Accurate Identification of Ontology Alignments at Different Granularity Levels

Hu, Xiaocao; Feng, Zhiyong; Chen, Shizhan; Huang, Keman; Li, Jianqiang; Zhou, MengChu

doi:10.1109/access.2016.2614759

Cited by 14 publications

(8 citation statements)

References 58 publications

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“…1. Directly infer correspondence using the reasoner with inputting conclusions from the lexical analysis phase [16], [22]- [24], [36]. 2.…”

Section: A Analysis Of Present Situationmentioning

confidence: 99%

“…S-Match defines the concept of a node whose logic expression is computed as the intersection of the concepts of all labels from the root node to the node itself. CtxMatch [36] and S-Match submit expressions of conclusions obtained at the lexical analysisbased phase to the deciders, and define the inferences of the deciders as the correspondence between entities. ILIADS uses the inferences of the deciders as the foundation for computing semantic similarity.…”

Section: A Analysis Of Present Situationmentioning

confidence: 99%

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Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

et al. 2021

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Information used in existing ontology matching solutions are usually grouped into four categories: lexical information, structural information, semantic information, and external information, respectively. By summarizing and analyzing the approaches for utilizing the same kind of information, this paper finds that lexical information is mainly analyzed based on text and dictionary similarity. Similarly, structural information and semantic information are mainly analyzed based on graph structure and reasoner, respectively. The approaches for aggregating information analysis results are discussed. Challenges in the analysis of various types of information for existing ontology matching solutions are also described, and insights into directions for future research are provided.

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“…1. Directly infer correspondence using the reasoner with inputting conclusions from the lexical analysis phase [16], [22]- [24], [36]. 2.…”

Section: A Analysis Of Present Situationmentioning

confidence: 99%

Section: A Analysis Of Present Situationmentioning

confidence: 99%

Ontology Matching: State of the Art, Future Challenges, and Thinking Based on Utilized Information

et al. 2021

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“…The equation (11) means that the risk τ i follows a beta distribution whose mean is τ * . Thus, the values of τ i are at the neighborhood of τ * , and their proximity is controlled by the parameter γ .…”

Section: Risk Approximation: a Bayesian Hierarchical Modelmentioning

confidence: 99%

“…The parameters γ and τ * are also unknown, hence we again model them as a distribution. According to equation (11), the values of γ must be greater than one since the concentration parameter cannot be negative. There are many distributions for non-negative variables, and we use here the gamma distribution for γ , e.g.,…”

Section: Risk Approximation: a Bayesian Hierarchical Modelmentioning

confidence: 99%

“…Due to its diverse applicability, the ontology alignment has been the topic of many research so that numerous alignment systems are put forward to discover the mappings of two given ontologies [11]- [22]. Also, the OAEI has taken place for more than a decade whose primary objectives are to monitor the progress of the field, bring together various alignment systems, and compare them systematically.…”

Section: Introductionmentioning

confidence: 99%

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Bayesian Evaluation and Comparison of Ontology Alignment Systems

Mohammadi

2019

IEEE Access

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Ontology alignment systems are evaluated by various performance scores, which are usually computed by a ratio related directly to the frequency of the true positives. However, such ratios provide little information regarding the uncertainty of the overall performance of the corresponding systems. The comparison is also drawn merely by the juxtaposition of computed scores, and specify that one system is superior to one another provided that its score is higher. Nonetheless, the comparison based solely on two figures would not quantify the significance of difference and would not determine the extent to which one system is better. The problem compounds for comparison over multiple benchmarks since averages and micro-averages of performance scores are considered. In this paper, the evaluation of alignment systems is translated into a statistical inference problem by introducing the notion of risk for alignment systems. The risk with respect to a performance score is shown to follow a binomial distribution and is equivalent to the complement of the score, e.g., precision risk = 1 − precision. It is also demonstrated that the maximum likelihood estimation (MLE) is precisely equivalent to the conventional evaluation by using ratios. Instead of using the MLE, the Bayesian model is developed to estimate the risk with respect to a score (or equivalently, the score itself) as a probability distribution from the performance of the systems over single or multiple benchmarks. As a result, the evaluation outcome is a distribution instead of a figure, which provides a broader view of the overall system performance. A Bayesian test is also devised to compare various systems based on their estimated risks, which can compute the confidence that one system is superior to one another. We report the result of applying the proposed methodology to multiple tracks from the ontology alignment evaluation initiative (OAEI).

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