Schema Evolution publication categorizer

Merging of relationships among data is an important activity in schema integration. The latter can arise as integration of user views In logical database design or as the creation of a global schema from existing databases in a distributed or centralized environment. During the “view lntegration" phase of design, separate views of data held by different user groups are integrated into a single conceptual schema for the entire organization.

Schema integration is the activity of providing a unified representation of multiple data sources. The core problems in schema integration are: schema matching, i.e. the identification of correspondences, or mappings, between schema objects, and schema merging, i.e. the creation of a unified schema based on the identified mappings. Existing schema matching approaches attempt to identify a single mapping between each pair of objects, for which they are 100% certain of its correctness. However, this is impossible in general, thus a human expert always has to validate or modify it.

Merging schemas or other structured data occurs in many different data
models and applications, including merging ontologies, view
integration, data integration and computer supported collaborative
work. This paper describes some of the key works in merging
schemas and discusses some of the commonalities and differences.

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.

Data integration systems offer a uniform interface to a set of data sources. Despite recent progress, setting up and maintaining a data integration application still requires significant upfront effort of creating a mediated schema and semantic mappings from the data sources to the mediated schema. Many application contexts involving multiple data sources (e.g., the web, personal information management, enterprise intranets) do not require full integration in order to provide useful services, motivating a pay-as-you-go approach to integration.

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.

The proliferation of ontologies and taxonomies in
many domains increasingly demands the integration of multiple
such ontologies. The goal of ontology integration is to merge two
or more given ontologies in order to provide a unified view on the
input ontologies while maintaining all information coming from
them. We propose a new taxonomy merging algorithm that, given
as input two taxonomies and an equivalence matching between
them, can generate an integrated taxonomy in a fully automatic
manner. The approach is target-driven, i.e. we merge a source

The proliferation of ontologies and taxonomies in
many domains increasingly demands the integration of multiple
such ontologies to provide a unified view on them. We demonstrate
a new automatic approach to merge large taxonomies such
as product catalogs or web directories. Our approach is based on
an equivalence matching between a source and target taxonomy
to merge them. It is target-driven, i.e. it preserves the structure
of the target taxonomy as much as possible. Further, we show
how the approach can utilize additional relationships between

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.

An increasing number of databases are becoming Web accessible through form-based search interfaces, and

Recently, there are many e-commerce search engines that return information from Web databases. Unlike text search engines, these e-commerce search engines have more complicated user interfaces. Our aim is to construct automatically a natural query user interface that integrates a set of interfaces over a given domain of interest. For example, each airline company has a query interface for ticket reservation and our system can construct an integrated interface for all these companies. This will permit users to access information uniformly from multiple sources.

We consider the problem of integrating a large number of interface schemas over the Deep Web, The scale of the problem and the diversity of the sources present serious challenges to the conventional manual or rule-based approaches to schema integration. To address these challenges, we propose a novel formulation of schema integration as an optimization problem, with the objective of maximally satisfying the constraints given by individual schemas.

Semantic matching of schemas in heterogeneous data sharing systems is time consuming and error prone. Existing mapping tools employ semi-automatic techniques for mapping two schemas at a time. In a large-scale scenario, where data sharing involves a large number of data sources, such techniques are not suitable. We present a new robust automatic method which discovers semantic schema matches in a large set of XML schemas, incrementally creates an integrated schema encompassing all schema trees, and defines mappings from the contributing schemas to the integrated schema.

In order to compute intelligent answers to complex questions, using the vast amounts of information existing in the Web, computers have (1) to translate such knowledge, typically from text documents, into a data structure suitable for automatic exploitation; (2) to accumulate enough knowledge about a certain topic or area by integrating or fusing these data structures, taking into account new information, additional details, better precision, synonyms, homonyms, redundancies, apparent contradictions and inconsistencies found in the incoming data structures to be added; and (3) to perform deduc

Model management is a high-level programming language designed to efficiently manipulate schemas and mappings. It is comprised of robust operators that combined in short programs can solve complex metadata-oriented problems in a compact way. For instance, countless enterprise data integration scenarios can be easily expressed in this high-level language thus saving hundreds of development man-hours. Here we present the first model management engine that has reverse-engineering support for data integration, which is one of the most pressing metadata-oriented problems.

We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. In this paper we attempt to use a powerful classification method, Support Vector Machine (SVM), to attack this problem.

Merging inheritance hierarchies with overlapping class extensions and types is an essential task in database design. In the context of view integration and schema integration for federated databases and multidatabases conflicting inheritance hierarchies have to be merged. Inheritance hierarchies often occur explicitly in object-oriented databases as well as implicitly in relational databases. Since a concept lattice can be regarded as an inheritance hierarchy we propose to apply the theory of concept analysis to the problem of merging inheritance hierarchies.

We describe the underlying operators and rules of an interactive procedure for merging generalization hierarchies. This procedure assists a designer in defining a global view which is a view over multiple databases. The small collection of operators permit:

(1) connecting generalization hierarchies to form a new hierarchy,
(2) adding and deleting subhierarchies
(3) deleting intermediate levels.

Internet marketplaces are now faced with new
challenges that arise from the need to seamlessly
integrate enormous number of product catalogs from
different sources. In order to help users find products
efficiently, Internet shops provide hierarchies of product
catalogs (called category hierarchies). However, the
absence of robust models (and well understood
semantics) for product catalogs and their hierarchies
severely impairs our ability to systematically support
structured operations.
In this paper, we present an extended catalog model

We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to learn a classifier that can classify objects from the source taxonomy into categories of the master taxonomy. The key insight is that the availability of the source taxonomy data could be helpful to build better classifiers for the master taxonomy if their categorizations have some semantic overlap.

Many approaches have been proposed for the
of mapping categories (classes) from a source
to classes in a master taxonomy. Most of techniques,
however, ignore the hierarchical structure
taxonomies. In this paper, we propose a maximum likelihood
based framework which exploits the hierarchical
structure to obtain a more natural mapping between
source classes and the master taxonomy. Furthermore,
unlike previous work, our technique also inserts
classes into appropriate places of the master
creating new categories if required. We evaluate approach
on text and hyperspectral datasets.

Ontology merging is the process of creating a new ontology from two or more existing ontologies with overlapping parts. Currently, there are many domain areas in Computer Science interested in this topic. Federated Databases and Semantic Web are some of them. In this paper we introduce a three level approach that provides a semi-automatic method to ontology merging. It performs some tasks automatically and guides the user in performing other tasks for which his intervention is required.

In this paper, we propose a multi-strategic matching and merging approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the Topic Maps. Our multi-strategic matching approach consists of a linguistic module and a Topic Map constraints-based module. A linguistic module computes similarities between concepts using morphological analysis, string normalization and tokenization and language-dependent heuristics.

Ontology merging and alignment is one of the effective methods for ontology sharing and reuse on the Semantic Web. A number of ontology merging and alignment tools have been developed, many of those tools depend mainly on concept (dis)similarity measure derived from linguistic cues. We present in this paper a linguistic information based approach to ontology merging and alignment.

Taxonomies are widely used to classify information, and multiple (possibly competing) taxonomies often exist for the same domain. Given a set of correspondences between two taxonomies, it is often necessary to "merge" the taxonomies, thereby creating a unified taxonomy (e.g., that can then be used by data integration and discovery applications). We present an algorithm for merging taxonomies that have been related using articulations given as RCC-5 constraints.