Partitioning of ontologies driven by a structure-based approach

Amato, Flora; Santo, Aniello De; Moscato, Vincenzo; Persia, Fabio; Picariello, Antonio; Poccia, Silvestro Roberto

doi:10.1109/icosc.2015.7050827

Cited by 15 publications

(12 citation statements)

References 9 publications

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“…• a semantic relatedness [36,37] based on a set of available ontologies, taxonomies and vocabularies using some high-level features values (i.e. ontological attributes [38]) combined with a "tag-based" similarity;…”

Section: Computing Items Similaritymentioning

confidence: 99%

SOS: A multimedia recommender System for Online Social networks

Amato

Moscato

Picariello

et al. 2019

Future Generation Computer Systems

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Section: Computing Items Similaritymentioning

confidence: 99%

SOS: A multimedia recommender System for Online Social networks

Amato

Moscato

Picariello

et al. 2019

Future Generation Computer Systems

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“…In general, ontology modularization covers the problem of identifying a fragment or a set of fragments of an ontology. The process of identifying a fragment of an ontology given a user input (request) is called ontology module extraction [17,48,49], while the process that partitions the ontology into a set of fragments is called ontology partitioning [2,6,45]. In this work, we distinguish between an ontology partition and ontology module.…”

Section: Definitionsmentioning

confidence: 99%

“…Therefore, each module can be considered as an ontology by itself. To achieve this, each ontology module should be self-contained, consistent, and topic-centric [6,12,58].…”

Section: Definitionsmentioning

confidence: 99%

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Ontology Modularization with OAPT

et al. 2020

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Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontology, only. Therefore, ontology modularization, i.e., splitting up an ontology into smaller parts that can be independently used, becomes a necessity. In this paper, we introduce a new approach to partition ontology based on the seeding-based scheme, which is developed and implemented through the Ontology Analysis and Partitioning Tool (OAPT). This tool proceeds according to the following methodology: first, before a candidate ontology is partitioned, OAPT optionally analyzes the input ontology to determine, if this ontology is worth considering using a predefined set of criteria that quantify the semantic and structural richness of the ontology. After that, we apply the seeding-based partitioning algorithm to modularize it into a set of modules. To decide upon a suitable number of modules that will be generated by partitioning the ontology, we provide the user a recommendation based on an information theoretic model selection method. We demonstrate the effectiveness of the OAPT tool and validate the performance of the partitioning approach by conducting an extensive set of experiments. The results prove the quality and the efficiency of the proposed tool.

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“…For example, the heterogeneity issue, due to the existence of heterogeneous information sources, has required sophisticated solutions to associate metadata to the collected data in order to correctly interpret the results. Semantically enriched metadata -formalized through ontologies using the semantic web languages -inside increasingly large collection of heterogeneous data have arisen the need for efficiently dealing with large ontologies, spawning new research fields, such as, knowledge representation and retrieval (Albanese et al, 2005;Rinaldi, 2008;Rinaldi, 2014), ontology matching and integration (Euzenat et al, 2007), partitioning (Amato et al, 2015b;Amato et al, 2015a), reuse (Modoni et al, 2015), versioning and maintenance (Flouris et al, 2006). A description of the existing solutions that deal with the other technological challenges of Big Data is out of the scope of this paper.…”

Section: A Framework For a Qualitative Evaluation Of Big Data Solutionsmentioning

confidence: 99%

Big Data: A Survey - The New Paradigms, Methodologies and Tools

Caldarola

Rinaldi

2015

Proceedings of 4th International Conference on Data Management Technologies and Applications

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For several years we are living in the era of information. Since any human activity is carried out by means of information technologies and tends to be digitized, it produces a humongous stack of data that becomes more and more attractive to different stakeholders such as data scientists, entrepreneurs or just privates. All of them are interested in the possibility to gain a deep understanding about people and things, by accurately and wisely analyzing the gold mine of data they produce. The reason for such interest derives from the competitive advantage and the increase in revenues expected from this deep understanding. In order to help analysts in revealing the insights hidden behind data, new paradigms, methodologies and tools have emerged in the last years. There has been a great explosion of technological solutions that arises the need for a review of the current state of the art in the Big Data technologies scenario. Thus, after a characterization of the new paradigm under study, this work aims at surveying the most spread technologies under the Big Data umbrella, throughout a qualitative analysis of their characterizing features. 362 Caldarola E. and Rinaldi A..

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Partitioning of ontologies driven by a structure-based approach

Cited by 15 publications

References 9 publications

SOS: A multimedia recommender System for Online Social networks

SOS: A multimedia recommender System for Online Social networks

Ontology Modularization with OAPT

Big Data: A Survey - The New Paradigms, Methodologies and Tools

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