Dealing with Uncertainty in Lexical Annotation

We present ALA, a tool for the automatic lexical annotation (i.e.annotation w.r.t. a thesaurus/lexical resource) of structured and semi-structured data sources and the discovery of probabilistic lexical relationships in a data integration environment. ALA performs automatic lexical annotation through the use of probabilistic annotations, i.e. an annotation is associated to a probability value. By performing probabilistic lexical annotation, we discover probabilistic inter-sources lexical relationships among schema elements. ALA extends the lexical annotation module of the MOMIS data integration system. However, it may be applied in general in the context of schema mapping discovery, ontology merging and data integration system and it is particularly suitable for performing “on-the-fly” data integration or probabilistic ontology matching

Uncertainty in data integration systems: automatic generation of probabilistic relationships

This paper proposes a method for the automatic discovery of probabilistic relationships in the environment of data integration systems. Dynamic data integration systems extend the architecture of current data integration systems by modeling uncertainty at their core. Our method is based on probabilistic word sense disambiguation (PWSD), which allows to automatically lexically annotate (i.e. to perform annotation w.r.t. a thesaurus/lexical resource) the schemata of a given set of data sources to be integrated. From the annotated schemata and the relathionships defined in the thesaurus, we derived the probabilistic lexical relationships among schema elements. Lexical relationships are collected in the Probabilistic Common Thesaurus (PCT), as well as structural relationships

Description logics for semantic query optimization in object-oriented database systems

Semantic query optimization uses semantic knowledge (i.e., integrity constraints) to transform a query into an equivalent one that may be answered more efficiently. This article proposes a general method for semantic query optimization in the framework of Object-Oriented Database Systems. The method is effective for a large class of queries, including conjunctive recursive queries expressed with regular path expressions and is based on three ingredients. The first is a Description Logic, ODLRE, providing a type system capable of expressing: class descriptions, queries, views, integrity constraint rules and inference techniques, such as incoherence detection and subsumption computation. The second is a semantic expansion function for queries, which incorporates restrictions logically implied by the query and the schema (classes + rules) in one query. The third is an optimal rewriting method of a query with respect to the schema classes that rewrites a query into an equivalent one, by determining more specialized classes to be accessed and by reducing the number of factors. We implemented the method in a tool providing an ODMG-compliant interface that allows a full interaction with OQL queries, wrapping underlying Description Logic representation and techniques to the user