A faster and less aggressive algorithm for correcting conservativity violations in ontology alignments

Applied Ontology ◽

10.3233/ao-210243 ◽

2021 ◽

pp. 1-20

Author(s):

Cauã Roca Antunes ◽

Alexandre Rademaker ◽

Mara Abel

Keyword(s):

State Of The Art ◽

Time Algorithm ◽

The State ◽

Ontology Alignment ◽

Ontology Alignment Evaluation Initiative ◽

Multiple Ontologies

Ontologies are computational artifacts that model consensual aspects of reality. In distributed contexts, applications often need to utilize information from several distinct ontologies. In order to integrate multiple ontologies, entities modeled in each ontology must be matched through an ontology alignment. However, imperfect alignments may introduce inconsistencies. One kind of inconsistency, which is often introduced, is the violation of the conservativity principle, that states that the alignment should not introduce new subsumption relations between entities from the same source ontology. We propose a two-step quadratic-time algorithm for automatically correcting such violations, and evaluate it against datasets from the Ontology Alignment Evaluation Initiative 2019, comparing the results to a state-of-the-art approach. The proposed algorithm was significantly faster and less aggressive; that is, it performed fewer modifications over the original alignment when compared to the state-of-the-art algorithm.

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Ontology mapping: the state of the art

The Knowledge Engineering Review ◽

10.1017/s0269888903000651 ◽

2003 ◽

Vol 18 (1) ◽

pp. 1-31 ◽

Cited By ~ 608

Author(s):

YANNIS KALFOGLOU ◽

MARCO SCHORLEMMER

Keyword(s):

Semantic Web ◽

Theoretical Approach ◽

State Of The Art ◽

The State ◽

Ontology Mapping ◽

Distributed Environment ◽

Diverse Communities ◽

Exchange Information ◽

Multiple Ontologies ◽

The Web

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mappings has been the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping.

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Sensor Ontology Metamatching with Heterogeneity Measures

Wireless Communications and Mobile Computing ◽

10.1155/2020/6666228 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Xingsi Xue ◽

Jiawei Lu ◽

Chengcai Jiang ◽

Yikun Huang

Keyword(s):

State Of The Art ◽

Similarity Measures ◽

Ontology Matching ◽

Learning Approach ◽

Ontology Alignment ◽

Matching Process ◽

Ontology Alignment Evaluation Initiative ◽

Matching Techniques ◽

First Time ◽

Concept Pairs

The heterogeneity problem among different sensor ontologies hinders the interaction of information. Ontology matching is an effective method to address this problem by determining the heterogeneous concept pairs. In the matching process, the similarity measure serves as the kernel technique, which calculates the similarity value of two concepts. Since none of the similarity measures can ensure its effectiveness in any context, usually, several measures are combined together to enhance the result’s confidence. How to find suitable aggregating weights for various similarity measures, i.e., ontology metamatching problem, is an open challenge. This paper proposes a novel ontology metamatching approach to improve the sensor ontology alignment’s quality, which utilizes the heterogeneity features on two ontologies to tune the aggregating weight set. In particular, three ontology heterogeneity measures are firstly proposed to, respectively, evaluate the heterogeneity values in terms of syntax, linguistics, and structure, and then, a semiautomatically learning approach is presented to construct the conversion functions that map any two ontologies’ heterogeneity values to the weights for aggregating the similarity measures. To the best of our knowledge, this is the first time that heterogeneity features are proposed and used to solve the sensor ontology metamatching problem. The effectiveness of the proposal is verified by comparing with using state-of-the-art ontology matching techniques on Ontology Alignment Evaluation Initiative (OAEI)’s testing cases and two pairs of real sensor ontologies.

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Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Information ◽

10.3390/info12110487 ◽

2021 ◽

Vol 12 (11) ◽

pp. 487

Author(s):

Sohaib Al-Yadumi ◽

Wei-Wei Goh ◽

Ee-Xion Tan ◽

Noor Zaman Jhanjhi ◽

Patrice Boursier

Keyword(s):

State Of The Art ◽

Model Performance ◽

Background Knowledge ◽

Ontology Matching ◽

Semantic Heterogeneity ◽

Ontology Alignment ◽

Selection Algorithm ◽

Confidence Measure ◽

Proposed Model ◽

Ontology Alignment Evaluation Initiative

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.

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Matching biodiversity and ecology ontologies: challenges and evaluation results

The Knowledge Engineering Review ◽

10.1017/s0269888920000132 ◽

2020 ◽

Vol 35 ◽

Cited By ~ 1

Author(s):

Naouel Karam ◽

Abderrahmane Khiat ◽

Alsayed Algergawy ◽

Melanie Sattler ◽

Claus Weiland ◽

...

Keyword(s):

Functional Diversity ◽

State Of The Art ◽

Ontology Matching ◽

Use Case ◽

Ontology Alignment ◽

Biodiversity Data ◽

Specific Domain ◽

Biodiversity Research ◽

Heterogeneous Nature ◽

Ontology Alignment Evaluation Initiative

Abstract Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are themselves differently conceived, and combining them in semantically enabled applications necessitates an effective alignment between their concepts. This paper describes the ontology matching of biodiversity- and ecology-related ontologies. We illustrate diverse challenges introduced by this kind of ontologies to ontology matching in general. Real use cases requiring pairwise alignments between environment and trait ontologies are introduced. We describe our experience creating a new track at the Ontology Alignment Evaluation Initiative designed for this specific domain and report on the results obtained by state-of-the-art participating systems. The biodiversity and ecology use case turns out to be a strong one for ontology matching, introducing new interesting challenges. Even if most of the matching systems perform relatively well in the proposed matching tasks, there is still room for improvement. We highlight possible directions in that matter and elaborate on our plan to further progress with the track.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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