Schema Evolution publication categorizer

This paper presents the LSLink (or Life Science Link) methodology that provides users with a set of tools to explore the rich Web of interconnected and annotated objects in multiple repositories, and to identify meaningful associations. Consider a physical link between objects in two repositories, where each of the objects is annotated with controlled vocabulary (CV) terms from two ontologies. Using a set of LSLink instances generated from a background dataset of knowledge we identify associations between pairs of CV terms that are potentially significant and may lead to new knowledge.

Ontologies have become very popular in life sciences and other domains. They mostly undergo continuous changes and new ontology versions are frequently released. However, current analysis studies do not consider the ontology changes reflected in different versions but typically limit themselves to a specific ontology version which may quickly become obsolete. To allow applications easy access to different ontology versions we propose a central and uniform management of the versions of different biomedical ontologies.

Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in
data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.

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.

We address the problem of unsupervised matching of schema information from a large number of data sources into the

Schema mappings are high-level specifications that describe the relationship between two database schemas. They are an important tool in several areas of database research, notably in data integration and data exchange. However, a concrete theory of schema mapping optimization including the formulation of optimality criteria

The inversion of schema mappings has been identified as one of the fundamental operators for the development of a
general framework for metadata management. In fact, during the last years three alternative notions of inversion for

Ontology matching has been widely studied. However, the resulting ontology mappings can be rather unstable when the participating ontologies or utilized secondary sources (e.g., instance sources, thesauri) evolve. We propose an evolution-based approach for assessing ontology mappings by annotating their correspondences by information about similarity values for past ontology versions. These annotations allow us to assess the stability of correspondences over time and they can thus be used to determine better and more robust ontology mappings.

Data reverse engineering (DRE) is a complex and costly process that requires a deep understanding of large dataintensive software systems. This process can be made easier with the use of program understanding methods and tools. In this paper, we focus on the program slicing technique and we show how it can be adapted to support DRE. We present a DML-independent SDG construction approach involving the analysis of database operations as a first stage. We describe a tool based upon this approach and we report on two industrial DRE projects.

The paper adresses the problem of consistency preservation in data intensive applications evolution. When the database structure evolves, the application programs must be changed to interface with the new schema. The latter modification can prove very complex, error prone and time consuming. We describe a comprehensive transformation/generative approach according to which automated program transformation can be derived from schema transformation.

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.

The entities described in the Gene Ontology, (i.e., molecular functions, cellular components and biological processes), often make reference (in their names) to other entities, either from GO or from other ontologies, such as ontologies of chemical entities, cell types and organisms. We developed a method for mapping terms from the Open Biomedical Ontology (OBO) family to GO. We show that 55% of the 17,250GO terms include in their names the name of some chemicalentity (ChEBI). Our findings are consistent with that of other studies.

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.

Objective. The objective of this experiment is to develop methods for aligning two representations of anatomy (the Foundational Model of Anatomy and GALEN) at the lexical and structural level.
Methods. The alignment consists of the following four steps: 1) acquiring terms, 2) identifying anchors (i.e., shared concepts) lexically, 3) acquiring explicit and implicit semantic relations, and 4) identifying anchors structurally.

The escalating complexity of software and system models is making it difficult to
rapidly explore the effects of a design decision.Automating such exploration with
model transformation and aspect-oriented techniques can improve both productivity
and model quality.

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-

In recent years many biomedical ontologies have been developed
and many of these ontologies contain overlapping information.
To be able to use multiple ontologies they have to be aligned. In this
paper we propose strategies for aligning ontologies based on life science
literature. We propose a basic algorithm as well as extensions that take
the structure of the ontologies into account. We evaluate the strategies
and compare them with strategies implemented in the alignment system
SAMBO. We also evaluate the combination of the proposed strategies
and the SAMBO strategies.

In today's Web, many functionality-wise similar Web services are offered through heterogeneous interfaces (operation
definitions) and business protocols (ordering constraints defined on legal operation invocation sequences). The typical
approach to enable interoperation in such a heterogeneous
setting is through developing adapters. There have been approaches for classifying possible mismatches between service
interfaces and business protocols to facilitate adapter development. However, the hard job is that of identifying, given
two service specications, the actual mismatches between

In distributed geospatial applications with heterogeneous databases, an ontology-driven approach to data integration relies on the alignment of the concepts of a global ontology that describe the domain, with the concepts of the ontologies that describe the data in the distributed databases. Once the alignment between the global ontology and each distributed ontology is established, agreements that encode a variety of mappings between concepts are derived. In this way, users can potentially query hundreds of geospatial databases using a single query.

Ontologies are viewed as the silver bullet for many applications: database integration, peer-to-peer systems, e-commerce, semantic web services, social networks and more. However, in open or evolving systems, different parties can adopt different ontologies. Instead of reducing heterogeneity, this raises heterogeneity problems to a higher level.

System evolution most often implies the integration of legacy components, such as databases, with newly developed ones, leading to mixed architectures that suffer from severe heterogeneity problems. For instance, incorporating a new program in a legacy database application can create an integrity mismatch, since the database model and the program data view can be quite different (e.g. standard file model versus OO model).

While recent data management technologies, such as object oriented techniques, address the problem of database schema evolution, standard information systems currently in use raise challenging evolution problems. This paper examines database evolution from the developer point of view. It shows how requirements changes are propagated to database schemas, to data and to programs through a general strategy. This strategy requires the documentation of database design. When absent, such documentation has to be rebuilt through reverse engineering techniques.

Model transformations are at the heart of model driven engineering (MDE) and can be used in many different application scenarios. For instance, model transformations are used to integrate very large models. As a consequence, they are becoming more and more complex. However, these transformations are still developed manually. Several code patterns are implemented repetitively, increasing the probability of programming errors and reducing code reusability. There is not yet a complete solution that automates the development of model transformations.

Executable schema mappings between XML schemas are essential
to support numerous data management tasks such as data exchange,
data integration and schema evolution. The novelty of this paper
consists in a method for automatic generation of automappings (automorphisms)
from key constraints and value dependencies over XML
schemas, and designing algebraic operations on mappings and schemas
represented by automappings. During execution of mappings some missing
or incomplete data may be inferred. A well-defined executable semantics

View merging, also called view integration, is a key problem in conceptual modeling. Large models are often constructed and accessed by manipulating individual views, but it is important to be able to consolidate a set of views to gain a unified perspective, to understand interactions between views, or to perform various types of analysis. View merging is complicated by incompleteness and inconsistency: Stakeholders often have varying degrees of confidence about their statements.