This paper describes the design of a pattern matcher for a knowledge representation system called LOOM. The pattern matcher has a very rich pattern-forming language, and is logic-based, with a deductive mechanism which includes a truth-maintenance component as an integral part of the pattern-matching logic. The technology behind the LOOM matcher uses an inference engine called a classiier to pe...

In this paper we introduce ENP-regex, a prototype of a regular expression matcher developed for Expressive Notation Package (ENP). ENP-regex allows us to use the regular expression syntax to match against several score attributes, such as pitch and rhythm. Instead of writing the regular expression matcher from scratch we implement a scheme where a thin conversion layer is inserted between an ex...

AgreementMakerLight (AML) is an automated ontology matching framework based on element-level matching and the use of external resources as background knowledge. This paper describes the configuration of AML for the OAEI 2013 competition and discusses its results. Being a newly developed and still incomplete system, our focus in this year’s OAEI were the anatomy and large biomedical ontologies t...

A generic semantic-driven data matching framework (SDD-Matcher) has been designed and developed for matching data objects across organizations. It contains matching algorithms at three different levels: string, lexical and graph. The level of graph is also called ontological or conceptual level. Those matching algorithms are the basic building blocks of an SDDMatcher matching strategy, each of ...

Abstract We show how to systematically derive an efficient regular expression (regex) matcher using a variety of program transformation techniques, but very little specialized formal language and automata theory. Starting from the standard specification set-theoretic semantics expressions, we proceed via continuation-based backtracking matcher, classical, table-driven state machine. All steps d...

Discovering correspondences between schema elements is a crucial task for data integration. Most schema matching tools are semiautomatic, e.g., an expert must tune certain parameters (thresholds, weights, etc.). They mainly use aggregation methods to combine similarity measures. The tuning of a matcher, especially for its aggregation function, has a strong impact on the matching quality of the ...

Discovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matchin...

Specializing string matchers is a canonical example of partial evaluation. A naive implementation of a string matcher repeatedly matches a pattern against every substring of the data string; this operation should intuitively benefit from specializing the matcher with respect to the pattern. In practice, however, producing an efficient implementation by performing this specialization using stand...

Discovering correspondences between schema elements is a crucial task for data integration. Most matching tools are semi-automatic, e.g. an expert must tune some parameters (thresholds, weights, etc.). They mainly use several methods to combine and aggregate similarity measures. However, their quality results often decrease when one requires to integrate a new similarity measure or when matchin...