning for ontology engineering

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

doi:10.1016/j.websem.2011.01.001

Cited by 102 publications

(42 citation statements)

References 24 publications

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“…The methodology proposed in this paper also shares some characteristics with Class Expression Learning (CEL) techniques. The algorithms for CEL are mainly used in the field of ontology engineering [21]. They can be used to suggest new class descriptions that are relevant to the problem while the ontologies are being developed.…”

Section: Related Workmentioning

confidence: 99%

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Salguero

Espinilla

2018

Computers & Electrical Engineering

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In recent years, many techniques have been proposed for automatic recognition of Activities of Daily Living from smart home sensor data. However, classifiers usually use features created ad hoc. In this work, the use of ontologies is proposed for the fully automatic generation of these features. The process consists of converting the original dataset into an ontology and then combine all the concepts and relations in that ontology to obtain relevant class expressions. The high formalization of ontologies allows us to reduce the search space by discarding many meaningless expressions, such as contradictory or unsatisfiable expressions. The relevant class expressions are then used as features by the classifiers to build the classification model. To validate our proposal, we have used as reference the results obtained by four different classification algorithms that use the most commonly used features.

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Section: Related Workmentioning

confidence: 99%

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Salguero

Espinilla

2018

Computers & Electrical Engineering

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“…Apache Stanbol 4 is an open source modular software stack and reusable set of components (exposed via RESTful interfaces) for semantic content management. One application is to extend traditional content management systems RDB2RDF Mapping Extraction DL-Learner [6,7,9] Machine Learning in OWL Schema Enrichment OntoWiki [1] Generic Data Wiki Authoring, Exploration ORE [8] Knowledge Base Debugging Repair PoolParty [14] SKOS Taxonomy Editor Authoring, Exploration SemMap Spatial data browser Browsing, Exploration Sig.ma EE [16] Data Browser Search, Exploration Sieve [11] Quality Assessment and Fusion Quality, Repair SILK [5] Linking Workbench Interlinking LIMES [13] Linking Workbench Interlinking Virtuoso [4] Hybrid Specification Language (Silk-LSL), developers can specify which types of RDF links should be discovered between data sources as well as which conditions data items must fulfill in order to be interlinked. These link conditions may combine various similarity metrics and can take the graph around a data item into account using an RDF path language.…”

Section: Data Integration Based On Sparql Webid and Vocabulariesmentioning

confidence: 99%

Managing the Life-Cycle of Linked Data with the LOD2 Stack

Auer

Bühmann

Dirschl

et al. 2012

The Semantic Web – ISWC 2012

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The LOD2 Stack is an integrated distribution of aligned tools which support the whole life cycle of Linked Data from extraction, authoring/creation via enrichment, interlinking, fusing to maintenance. The LOD2 Stack comprises new and substantially extended existing tools from the LOD2 project partners and third parties. The stack is designed to be versatile; for all functionality we define clear interfaces, which enable the plugging in of alternative third-party implementations. The architecture of the LOD2 Stack is based on three pillars: (1) Software integration and deployment using the Debian packaging system. (2) Use of a central SPARQL endpoint and standardized vocabularies for knowledge base access and integration between the different tools of the LOD2 Stack. (3) Integration of the LOD2 Stack user interfaces based on REST enabled Web Applications. These three pillars comprise the methodological and technological framework for integrating the very heterogeneous LOD2 Stack components into a consistent framework. In this article we describe these pillars in more detail and give an overview of the individual LOD2 Stack components. The article also includes a description of a real-world usage scenario in the publishing domain.

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“…The current state of the art approaches are able to generate lightweight ontology such as [5] and [6]. Some other works are able to generate more expressive ontologies from existing knowledge base (KB) like in [7] and [8], or from natural language definitions like in [9] or [10]. We also have approaches that are able to generate ontologies from sentences written in a given controlled natural language.…”

Section: Introductionmentioning

confidence: 99%

NALDO: From natural language definitions to OWL expressions

Emani

Silva

Fiés

et al. 2019

Data & Knowledge Engineering

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Domain ontologies are pivotal for Semantic Web applications. The richness of an ontology goes in hand with its usefulness and efficiency. Unfortunately, manually enriching an ontology is very time-consuming. In this paper, we propose to enrich an ontology automatically by obtaining logical expressions of concepts. We present NALDO, a novel approach that provides an OWL DL (Web Ontology Language Description Logics) expression of a concept from two inputs: (1) the natural language definition of the concept and (2) an ontology describing the domain of this concept. NALDO uses as much as possible entities provided by the domain ontology, however it can suggest, when needed, new entities. The expressiveness of expressions provided by NALDO covers value and cardinality restrictions, subsumption and equivalence. We evaluate our approach against the definitions and the corresponding ontologies of the BEAUFORD benchmark. Our results show that NALDO is able to perform the correct identification of formal entities with an F1-measure up to 0.79.

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Class expression learning for ontology engineering

Cited by 102 publications

References 24 publications

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Ontology-based feature generation to improve accuracy of activity recognition in smart environments

Managing the Life-Cycle of Linked Data with the LOD2 Stack

NALDO: From natural language definitions to OWL expressions

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