Abstraction Refinement for Ontology Materialization

Glimm, Birte; Kazakov, Yevgeny; Liebig, Thorsten; Tran, Trung-Kien; Vialard, Vincent

doi:10.1007/978-3-319-11915-1_12

Cited by 17 publications

(18 citation statements)

References 11 publications

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“…PB and PCB conversions transcribe the distribution of types and properties in the neighborhood of the instances of an RDF graph. In a sense, PB conversion is similar to the notion of individual type proposed by Glimm et al [3]; however, our conversion also re ects the data distribution. PCB conversion extends such description with a description of the actual interconnection between instances of di erent types.…”

Section: From Rdf To Transactionssupporting

confidence: 58%

“…Finally, regarding our proposed conversions, we can nd other approaches proposing similar abstractions of RDF graphs for di erent purposes. Glim et al [3] propose an abstraction of ABoxes to characterize individuals close to our PB and PCB conversions. is abstraction describes the asserted types and relations of individual to create a summary used for fast inferences over low-level data.…”

Section: Related Workmentioning

confidence: 99%

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Measuring structural similarity between RDF graphs

Maillot¹,

Bobed²

2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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In the latest years, there has been a huge e ort to deploy large amounts of data, making it available in the form of RDF data thanks, among others, to the Linked Data initiative. In this context, using shared ontologies has been crucial to gain interoperability, and to be able to integrate and exploit third party datasets. However, using the same ontology does not su ce to successfully query or integrate external data within your own dataset: the actual usage of the vocabulary (e.g., which concepts have instances, which properties are actually populated and how, etc.) is crucial for these tasks. Being able to compare di erent RDF graphs at the actual usage level would indeed help in such situations. Unfortunately, the complexity of graph comparison is an obstacle to the scalability of many approaches. In this article, we present our structural similarity measure, designed to compare structural similarity of low-level data between two di erent RDF graphs according to the pa erns they share. To obtain such pa erns, we leverage a data mining method (KRIMP) which allows to extract the most descriptive pa erns appearing in a transactional database. We adapt this method to the particularities of RDF data, proposing two di erent conversions for an RDF graph. Once we have the descriptive pa erns, we evaluate how much two graphs can compress each other to give a numerical measure depending on the common data structures they share. We have carried out several experiments to show its ability to capture the structural di erences of actual vocabulary usage.

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Section: From Rdf To Transactionssupporting

confidence: 58%

Section: Related Workmentioning

confidence: 99%

Measuring structural similarity between RDF graphs

Maillot¹,

Bobed²

2018

Proceedings of the 33rd Annual ACM Symposium on Applied Computing

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“…We remark that, given an ABox, its abstraction is simply obtained by traversing its facts. It is further shown in [25] how the abstraction of the ABox of an input Horn-ALCHOI KB can be gradually refined, using reasoning steps, to obtain the abstraction of the ABox of the input KB saturation.…”

Section: Other Summarization Methodsmentioning

confidence: 99%

Summarizing semantic graphs: a survey

et al. 2018

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The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its construction. This survey is the first to provide a comprehensive survey of summarization method for semantic RDF graphs. We propose a taxonomy of existing works in this area, including also some closely related works developed prior to the adoption of RDF in the data management community; we present the concepts at the core of each approach and outline their main technical aspects and implementation. We hope the survey will help readers understand this scientifically rich area, and identify the most pertinent summarization method for a variety of usage scenarios.

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“…The abstraction of a KG is computed by identifying modules with similar structures and then representing each group of similar modules by just a single representative one. To characterize the structure of modules, we rely on the local type of individuals [9,11]. Intuitively, a local type of an individual represents a set of classes and the incoming and outgoing properties occurring in the corresponding module for that individual.…”

Section: Graph Abstractionmentioning

confidence: 99%

Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs

Tran¹,

Gad-Elrab

Stepanova³

et al. 2020

Proceedings of the Web Conference 2020

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Knowledge graphs (KGs) are essential resources for many applications including Web search and question answering. As KGs are often automatically constructed, they may contain incorrect facts. Detecting them is a crucial, yet extremely expensive task. Prominent solutions detect and explain inconsistency in KGs with respect to accompanying ontologies that describe the KG domain of interest. Compared to machine learning methods they are more reliable and human-interpretable but scale poorly on large KGs. In this paper, we present a novel approach to dramatically speed up the process of detecting and explaining inconsistency in large KGs by exploiting KG abstractions that capture prominent data patterns. Though much smaller, KG abstractions preserve inconsistency and their explanations. Our experiments with large KGs (e.g., DBpedia and Yago) demonstrate the feasibility of our approach and show that it significantly outperforms the popular baseline.

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Abstraction Refinement for Ontology Materialization

Cited by 17 publications

References 11 publications

Measuring structural similarity between RDF graphs

Measuring structural similarity between RDF graphs

Summarizing semantic graphs: a survey

Fast Computation of Explanations for Inconsistency in Large-Scale Knowledge Graphs

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