Efficient retrieval of ontology fragments using an interval labeling scheme

Nebot, Victoria; Berlanga, Rafael

doi:10.1016/j.ins.2009.08.012

Cited by 22 publications

(18 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, we provide an interval's algebra over the node descriptors that allows performing basic DL operations over concepts. A detailed description of the indexing process and interval's algebra can be found in [30].…”

Section: Ontology Indexesmentioning

confidence: 99%

“…We define upper modules in the same way as in [21,23,30], that is, by applying the notion of conservative extension. Thus, an upper module M of O for the signature Sig is a sub-ontology of O such that it preserves all the entailments over the symbols of Sig expressed in a language L, that is, O |= α with Sig(α) ⊆ Sig and α ∈ L iff M Di |= α.…”

Section: Dimension Modulesmentioning

confidence: 99%

“…Thus, the language of entailments corresponds to the typical reasoners output. This kind of upper modules can be extracted very efficiently over very large ontologies [30].…”

Section: Dimension Modulesmentioning

confidence: 99%

“…Similarly to our previous work about fragment extraction [30], we propose to build tailored ontology fragments that both preserve as much as possible the original semantics of the symbols in Sig(D i ) and present a good OLAPlike structure. For the first goal, we will use the upper modules M Di as the baseline for the hierarchy extraction.…”

Section: Selecting Good Nodesmentioning

confidence: 99%

“…Alternatively, we propose a second approach in Algorithm 3, the local approach, which selects the best ancestors of each concept leaf of the taxonomy. In both approaches, the final hierarchy is obtained by extracting the spanning tree that maximizes the number of ancestors of each node from the resulting reduced taxonomy [30]. One advantage of the local approach is that we can further select the number of levels up to each signature concept, defining so the hierarchical categories for the dimensions.…”

Section: Generating Hierarchiesmentioning

confidence: 99%

See 4 more Smart Citations

Building data warehouses with semantic web data

NebotVictoria

BerlangaRafael

2012

Decision Support Systems

Self Cite

View full text Add to dashboard Cite

The Semantic Web (SW) deployment is now a realization and the amount of semantic annotations is ever increasing thanks to several initiatives that promote a change in the current Web towards the Web of Data, where the semantics of data become explicit through data representation formats and standards such as RDF/(S) and OWL. However, such initiatives have not yet been accompanied by efficient intelligent applications that can exploit the implicit semantics and thus, provide more insightful analysis. In this paper, we provide the means for efficiently analyzing and exploring large amounts of semantic data by combining the inference power from the annotation semantics with the analysis capabilities provided by OLAP-style aggregations, navigation, and reporting. We formally present how semantic data should be organized in a well-defined conceptual MD schema, so that sophisticated queries can be expressed and evaluated. Our proposal has been evaluated over a real biomedical scenario, which demonstrates the scalability and applicability of the proposed approach.

show abstract

Section: Ontology Indexesmentioning

confidence: 99%

Section: Dimension Modulesmentioning

confidence: 99%

“…Thus, the language of entailments corresponds to the typical reasoners output. This kind of upper modules can be extracted very efficiently over very large ontologies [30].…”

Section: Dimension Modulesmentioning

confidence: 99%

Section: Selecting Good Nodesmentioning

confidence: 99%

Section: Generating Hierarchiesmentioning

confidence: 99%

See 3 more Smart Citations

Building data warehouses with semantic web data

NebotVictoria

BerlangaRafael

2012

Decision Support Systems

Self Cite

View full text Add to dashboard Cite

show abstract

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

Solimando

Jiménez-Ruiz

Guerrini

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In order to enable interoperability between ontology-based systems, ontology matching techniques have been proposed. However, when the generated mappings suffer from logical flaws, their usefulness may be diminished. In this paper we present an approximate method to detect and correct violations to the so-called conservativity principle where novel subsumption entailments between named concepts in one of the input ontologies are considered as unwanted. We show that this is indeed the case in our application domain based on the EU Optique project. Additionally, our extensive evaluation conducted with both the Optique use case and the data sets from the Ontology Alignment Evaluation Initiative (OAEI) suggests that our method is both useful and feasible in practice.

show abstract

Mining Association Rules from Semantic Web Data

Nebot

Berlanga

2010

Trends in Applied Intelligent Systems

Self Cite

View full text Add to dashboard Cite

Abstract. The amount of ontologies and semantic annotations available on the Web is constantly increasing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/S and OWL. We take advantage of the schema-level (i.e. Tbox ) knowledge encoded in the ontology to derive just the appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of a query pattern. Initial experiments performed on real world semantic data enjoy promising results and show the usefulness of the approach.

show abstract

Efficient retrieval of ontology fragments using an interval labeling scheme

Cited by 22 publications

References 20 publications

Building data warehouses with semantic web data

Building data warehouses with semantic web data

Detecting and Correcting Conservativity Principle Violations in Ontology-to-Ontology Mappings

Mining Association Rules from Semantic Web Data

Contact Info

Product

Resources

About