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.

Background
Ontologies are increasingly used to structure and semantically describe entities of domains, such as genes and proteins in life sciences. Their increasing size and the high frequency of updates resulting in a large set of ontology versions necessitates efficient management and analysis of this data.

Results

Life science ontologies evolve frequently to meet new requirements or to better reflect the current domain knowledge. The development and adaptation of large and complex ontologies is typically performed collaboratively by several curators. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between ontology versions. Such a Diff supports the synchronization of changes in collaborative curation, the adaptation of dependent data such as annotations, and ontology version management.

Summary: Life science ontologies substantially change over time to meet the requirements of their users and to include the newest domain knowledge. Thus, an important task is to know what has been modified between two versions of an ontology (diff ). This diff should contain all performed changes as compact and understandable as possible. We present CODEX (Complex Ontology Diff Explorer), a tool that allows determining semantic changes between two versions of an ontology which users can interactively analyze in multiple ways.

In den Lebenswissenschaften haben sich Ontologien in den letzten Jahren auf breiter Front durchgesetzt und sind in vielen Anwendungs- und Analyseszenarien kaum mehr wegzudenken. So etablierten sich nach und nach immer mehr domänenspezifische Ontologien, z.B. Anatomie-Ontologien oder Ontologien zur Beschreibung der Funktionen von Genen oder Proteinen. Da das Wissen in den Lebenswissenschaften sich rapide ändert und weiterentwickelt, müssen die entsprechenden Ontologien ständig angepasst und verändert werden, um einen möglichst aktuellen Wissensstand zu repräsentieren.

Ontologies such as taxonomies, product catalogs or
web directories are heavily used and hence evolve frequently
to meet new requirements or to better reflect the current instance
data of a domain. To effectively manage the evolution
of ontologies it is essential to identify the difference (Diff) between
two ontology versions. We propose a novel approach to
determine an expressive and invertible diff evolution mapping
between given versions of an ontology. Our approach utilizes
the result of a match operation to determine an evolution

This demonstration presents Galaxy, a schema manager that facilitates easy and correct data sharing among autonomous but related, evolving data sources. Galaxy reduces heterogeneity by helping database developers identify, reuse, customize, and advertise related schema components. The central idea is that as schemata are customized, Galaxy maintains a derivation graph, and exploits it for matching, data exchange, discovery, and multidatabase query over the “galaxy” of related data sources.

In this article, we propose a change detection approach in the context of an ontology evolution framework for OWL DL ontologies. The framework allows ontology engineers to request and apply changes to the ontology they manage. Furthermore, the framework assures that the ontology and its depending artifacts remain consistent after changes have been applied. Innovative is that the framework includes a change detection mechanism that allows generating automatically a detailed overview of changes that have occurred based on a set of change definitions.

Model differentiation techniques, which provide the capability to identify mappings and
differences between models, are essential to many model development and management
practices. There has been initial research toward model differentiation applied to UML diagrams,
but differentiation of domain-specific models has not been explored deeply in the modeling
community. Traditional modeling practice using the UML relies on a single fixed generalpurpose
language (i.e., all UML diagrams conform to a single metamodel). In contrast, Domain-

Detecting changes by comparing data snapshots is an important requirement for difference queries, active databases, and version and configuration management. In this paper we focus on detecting meaningful changes in hierarchically structured data, such as nested-object data. This problem is much more challenging than the corresponding one for relational or flat-file data.

Detecting and representing changes to data is important for active databases, data warehousing, view maintenance, and version and configuration management. Most previous work in change management has dealt with flat-file and relational data; we focus on hierarchically structured data. Since in many cases changes must be computed from old and new versions of the data, we define the hierarchical change detection problem as the problem of finding a \"minimum-cost edit script\" that transforms one data tree to another, and we present efficient algorithms for computing such an edit script.

This paper generalizes the work on the edit distance between
strings [6, 11, 13, 16, 20, 21, 25] and trees [19, 28, 29]. Various
kinds of constrained and
generalized edit distance on strings and trees have been developed
[1, 9, 10, 17, 27]. Our degree-2
distance, when applied to unordered trees, is a restricted form of
the constrained distance previously
reported in [27]. When applied to ordered trees, the degree-2
distance is a generalized
measure of the constrained distance originated from Selkow [17],
though our algorithm has the

The problems of finding a longest common subsequence of two sequences A and B and a shortest edit script
for transforming A into B have long been known to be dual problems. In this paper, they are shown to be
equivalent to finding a shortest/longest path in an edit graph. Using this perspective, a simple O(ND) time
and space algorithm is developed where N is the sum of the lengths of A and B and D is the size of the
minimum edit script for A and B. The algorithm performs well when differences are small (sequences are

As ontology development becomes a more ubiquitous and collaborative process, the developers face the problem of maintaining versions of ontologies akin to maintaining versions of software code in large software projects. Versioning systems for software code provide mechanisms for tracking versions, checking out versions for editing, comparing different versions, and so on. We can directly reuse many of these mechanisms for ontology versioning. However, version comparison for code is based on comparing text files—an approach that does not work for comparing ontologies.