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Justification Patterns for OWL DL Ontologies
For debugging OWL-DL ontologies, natural language explanations of inconsistencies and undesirable entailments are of great help. From such explanations, ontology developers can learn why an ontology gives rise to specific entailments. Unfortunately, commonly used tableaux-based reasoning services do not provide a basis for such explanations, since they rely on a refutation proof strategy and normalising transformations that are difficult for human ontology editors to understand. For this reason, we investigate the use of automatically generated justifications for entailments (i.e., minimal sets of axioms from the ontology that cause entailments to hold). We show that such justifications fall into a manageable number of patterns, which can be used as a basis for generating natural language explanations by associating each justification pattern with a rhetorical pattern in natural language
Syntactic vs. Semantic Locality: How Good Is a Cheap Approximation?
Extracting a subset of a given OWL ontology that captures all the ontology's
knowledge about a specified set of terms is a well-understood task. This task
can be based, for instance, on locality-based modules (LBMs). These come in two
flavours, syntactic and semantic, and a syntactic LBM is known to contain the
corresponding semantic LBM. For syntactic LBMs, polynomial extraction
algorithms are known, implemented in the OWL API, and being used. In contrast,
extracting semantic LBMs involves reasoning, which is intractable for OWL 2 DL,
and these algorithms had not been implemented yet for expressive ontology
languages. We present the first implementation of semantic LBMs and report on
experiments that compare them with syntactic LBMs extracted from real-life
ontologies. Our study reveals whether semantic LBMs are worth the additional
extraction effort, compared with syntactic LBMs
Expressing OWL axioms by English sentences: dubious in theory, feasible in practice
With OWL (Web Ontology Language) established as a standard for encoding ontologies on the Semantic Web, interest has begun to focus on the task of verbalising OWL code in controlled English (or other natural language). Current approaches to this task assume that axioms in OWL can be mapped to sentences in English. We examine three potential problems with this approach (concerning logical sophistication, information structure, and size), and show that although these could in theory lead to insuperable difficulties, in practice they seldom arise, because ontology developers use OWL in ways that favour a transparent mapping. This result is evidenced by an analysis of patterns from a corpus of over 600,000 axioms
in about 200 ontologies
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
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