Schema Evolution publication categorizer

The schema and ontology matching systems COMA and COMA++ are widely used in the community as a basis for comparison of new match approaches. We give an overview of the evolution of COMA during the last decade. In particular we discuss lessons learned on strong points and remaining weaknesses. Furthermore, we outline the design and functionality of the upcoming COMA 3.0.

The book edited by Bellahsene, Bonifati and Rahm provides an overview of the ways in which the schema and ontology matching and mapping tools have addressed the above requirements and points to the open technical challenges. The contributions from leading experts are structured into three parts: large-scale and knowledge-driven schema matching, quality-driven schema mapping and evolution, and evaluation and tuning of matching tasks.

We present the Auto Mapping Core (AMC), a new
framework that supports fast construction and tuning of schema
matching approaches for specific domains such as ontology
alignment, model matching or database-schema matching.
Distinctive features of our framework are new visualisation
techniques for modelling matching processes, stepwise tuning of
parameters, intermediate result analysis and performanceoriented
rewrites. Furthermore, existing matchers can be
plugged into the framework to comparatively evaluate them in a
common environment. This allows deeper analysis of behaviour

Mapping complex metadata structures is crucial in a number of domains such as data integration, ontology alignment or model management. To speed up that process automatic matching systems were developed to compute mapping suggestions that can be corrected by a user. However, constructing and tuning match strategies still requires a high manual effort by matching experts as well as correct mappings to evaluate generated mappings. We therefore propose a self-configuring schema matching system that is able to automatically adapt to the given mapping problem at hand.

Schemr is a schema search engine, and provides users the ability to search for and visualize schemas stored in a metadata repository. Users may search by keywords and by example -- using schema fragments as query terms. Schemr uses a novel search algorithm, based on a combination of text search and schema matching techniques, as well as a structurally-aware scoring metric. Schemr presents search results in a GUI that allows users to explore which elements match and how well they do. The GUI supports interactions, including panning, zooming, layout and drilling-in.

A key aspect of any data integration endeavor is determining the relationships between the source schemata and the target schema. This schema integration task must be tackled regardless of the integration architecture or mapping formalism. In this paper, we provide a task model for schema integration. We use this breakdown to motivate a workbench for schema integration in which multiple tools share a common knowledge repository.

System interoperability is a well known issue, especially for heterogeneous information systems, where ontology-based representations may support automatic and user-transparent integration. In this paper we present X-SOM: an ontology mapping and integration tool. The contribution of our tool is a modular and extensible architecture that automatically combines several matching techniques by means of a neural network, performing also ontology debugging to avoid inconsistencies.

The life of a modern Information System is often characterized by (i) a push toward integration with other systems, and (ii) the evolution of its data management core in response to continuously changing application requirements. Most of the current proposals dealing with these issues from a database perspective rely on the formal notions of mapping and query rewriting.

Schema integration is the process by which schemata from heterogeneous databases are conceptually integrated into a single cohesive schema. In this work we propose a modeling framework for schema integration, capturing the inherent uncertainty accompanying the integration process. The model utilizes a fuzzy framework to express a confidence measure, associated with the outcome of a schema integration process.

Information seeking is the process in which human beings recourse to information resources in order to increase their level of knowledge with respect to their goals. In this paper we offer a methodology for automating the evolution of ontologies and share the results of our experiments in supporting a user in seeking information using interactive systems. The main conclusion of our experiments is that if one narrows down the scope of the domain, ontologies can be extracted with a very high level of precision (more than 90% in some cases).

This paper summarizes the OAEI Contest 2006 results for the matching tool
COMA++. The study shows that a generic schema matching system can also
effectively solve complex ontology matching tasks.

Learning to Map between Structured Representations of Data

Schema matching aims at identifying semantic correspondences between elements of two schemas, e.g., database schemas, ontologies, and XML message formats. It is needed in many database applications, such as integration of web data sources, data warehouse loading and XML message mapping. In today's systems, schema matching is manual; a time-consuming, tedious, and error-prone process, which becomes increasingly impractical with a higher number of schemas and data sources to be dealt with.

A broad spectrum of data is available on the Web in distinct heterogeneous sources, and stored under different formats. As the num- ber of systems that utilize this heterogeneous data grows, the importance of data translation and conversion mechanisms increases greatly. In this paper we present a new translation system, based on schema-matching, aimed at simplifying the intricate task of data conver- sion. We observe that in many cases the schema of the data in the source system is very similar to that of the target system.