Ontology Matching Tools Research Articles

Interactive ontology matching based on partial reference alignment

The technique that enables the user and the automatic ontology matching tool to cooperate with each other to generate high-quality alignments in a reasonable amount of time is referred to as the interactive ontology matching. Interactive ontology matching poses a new challenge in a way of how to efficiently leverage user validation to improve the ontology alignment. To address this challenge, this paper presents an innovative interactive ontology matching technique based on Partial Reference Alignment (PRA) to better balance between the large workload posed on users and the demand of improving the quality of ontology alignment. In particular, a PRA-based Interactive Compact Hybrid Evolutionary Algorithm (ICHEA) is proposed to reduce user workload, by adaptively determining the timing of involving users, showing them the most problematic mappings, and helping them to deal with multiple conflicting mappings simultaneously. Meanwhile, it increases the value of user involvement by propagating the confidences of validated mappings, as well as reducing the negative effects brought by the erroneous user validations. The well-known OAEI 2016's benchmark track and interactive track are utilized to test the performance of this approach. The experimental results on benchmark track show that both the f-measure and the f-measure per second of this approach outperform those of the OAEI participants and three state-of-the-art Evolutionary Algorithm (EA) based ontology matching techniques. In addition, the experimental results of three interactive testing cases further show that ICHEA can efficiently determine high-quality ontology alignments under different cases of user error rates, and the performance of the approach is generally better than that of state-of-the-art interactive ontology matching systems.

Large-Scale Ontology Matching

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated the development of large-sized independent ontologies within the same or different domains with some overlapping information among them. To integrate such large ontologies, automatic matchers become an inevitable solution. However, the process of matching large ontologies has high space and time complexities. Therefore, for a tool to efficiently and accurately match these large ontologies within the limited computing resources, it must have techniques that can significantly reduce the high space and time complexities associated with the ontology matching process. This article provides a review of the state-of-the-art techniques being applied by ontology matching tools to achieve scalability and produce high-quality mappings when matching large ontologies. In addition, we provide a direct comparison of the techniques to gauge their effectiveness in achieving scalability. A review of the state-of-the-art ontology matching tools that employ each strategy is also provided. We also evaluate the state-of-the-art tools to gauge the progress they have made over the years in improving alignment’s quality and reduction of execution time when matching large ontologies.

A Better Approach to Ontology Integration using Clustering Through Global Similarity Measure

Knowledge representation is a crucial area of work in the intelligent system, especially in query answering system development. Ontology is used to represent shared knowledge of a particular domain for query answering system. Domain-specific ontology can be designed and developed by many groups and researchers, because of which there is heterogeneity in the knowledgebase. Ontology integration or merging is necessary in order to solve this problem of mixed knowledge. Finding similarity between two ontologies is crucial to achieve integration or merging of ontology. In this study, we present a method to generate a cluster of ontologies using global similarity measure of two ontologies. Ontology matching tools are used to find matched classes between two ontologies. Output of ontology matching tool is mapping between two ontologies and is used for generating clusters of ontology. We use Jaccard Similarity Index as a global similarity measure for clustering. Based on this measure, the popular k-means clustering algorithm is used to perform clustering of ontologies. Bins of ontologies are generated from each cluster. From each bin, all ontologies are finally merged into a single ontology, which helps us in reducing search effort in querying knowledge in query processing. The outcome of this research paper to provide better solution for merging ontology. Here, we use agriculture domain ontology corpus from the standard dataset for experimentation.

A statistically-based ontology matching tool

Ontologies have become a popular means of knowledge sharing and reuse. This has motivated development of large independent ontologies within the same or different domains with some overlapping information among them. In order to match such large ontologies, automatic matchers become an inevitable solution. This work explores the use of a predictive statistical model to establish an alignment between two input ontologies. We demonstrate how to integrate ontology partitioning and parallelism in the ontology matching process in order to make the statistical predictive model scalable to large ontology matching tasks. Unlike most ontology matching tools which establish 1:1 cardinality mappings, our statistical model generates one-to-many cardinality mappings.

Overview of YAM++—(not) Yet Another Matcher for ontology alignment task

Several challenges to the field of ontology matching have been outlined in recent research. The selection of the appropriate similarity measures as well as the configuration tuning of their combination are known as fundamental issues the community should deal with. Verifying the semantic coherence of the discovered alignment is also known as a crucial task. As the challenging issues are both in basic matching techniques and in their combination, our approach is aimed to provide improvement at the basic matcher level and also at the level of framework. Matching large scale ontologies is currently one of the most challenging issues in ontology matching field. The main reason is that large ontologies are highly heterogeneous both at terminological and conceptual levels. Furthermore, matching very large ontologies entails exploring a very large searching space to discover correspondences. It may also require a huge amount of main memory to maintain the temporary results at each computational step. These factors strongly impact the effectiveness and efficiency of any ontology matching tool. To overcome these issues, we have developed a disk-based ontology matching approach. The underlying idea of our approach is that the complexity and therefore the cost of the matching algorithms are reduced thanks to the indexing data structures by avoiding exhaustive pair-wise comparisons. Indeed, we extensively used indexing techniques in many places. For example, we defined a bitmap encoding the structural information of an ontology. This indexing structure will be exploited for accelerating similarity propagation. Moreover, our approach uses a disk-based mechanism to store temporary data. This allows to perform any ontology matching task on a simple PC or laptop instead of a powerful server. In this paper, we describe YAM++, an ontology matching tool, aimed at solving these issues. We evaluated the efficiency of YAM++ in various OAEI 2012 and OAEI 2013 tracks. YAM++ was one of the best ontology matching systems in terms of F-measure. Most notably, the current version of YAM++ has passed all scalability and large scale ontology matching tests and obtained high matching quality results.

Alignment Evolution under Ontology Change

The alignment of ontologies is the backbone of semantic interoperability. It facilitates the import of data from an ontology to another, translating queries between them or merging ontologies in a global one. However, these services cannot be guaranteed throughout the life cycle of the ontology. The problem is that the evolution of mapped ontologies may be affected and make obsolete the relationship of the mapping. Inspired by belief revision theory, the authors identify and formalize the constraints and requirements of the alignment evolution problem. Then they give an orchestration of designed operations to resolve the problem. The satisfaction of these constraints and requirements is discussed for each operation showing its strengths and weaknesses. Finally, the authors conduct an experimental process with the objective to show the limits of alignment evolution methods and ontology matching tools when dealing with alignment evolution problem highlighting the emergency to invest in dedicated methods.

Overview of YAM - (Not) Yet Another Matcher for Ontology Alignment Task

Several challenges to the field of ontology matching have been outlined in recent research. The selection of the appropriate similarity measures as well as the configuration tuning of their combination are known as fundamental issues the community should deal with. Verifying the semantic coherence of the discovered alignment is also known as a crucial task. As the challenging issues are both in basic matching techniques and in their combination, our approach is aimed to provide improvement at the basic matcher level and also at the level of framework. Matching large scale ontologies is currently one of the most challenging issues in ontology matching field. The main reason is that large ontologies are highly heterogeneous both at terminological and conceptual levels. Furthermore, matching very large ontologies entails exploring a very large searching space to discover correspondences. It may also require a huge amount of main memory to maintain the temporary results at each computational step. These factors strongly impact the effectiveness and efficiency of any ontology matching tool. To overcome these issues, we have developed a disk-based ontology matching approach. The underlying idea of our approach is that the complexity and therefore the cost of the matching algorithms are reduced thanks to the indexing data structures by avoiding exhaustive pair-wise comparisons. Indeed, we extensively used indexing techniques in many places. For example, we defined a bitmap encoding the structural information of an ontology. This indexing structure will be exploited for accelerating similarity propagation. Moreover, our approach uses a disk-based mechanism to store temporary data. This allows to perform any ontology matching task on a simple PC or laptop instead of a powerful server.In this paper, we describe YAM , an ontology matching tool, aimed at solving these issues. We evaluated the efficiency of YAM in various OAEI 2012 and OAEI 2013 tracks. YAM was one of the best ontology matching systems in terms of F-measure. Most notably, the current version of YAM has passed all scalability and large scale ontology matching tests and obtained high matching quality results.

Object-oriented and ontology-alignment patterns-based expressive Mediation Bridge Ontology (MBO)

The Semantic Web is dependent on extensive knowledge management by interlinking resources on the web using matching techniques. This role is played by the progressing domain of ontology matching, by introducing ontology-matching tools. The focus of these matching tools is limited to matching techniques and automation, rather than expressive formal representation of alignments. We propose Mediation Bridge Ontology (MBO), an expressive alignment representation ontology used to store correspondences between matching ontologies matched by our ontology-matching tool, System for Parallel Heterogeneity Resolution (SPHeRe). The MBO utilizes object-oriented design patterns and the proposed ontology-alignment design patterns to provide extendibility and reusability factors to SPHeRe system. We compared our proposed system with existing systems using Coupling Factor, Number of Polymorphic methods and Rate of Change metrics to support extendibility and reusability. These factors contribute to the overall objective of interoperability for knowledge management in the Semantic Web.

Assessment of Name Based Algorithms for Land Administration Ontology Matching

The purpose of this paper is to tackle semantic heterogeneity problem between land administration domain ontologies using name based ontology matching approach. The majority of ontology matching solutions use one or more string similarity measures to determine how similar two concepts are. Due to wide variety of available general purpose techniques it is not always clear which ones to use for a specific domain.The goal of this research is to evaluate several most applicable string similarity measures for use in land administration domain ontology matching. To support the research ontology matching tool prototype is developed, where the proposed algorithms are implemented. The practical results of ontology matching for State Land Service of Latvia are presented and analyzed. Matching of Land Administration Domain Model international standard, present Latvian Land Administration ontology is conducted.

KnoE: A Web Mining Tool to Validate Previously Discovered Semantic Correspondences

The problem of matching schemas or ontologies consists of providing corresponding entities in two or more knowledge models that belong to a same domain but have been developed separately. Nowadays there are a lot of techniques and tools for addressing this problem, however, the complex nature of the matching problem make existing solutions for real situations not fully satisfactory. The Google Similarity Distance has appeared recently. Its purpose is to mine knowledge from the Web using the Google search engine in order to semantically compare text expressions. Our work consists of developing a software application for validating results discovered by schema and ontology matching tools using the philosophy behind this distance. Moreover, we are interested in using not only Google, but other popular search engines with this similarity distance. The results reveal three main facts. Firstly, some web search engines can help us to validate semantic correspondences satisfactorily. Secondly there are significant differences among the web search engines. And thirdly the best results are obtained when using combinations of the web search engines that we have studied.

Towards more Challenging Problems for Ontology Matching Tools

We motivate the need for challenging problems in the evaluation of ontology matching tools. To address this need, we propose mapping sets between well-known biomedical ontologies that are based on the UMLS Metathesaurus. These mappings could be used as a basis for a new track in future OAEI campaigns.

Towards more Challenging Problems for Ontology Matching Tools

We motivate the need for challenging problems in the evaluation of ontology matching tools. To address this need, we propose mapping sets between well-known biomedical ontologies that are based on the UMLS Metathesaurus. These mappings could be used as a basis for a new track in future OAEI campaigns (http://oaei.ontologymatching.org/).

Reverse ontology matching

Ontology Matching aims to find the semantic correspondences between ontologies that belong to a single domain but that have been developed separately. However, there are still some problem areas to be solved, because experts are still needed to supervise the matching processes and an efficient way to reuse the alignments has not yet been found. We propose a novel technique named Reverse Ontology Matching, which aims to find the matching functions that were used in the original process. The use of these functions is very useful for aspects such as modeling behavior from experts, performing matching-by-example, reverse engineering existing ontology matching tools or compressing ontology alignment repositories. Moreover, the results obtained from a widely used benchmark dataset provide evidence of the effectiveness of this approach.

Coupling human- and machine-driven mapping of SKOS thesauri

This paper elaborates on the importance of the Simple Knowledge Organisation System (SKOS) format for developing thesauri in the context of Spatial Data Infrastructures (SDIs) and presents a prototype application supporting one of the most important tasks for the harmonisation of SKOS thesauri, i.e., relating independent categorisations of terms that may be developed by distinct thematic communities. This work outlines the main sources for SDI-related thesauri that are currently available and addresses many challenges that shall be tackled to streamline the development process associated with these resources. This paper also describes the ongoing activities for the extension of the tool to support automatic derivation of mappings through recourse to ontology-matching tools and algorithms.

Ontology Matching Based on hypernym, hyponym, holonym, and meronym Sets in WordNet

Considerable research in the field of ontology matching has been performed where information sharing and reuse becomes necessary in ontology development. Measurement of lexical similarity in ontology matching is performed using synset, defined in WordNet. In this paper, we defined a Super Word Set, which is an aggregate set that includes hypernym, hyponym, holonym, and meronym sets in WordNet. The Super Word Set Similarity is calculated by the rate of words of concept name and synset’s words inclusion in the Super Word Set. In order to measure of Super Word Set Similarity, we first extracted Matched Concepts(MC), Matched Properties(MP) and Property Unmatched Concepts(PUC) from the result of ontology matching. We compared these against two ontology matching tools – COMA++ and LOM. The Super Word Set Similarity shows an average improvement of 12% over COMA++ and 19% over LOM.