Tractable Extensions of the Description Logic ${\mathcal{EL}}$ with Numerical Datatypes

Magka, Despoina; Kazakov, Yevgeny; Horrocks, Ian

doi:10.1007/s10817-011-9235-0

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…We will also seek to extend our prototype to accommodate subsumption between chemical classes so as to generate a complete multi-level chemical hierarchy using ideas from our recent work [49,50]. We could extend our formalism with numerical value restrictions [51] in order to express e.g. classes depending on molecular weight.…”

Section: Resultsmentioning

confidence: 99%

A rule-based ontological framework for the classification of molecules

2014

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BackgroundA variety of key activities within life sciences research involves integrating and intelligently managing large amounts of biochemical information. Semantic technologies provide an intuitive way to organise and sift through these rapidly growing datasets via the design and maintenance of ontology-supported knowledge bases. To this end, OWL—a W3C standard declarative language— has been extensively used in the deployment of biochemical ontologies that can be conveniently organised using the classification facilities of OWL-based tools. One of the most established ontologies for the chemical domain is ChEBI, an open-access dictionary of molecular entities that supplies high quality annotation and taxonomical information for biologically relevant compounds. However, ChEBI is being manually expanded which hinders its potential to grow due to the limited availability of human resources.ResultsIn this work, we describe a prototype that performs automatic classification of chemical compounds. The software we present implements a sound and complete reasoning procedure of a formalism that extends datalog and builds upon an off-the-shelf deductive database system. We capture a wide range of chemical classes that are not expressible with OWL-based formalisms such as cyclic molecules, saturated molecules and alkanes. Furthermore, we describe a surface ‘less-logician-like’ syntax that allows application experts to create ontological descriptions of complex biochemical objects without prior knowledge of logic. In terms of performance, a noticeable improvement is observed in comparison with previous approaches. Our evaluation has discovered subsumptions that are missing from the manually curated ChEBI ontology as well as discrepancies with respect to existing subclass relations. We illustrate thus the potential of an ontology language suitable for the life sciences domain that exhibits a favourable balance between expressive power and practical feasibility.ConclusionsOur proposed methodology can form the basis of an ontology-mediated application to assist biocurators in the production of complete and error-free taxonomies. Moreover, such a tool could contribute to a more rapid development of the ChEBI ontology and to the efforts of the ChEBI team to make annotated chemical datasets available to the public. From a modelling point of view, our approach could stimulate the adoption of a different and expressive reasoning paradigm based on rules for which state-of-the-art and highly optimised reasoners are available; it could thus pave the way for the representation of a broader spectrum of life sciences and biomedical knowledge.

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Section: Resultsmentioning

confidence: 99%

A rule-based ontological framework for the classification of molecules

2014

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“…There are only 19 datatypes that were chosen such that their intersections are either infinite or empty [32]. Magka et al proved that OWL 2 EL class expressions can be extended with inequalities over numeric domains [29]. For a SubClassOf: axioms with a subclass expression in OWL 2 EL, a superclass expression can be extended with ≥ and ≤ relations over real, rational, and integer numbers, and with ≥ relation over natural numbers.…”

Section: Owl 2 Elmentioning

confidence: 99%

Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets

Potoniec

Jakubowski

Ławrynowicz

2017

Journal of Web Semantics

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In this study, we present Swift Linked Data Miner, an interruptible algorithm that can directly mine an online Linked Data source (e.g., a SPARQL endpoint) for OWL 2 EL class expressions to extend an ontology with new SubClassOf: axioms. The algorithm works by downloading only a small part of the Linked Data source at a time, building a smart index in the memory and swiftly iterating over the index to mine axioms. We propose a transformation function from mined axioms to RDF Data Shapes. We show, by means of a crowdsourcing experiment, that most of the axioms mined by Swift Linked Data Miner are correct and can be added to an ontology. We provide a ready to use Protégé plugin implementing the algorithm, to support ontology engineers in their daily modeling work.

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“…Thus, an object will be expensive if its cost is greater than the threshold d. The relation expensive resembles a datatype property in OWL, associating an individual with a data value. In DL, we could introduce a concept Expensive and constrain its interpretation with a datatype restriction of the following kind [27], where X is a variable whose role we will discuss shortly:…”

Section: Gradable Adjectives and Measuresmentioning

confidence: 99%

Learning Vague Concepts for the Semantic Web

Pareti,

Klein

2014

Preprint

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Ontologies can be a powerful tool for structuring knowledge, and they are currently the subject of extensive research. Updating the contents of an ontology or improving its interoperability with other ontologies is an important but difficult process. In this paper, we focus on the presence of vague concepts, which are pervasive in natural language, within the framework of formal ontologies. We will adopt a framework in which vagueness is captured via numerical restrictions that can be automatically adjusted. Since updating vague concepts, either through ontology alignment or ontology evolution, can lead to inconsistent sets of axioms, we define and implement a method to detecting and repairing such inconsistencies in a local fashion.

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Tractable Extensions of the Description Logic ${\mathcal{EL}}$ with Numerical Datatypes

Cited by 10 publications

References 5 publications

A rule-based ontological framework for the classification of molecules

A rule-based ontological framework for the classification of molecules

Swift Linked Data Miner: Mining OWL 2 EL class expressions directly from online RDF datasets

Learning Vague Concepts for the Semantic Web

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