Schema Evolution publication categorizer

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 practice, any database management system sometimes needs reorganization, that is, a change in some
aspect of the logical and/or physical arrangement of a database. In traditional practice, many types of reorganization
have required denying access to a database (taking the database offline) during reorganization.
Taking a database offline can be unacceptable for a highly available (24-hour) database, for example, a
database serving electronic commerce or armed forces, or for a very large database. A solution is to reorganize

In this paper we present a formal model for the support of temporal schema versions in object-oriented databases. Its definition is partially based on a generic (ODMG compatible) object model and partially introduces new concepts. The proposed model supports all the schema changes which are usually considered in the OODB literature, for which an operational semantics and a formal analysis of their correct behaviour is provided.

A schema mapping is a specification that describes how data from a source schema is to be mapped to a target schema. Once the data has been transferred from the source to the target, a natural question is whether one can undo the process and recover the initial data, or at least part of it. In fact, it would be desirable to find a reverse schema mapping from target to source that specifies how to bring the exchanged data back.

Information integration is a key challenge faced by all major organizations, business and governmental ones alike. Two research facets of this challenge that have received considerable attention in recent years are data exchange and data integration. The study of data exchange and data integration has been facilitated by the systematic use of schema mappings, which are high-level specifications that describe the relationship between two database schemas.

Schema evolution occurs frequently in practice. In this paper, we show that the entire schema does not need to be redesigned every time a change is made. We characterize the subschema affected by basic schema operations, which allows us to limit the scope of checking required and have a guarantee of the overall design being correct (safe changes). In addition, we characterize the effects on the actual relations of the addition and removal of constraints. In some cases, relations may have to be added to or removed from the database in order to preserve correctness.

It is natural for ontologies to evolve over time. These changes could be at structural and semantic levels. Due to changes to an ontology, its data instances may become invalid, and as a result, may become non-interpretable. In this paper, we address precisely this problem, validity of data instances due to ontological evolution. Towards this end, we make the following three novel contributions to the area of Semantic Web. First, we propose formal notions of structural validity and semantic validity of data instances, and then present approaches to ensure them.

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.

It has been a formidable task to achieve efficiency and scalability for the alignment between two massive, conceptually similar ontologies. Here we assume, an ontology is typically given in RDF (Resource Description Framework) or OWL (Web Ontology Language) and can be represented by a directed graph. A straightforward approach to the alignment of two ontologies entails an O(N2) computation by comparing every combination of pairs of nodes from given two ontologies, where N denotes the average number of nodes in each ontology.

Ontology alignment identifies semantically matching entities in different ontologies. Various ontology alignment strategies have been proposed; however, few systems have explored how to automatically combine multiple strategies to improve the matching effectiveness. This paper presents a dynamic multistrategy ontology alignment framework, named RiMOM. The key insight in this framework is that similarity characteristics between ontologies may vary widely.

Grid environments, providing distributed infrastructures, computing resources and data storage, usually show a high degree of heterogeneity and change in their metadata. We propose a platform for collaborative management and maintenance of common metadata for grids. As the conceptual foundation of this platform, a meta model is presented which distinguishes structured descriptions and classification structures that both are modifiable.

Background
Numerous ontologies have recently been developed in life sciences to support a consistent annotation of biological objects, such as genes or proteins. These ontologies underlie continuous changes which can impact existing annotations. Therefore, it is valuable for users of ontologies to study the stability of ontologies and to see how many and what kind of ontology changes occurred.

Results

In this paper, we discuss the problem of performing impact prediction for changes that occur in the schema/structure of the data warehouse sources. We abstract Extract-Transform-Load (ETL) activities as queries and sequences of views. ETL activities and its sources are uniformly modeled as a graph that is annotated with policies for the management of evolution events. Given a change at an element of the graph, our method detects the parts of the graph that are affected by this change and highlights the way they are tuned to respond to it.

We develop a new algorithm for clustering search results. Differently from many other clustering systems that have been recently proposed as a post-processing step forWeb search engines, our systemis not based on phrase analysis inside snippets, but instead uses latent semantic indexing on thewhole document content.Amain contribution of the paper is a novel strategy – called dynamic SVDclustering – to discover the optimal number of singular values to be used for clustering purposes.

Ontologies proliferate with the progress of the Semantic Web. Ontology matching is an important way of establishing interoperability between (Semantic) Web applications that use different but related ontologies. Due to their sizes and monolithic nature, large ontologies regarding real world domains bring a new challenge to the state of the art ontology matching technology. In this paper, we propose a divide-and-conquer approach to matching large ontologies.

Many XML-relational systems, i.e., the systems that use an XML schema as an external schema and a relational schema as an internal schema of the data application representation level, require modifications of the data schemas in the course of time. Schema evolution is one of the ways to support schema modifications for the application at the DBMS level. A number of schema evolution support systems for different data models have been suggested.

One of the most important challenges that software engineers (designers, developers) still have to face in their everyday work is the evolution of working database systems. As a step for the solution of this problem in this paper we propose MeDEA, which stands for Metamodel-based Database Evolution Architecture. MeDEA is a generic evolution architecture that allows us to maintain the traceability between the different artifacts involved in any database development process. MeDEA is generic in the sense that it is independent of the particular modeling techniques being used.

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 is a basic operation of data integration, and several tools for automating it have been proposed and evaluated in the database community. Research in this area reveals that there is no single schema matcher that is guaranteed to succeed in finding a good mapping for all possible domains and, thus, an ensemble of schema matchers should be considered. In this paper, we introduce schema metamatching, a general framework for composing an arbitrary ensemble of schema matchers and generating a list of best ranked schema mappings.

We have recently mapped the Gene Ontology (GO), developed by the Gene Ontology Consortium, into the National Library of Medicine’s Unified Medical Language System (UMLS). GO has been developed for the purpose of annotating gene products in genome databases, and the UMLS has been developed as a framework for integrating large numbers of disparate terminologies, primarily for the purpose of providing better access to biomedical information sources. The mapping of GO to UMLS highlighted issues in both terminology systems.

Research on ontologies is becoming widespread in the biomedical informatics community. At the same time, it has become apparent that the challenges of properly constructing and maintaining ontologies have proven more difficult than many workers in the field initially expected. Discovering general, feasible methods has thus become a central activity for many of those hoping to reap the benefits of ontologies. This paper reviews current methods in the construction, maintenance, alignment, and evaluation of ontologies.

This paper describes how we used generic schema matching algorithms to align the Foundational Model of Anatomy (FMA)and the GALEN Common Reference Model (CRM), two largemodels of human anatomy. We summarize the generic schema
matching algorithms we used to identify correspondences. We present sample results that highlight the similarities and differences between the FMA and the CRM. We also identify uses of aggregation, transitivity, and reification, for which generic schema matching fails to produce an accurate mapping and
present manually constructed solutions for them.