Detecting Restriction Class Correspondences in Linked Data

Abstract

Linked Data consists of many structured data knowledge bases that have been interlinked, often using equivalence statements. These equivalences usually take the form of owl:sameAs statements linking individuals, links between classes are far less common Often, the lack of class links is because their relationships cannot be described as one to one equivalences. Instead, complex correspondences referencing logical combinations of multiple entities are often needed to describe how the classes in an ontology are related to classes in a second ontology. This chapter introduces a novel Bayesian Restriction Class Correspondence Estimation (Bayes-ReCCE) algorithm, an extensional approach to detecting complex correspondences between classes. Bayes-ReCCE operates by analysing features of matched individuals in the knowledge bases, and uses Bayesian inference to search for complex correspondences between the classes these individuals belong to. Bayes-ReCCE is designed to be capable of providing meaningful results even when only small numbers of matched instances are available.

CANARD: An approach for generating expressive correspondences based on competency questions for alignment

“…Overall, CANARD can deliver complex correspondences for all evaluated (populated) datasets in the OAEI, with a higher number of complex(s:c and c:c) correspondences. It would be interesting to compare our approach with extensional approaches such as [12,17,18,35] (whose implementation was not available) even though all of them are limited to (s:c) and (c:s) correspondences. The experiment on the Taxon dataset showed that our approach is one of the few that can perform on large knowledge bases.…”

“…In [38], a structural matching approach (FCM-Map) adopts the Formal Concept Analysis (FCA) method to find complex correspondence candidates. The approaches in [18,35] use statistical information based on the linked instances to find correspondences fitting a given pattern. The one in [20] uses a path-finding algorithm to find correspondences between two knowledge bases with common instances.…”

“…None of these approaches involve, however, the user before or during the matching process. As in [12,17,18,20,35,41], CANARD relies on common instances. Differently from them, it does not rely on correspondence patterns.…”

“…These generalisation processes however require a considerable amount of data (or instances). Approaches such as the ones from [12,17,18,35] rely on large amounts of common ontology instances for finding complex correspondences. Few exceptions in ontology matching rely on a few examples.…”

“…Earlier works in the field have introduced the need for expressive alignments [15,34], and different approaches for generating them have been proposed in the literature afterwards. These approaches rely on diverse methods, such as correspondence patterns [9,22,23], knowledge-rules [13] and association rules [40], statistical methods [18,35], genetic programming [19] or still path-finding algorithms [20]. The reader can refer to [27] for a survey on complex matching approaches.…”