Schema Evolution publication categorizer

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 OAEI 2012 evaluation results for the matching system GOMMA developed at the University of Leipzig. The original application focus of GOMMA has been the life science domain but as a generic tool it can also match ontologies from other areas. It could thus participate in all OAEI tracks running on the SEALS platform. GOMMA supports several methods for efficiently matching large ontologies in particular parallel matching on multiple cores or machines, reducing the search space as well as reusing and composing previous mappings to related ontologies.

Benchmarking approaches for ontology merging is challenging and has received little attention so far. A key problem is that there is in general no single best solution for a merge task and that merging may either be performed symmetrically or asymmetrically. As a first step to evaluate the quality of ontology merging solutions we propose the use of general metrics such as the relative coverage of the input ontologies, the compactness of the merge result as well as the degree of introduced redundancy. We use these metrics to evaluate three merge approaches for different merge scenarios.

Schema integration is a central task for data integration. Over the years, many tools have been developed to discover correspondences between schemas elements. Some of them produce an integrated schema. However, the schema matching community lacks some metrics which evaluate the quality of an integrated schema. Two measures have been proposed, completeness and minimality. In this paper, we extend these metrics for an expert integrated schema. Then, we complete them by another metric that evaluates the structurality of an integrated schema.

A fundamental problem in information integration is to precisely specify the relationships, called mappings, between schemas. Designing mappings is a time-consuming process. To alleviate this problem, many mapping systems have been developed to assist the design of mappings. However, a benchmark for comparing and evaluating these systems has not yet been developed.

We present XBenchMatch, a benchmark which uses as input the result of a schema matching algorithm (set of mappings and/or an integrated schema) and generates statistics about the quality of this input and the performance of the matching tool.

Ontology mapping is the key to data interoperability in the semantic

During data warehouse design, the designer frequently encounters the problem of choosing among different alternatives for the same design construct. The behavior of the chosen design in the presence of evolution events is an important parameter for this choice. This paper proposes metrics to assess the quality of the warehouse design from the viewpoint of evolution. We employ a graph-based model to uniformly abstract relations and software modules, like queries, views, reports, and ETL activities. We annotate the warehouse graph with policies for the management of evolution events.

We propose the use of application semantics to enhance the process of semantic reconciliation. Application semantics involve those elements of the business reasoning that affect the way concepts are presented to users, their layout, etc. In particular, we pursue in this paper the notion of precedence, in which temporal constraints determine the ordering of concepts when presented to the user.

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.

Ontologies now play an important role for many knowledge-intensive
applications for which they provide a source of precisely defined terms. However,
with their wide-spread usage there come problems concerning their proliferation.
Ontology engineers or users frequently have a core ontology that they
use, e.g., for browsing or querying data, but they need to extend it with, adapt it
to, or compare it with the large set of other ontologies. For the task of detecting
and retrieving relevant ontologies, one needs means for measuring the similarity

The introduction of the Semantic Web vision and
the shift toward machine understandable Web resources has
unearthed the importance of automatic semantic reconciliation.
Consequently , new tools for automating the process
were proposed.In this work we present a formal model of
semantic reconciliation and analyze in a systematic manner
the properties of the process outcome, primarily the inherent
uncertainty of the matching process and how it reflects on
the resulting mappings.An important feature of this research
is the identification and analysis of factors that impact the