Hybrid Measure of Agreement and Expertise for Ontology Matching in Lieu of a Reference Ontology

Abstract

Ontologies have been widely used as a knowledge representation framework, and numerous methods have been put forth to match ontologies. It is well known that ontology matchers behave differently in various domains, and it is a challenge to predict or characterize their behavior. Herein, a hybrid expertise-agreement aggregation strategy is proposed. Although others rely on the existence of a reference ontology, this typically does not exist in the real world. In this article, the fuzzy integral (FI) is used to aggregate multiple ontology matchers in lieu of a reference ontology. Specifically, we present a measure of expertise and fuse it with our previous agreement measure that is motivated by crowd sourcing to improve recall. This way, any available domain knowledge, in terms of partial ordering of a subset of inputs, can be included in the decisionmaking process. By adding the domain knowledge to the agreement model, we are able to reach the upmost performance. Preliminary results demonstrate the robustness of our approach across domains. Sensitivity analysis is also provided, which shows the limits to which extreme destructive expertise affects system performance. C 2015 Wiley Periodicals, Inc.

FMLNCSIM: fuzzy measure-based lncRNA functional similarity calculation model

“…For example, k-additive fuzzy measure reduces the number of variables for definition by limiting the interaction between its subsets [76, 77]. In this work, Sugeno λ-measures, one of the most widely and successfully used class of fuzzy measures, were introduced to calculate disease semantic similarity based on disease MeSH DAGs [78, 79]. Specifically, the information content (IC) for each disease term were computed based on the corresponding MeSH DAG, and further used as fuzzy density values.…”

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

“…The difference between these solutions is the method used for setting weights and thresholds. For example, RiMOM sets wi via the dynamic multi-strategy method, ILIADS sets μ via the heuristic algorithm, FOAM [41], [42] specifies wi via the ordered weighted average method, and ASMOV sets wi and μ empirically. In [43], the discrete weighting method is used to aggregate lexical information and semantic information analysis results.…”

“…Various techniques are gamed through the use of parameters and aggregated to provide desired results. A system that uses similar methods is also described in [42]. Lily primarily uses the semantic description-based text matcher and the similarity dissemination matcher.…”

Explainable data fusion

“…The fuzzy integral has been demonstrated numerous times in a variety of applications; e.g., explosive hazard detection [22,23], computer vision [24], pattern recognition [25,26,27], multi-criteria decision making [28,29], forensic anthropology [30,31], fuzzy logic [32], multiple kernel learning [33], multiple instance learning [34], ontologies [35], missing data [36], and deep learning for remote sensing [37,38], to name a few. The ChI is a nonlinear aggregation function parameterized by the FM.…”