Rule-based information integration

In this report, we show the process of information integration. We specifically discuss the language used for integration. We show that integration consists of two phases, the schema mapping phase and the data integration phase. We formally define transformation rules, conversion, evolution and versioning. We further discuss the integration process from a data point of view

A collective, probabilistic approach to schema mapping using diverse noisy evidence

We propose a probabilistic approach to the problem of schema mapping. Our approach is declarative, scalable, and extensible. It builds upon recent results in both schema mapping and probabilistic reasoning and contributes novel techniques in both fields. We introduce the problem of schema mapping selection, that is, choosing the best mapping from a space of potential mappings, given both metadata constraints and a data example. As selection has to reason holistically about the inputs and the dependencies between the chosen mappings, we define a new schema mapping optimization problem which captures interactions between mappings as well as inconsistencies and incompleteness in the input. We then introduce Collective Mapping Discovery (CMD), our solution to this problem using state-of-the-art probabilistic reasoning techniques. Our evaluation on a wide range of integration scenarios, including several real-world domains, demonstrates that CMD effectively combines data and metadata information to infer highly accurate mappings even with significant levels of noise

Towards Automated Invocation of Web APIs

This paper, targeting the large variety of Web APIs, presents an approach towards automated invocation of Web APIs. This approach applies a data schema, to the SAWSDL lowering schema mapping, a grounding mechanism that connects the ontological representations of Web APIs with their execution messages. It is intuitive to existing standard efforts and effective in coping with the heterogeneities witnessed by a majority of Web APIs

SchemaMapper: A tool for visualization of schema mapping

The world has changed significantly in the past few years with an increasing thrust towards the use of digital information. Every kind of application domain has found reasons to use digital information sources extensively. As a result, different types of data representation models or schemas have been developed. This poses a problem when there is a need for data integration from several sources. Diverse representations must be merged in order to create a single global representation. Hence there is a need for schema mapping tools that will enable amalgamation of heterogeneous data representations. That goal is difficult to achieve today since existing schema mapping tools are domain unaware. SchemaMapper, a new tool we have developed, tries to be domain aware and hence help speed up the schema mapping process. Further, it supports visualization of the mapping process by using a hyperbolic tree representation. This has not been used before in the context of schema mapping. Although the primary motivation for SchemaMapper comes from ETANA-DL (a digital library to promote integration of information and services from diverse archaeological sites), it can potentially be used in any other similar domains in the future, or further extended for different types of schema mappings. This report describes in detail the prototype developed for exploring the feasibility of such a tool, providing architecture and implementation details. Experiments were conducted to evaluate SchemaMapper and the initial results have been very encouraging. All the schemas used during the evaluation process were real life examples taken from ETANA-DL. Analysis of the evaluation results suggests that domain awareness is extremely useful for the schema mapping process. Also, the linear tree representation of schemas which existing tools use appears to have inherent disadvantages which need to be overcome in order to make the process more effective

The Inverse of a Schema Mapping

The inversion of schema mappings has been identified as one of the fundamental operators for the development of a general framework for data exchange, data integration, and more generally, for metadata management. Given a mapping M from a schema S to a schema T, an inverse of M is a new mapping that describes the reverse relationship fromT to S, and that is semantically consistent with the relationship previously established by M. In practical scenarios, the inversion of a schema mapping can have several applications. For example, in a data exchange context, if a mapping M is used to exchange data from a source to a target schema, an inverse of M can be used to exchange the data back to the source, thus reversing the application of M. The formalization of a clear semantics for the inverse operator has proved to be a very difficult task. In fact, during the last years, several alternative notions of inversion for schema mappings have been proposed in the literature. This chapter provides a survey on the different formalizations for the inverse operator and the main theoretical and practical results obtained so far. In particular, we present and compare the main proposals for inverting schema mappings that have been considered in the literature. For each one of them we present their formal semantics and characterizations of their existence. We also present algorithms to compute inverses and study the language needed to express such inverses

Schema Mapper: A Visualization Tool for DL Integration

Schema mapping is a challenging problem. It has come to the fore in recent years; there are important applications like database schema integration and, more recently, digital library merging of heterogeneous data. Previous studies have approached the schema mapping process either from algorithmic or visualization perspectives, with few integrating both. With Schema Mapper we demonstrate a semi-automatic tool for schema integration that combines a novel visual interface with an algorithm-based recommendation engine. Schemas are visualized as hyperbolic trees (see Fig. 1), thus allowing more schema nodes to be displayed at one time. Matches to selections are recommended to the user, which makes the mapping operation easier and faster