A structure-based approach for ontology partitioning

2006

Ontologies are at the heart of the semantic web. They define the concepts and relationships that make global interoperability possible. However, as these ontologies grow in size they become more and more difficult to create, use, understand, maintain, transform and classify. We present and evaluate several algorithms for extracting relevant segments out of large description logic ontologies for the purposes of increasing tractability for both humans and computers. The segments are not mere fragments, but stand alone as ontologies in their own right. This technique takes advantage of the detailed semantics captured within an OWL ontology to produce highly relevant segments. The research was evaluated using the GALEN ontology of medical terms and procedures.

Proceedings of the 9th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, 2017

Actually, huge amounts of data are generated at distributed heterogeneous sources, to create and to share information on several domains. Thus, data scientists need to develop appropriate and efficient management strategies to cope with the heterogeneity and the interoperability issues of data sources. In fact, ontology as schema-less graph model and ontology matching as dynamic real-time large-scale data integration enabler are addressed to design and develop advanced management mechanisms. However, given the large-scale context, we adopt ontology partitioning strategies, which split ontologies into a set of disjoint partitions, as a crucial part to reduce the computational complexity and to improve the performance of the ontology matching process. To this end, this paper proposes a novel approach for large-scale ontology partitioning through parallel Markov-based clustering strategy using Spark framework. This latter offers the ability to run in-memory computations to provide faster and expressive partitioning and to increase the speed of the matching system. The results drawn by our strategy over real-world ontologies demonstrate significant performance which makes it suitable to be incorporated in our large-scale ontology matching system.

The text documents are vital resources for any organization. With time the collection of documents keeps on expanding and results into corpora. In document corpora research and in the field of semantic web, it is essential to examine a lot of documents or web pages in order to find relations and synonyms to realize logical differences that exist among them. Conventional data mining and information retrieval techniques are time consuming and may occupy larger space. The situation may further worsen if the text documents are gigantic in nature. In order to deal with this ordeal an alternative technique was developed known as clustering. Clustering techniques develop relations between the documents and then the documents are clustered on the basis of these relations. In this paper the survey of the various methodologies for ontology clustering has been done. The survey gave a clear understanding about the importance of partitioning huge ontologies for making information retrieval efficient.

Studies in Computational Intelligence, 2010

Ontology alignment is an important task for information integration systems that can make different resources, described by various and heterogeneous ontologies, interoperate. However very large ontologies have been built in some domains such as medicine or agronomy and the challenge now lays in scaling up alignment techniques that often perform complex tasks. In this paper, we propose two partitioning methods which have been designed to take the alignment objective into account in the partitioning process as soon as possible. These methods transform the two ontologies to be aligned into two sets of blocks of a limited size. Furthermore, the elements of the two ontologies that might be aligned are grouped in a minimal set of blocks and the comparison is then enacted upon these blocks. Results of experiments performed by the two methods on various pairs of ontologies are promising.

Ingénierie des systèmes d'information/Ingénierie des systèmes d'Information, 2024

Nowadays, ontologies are backbone of Semantic Web. Several domains use ontologies as knowledge models. As their number is constantly increasing, designers are opting to reuse some of those that exist to build new ones. When it is impossible to reuse a part depending on its organization, they import the whole ontology and this makes the manipulation cumbersome especially if the ontology has large concepts. Therefore, segmenting ontologies into partitions, if they are not yet, becomes a constant challenge for designers. This paper presents an approach to modularize ontology using hidden Markov model. Ontology triples are extracted within ontology through SPARQL queries and labelled with integers. The labelled triples constituted a Markov chain where ontology concepts are states and ontology relationships are symbols. This set is used to initialize HMM parameters such as states transition probabilities and symbols observation probabilities matrix and initial states probabilities vector. The transition probabilities matrix of HMM is then used as input of K-Means algorithm to generated modules of ontology concepts. This approach does not handled ontology axioms, which characterize heavy ontologies, and only lightweight ontologies are considered. Experiment on eighteen ontologies, obtained modules satisfied ontology modularization criteria such as independence, non-redundancy, correctness and completeness.

2013

Extracting a subset of a given 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. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of locality-based modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained.

2010

Efficiently extracting a module from a given ontology that captures all the ontology's knowledge about a set of specified terms is a well-understood task. This task can be based, for instance, on locality-based modules. In contrast, extracting all modules of an ontology is computationally difficult because there can be exponentially many. However, it is reasonable to assume that, by revealing the modular structure of an ontology, we can obtain information about its topicality, connectedness, structure, superfluous parts, or agreement between actual and intended modeling. Furthermore, incremental reasoning makes use of a number of, although not all possible, modules of an ontology. We report on experiments to estimate the number of modules of real-life ontologies. We also evaluate the modular structure of ontologies that we succeeded to fully modularise. In that evaluation, we look at the number and sizes of the modules, as well as the relation between module size and number and ...

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informatik.uni-bremen.de

Extracting a subset of a given 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. However, a single module does not allow us to understand neither topicality, connectedness, structure, or superfluous parts of an ontology, nor agreement between actual and intended modeling. The strong logical properties of locality-based modules suggest that the family of all such modules of an ontology can support comprehension of the ontology as a whole. However, extracting that family is not feasible, since the number of localitybased modules of an ontology can be exponential w.r.t. its size. In this paper we report on a new approach that enables us to efficiently extract a polynomial representation of the family of all locality-based modules of an ontology. We also describe the fundamental algorithm to pursue this task, and report on experiments carried out and results obtained.

2018

Ontologies have been widely used in numerous and varied applications, e.g., to support data modeling, information integration, and knowledge management. With the increasing size of ontologies, ontology understanding, which is playing an important role in different tasks, is becoming more difficult. Consequently, ontology summarization, as a way to distill key information from an ontology and generate an abridged version to facilitate a better understanding, is getting growing attention. In this survey paper, we review existing ontology summarization techniques and focus mainly on graph-based methods, which represent an ontology as a graph and apply centrality-based and other measures to identify the most important elements of an ontology as its summary. After analyzing their strengths and weaknesses, we highlight a few potential directions for future research.