The properties of property alignment on the semantic web

Abstract

2014. The Properties of Property Alignment on the Semantic Web. Ontology alignment is an important step in enabling computers to query and reason across the many linked datasets on the semantic web. This is a difficult challenge because the ontologies underlying different linked datasets can vary in terms of subject area coverage, level of abstraction, ontology modeling philosophy, and even language. The alignment approach presented here centers on string similarity metrics. Nearly all ontology alignment systems use a string similarity metric in one form or another, but it seems that the choice of a particular metric is often arbitrary. We begin this dissertation with the most comprehensive survey to date on the performance of string similarity metrics and string preprocessing strategies for ontology alignment. Based on this work we present practical guidelines for choosing string metrics in the face of different types of ontologies and different alignment goals. Additionally, we show that string similarity metrics alone can perform competitively with state-of-the-art alignment systems on the most popular benchmarks in the field. One of the contributions of our string similarity metric survey is quantification of the difference in performance between aligning classes and aligning properties (relations between classes). Put simply: aligning properties is hard, and existing string similarity metrics are not of great help. We therefore take on the task of developing a new string-based alignment approach that performs better on properties. Unfortunately, evaluating that approach is difficult because the only existing alignment benchmark that includes properties is, in our view, unrealistic since all relations in the reference alignment are presented as completely certain. Human experts do not have this degree of confidence when asked to align an ontology. We therefore present a more nuanced version of this benchmark that we have created through a combination of expert survey and crowdsourcing. We then present our new string-based property alignment system and evaluate its performance on both the current benchmark and our proposed revision. Our property-centric string metric can be configured for either high precision or high recall. The results show a five-fold increase in precision and a doubling of recall over an approach based on the best current string metric. Finally, we apply our system to a real-world test case and analyze the results.

Cleaning by clustering: methodology for addressing data quality issues in biomedical metadata

“…1 ): Name similarity , denoted by sim name (), is computed by comparing the lexical names of the keys, such as “tissue isolated” and “tissue derived”. Core concept similarity , denoted by sim core (), is computed by comparing the most important concepts (called core concepts [ 10 ]) in the names of the keys. The core concept is either the first verb in the name that is greater than four characters long or, if there is no such verb, the first noun in the name, together with any adjective modifying that noun.…”

SANOM-HOBBIT: simulated annealing-based ontology matching on HOBBIT platform

“…A recent study (Cheatham & Hitzler, 2014) has investigated that the mapping of properties based solely on their names would lead to both high false-positive and false-negative rates. Following the recommendation of Cheatham and Hitzler (2014), we further use the core concept of the properties as one of their names.…”

Bayesian Evaluation and Comparison of Ontology Alignment Systems

“…A typical ontology matching system takes two ontologies (usually referred as the source and the target) as the input and tries to find the similar entities of the source to those of the target [2]. To find the identical entities, one might use several similarity metrics, e.g., string, linguistic, structural similarity measures [2], [31], [32].…”