Schema Evolution publication categorizer

In this paper, we introduce a general overview of Falcon-AO: a practical ontology matching system with acceptable to good performance and a number of remarkable features. Furthermore, Falcon-AO is one of the best systems in all kinds of tests in the latest three years’ OAEI campaigns. Falcon-AO is written in Java, and is open source.

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.

We present the AgreementMaker system for matching real-world schemas and ontologies, which may consist of hundreds or even thousands of concepts. The end users of the system are sophisticated domain experts whose needs have driven the design and implementation of the system: they require a responsive, powerful, and extensible framework to perform, evaluate, and compare matching methods. The system comprises a wide range of matching methods addressing dierent levels of granularity of the components being matched (conceptual vs.

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

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.

Research has investigated mappings among data sources under two perspectives. On one side, there are studies of practical tools for schema mapping generation; these focus on algorithms to generate mappings based on visual specifications provided by users. On the other side, we have theoretical researches about data exchange. These study how to generate a solution -- i.e., a target instance -- given a set of mappings usually specified as tuple generating dependencies.

System interoperability is a well known issue, especially for heterogeneous information systems, where ontology-based representations may support automatic and user-transparent integration. In this paper we present X-SOM: an ontology mapping and integration tool. The contribution of our tool is a modular and extensible architecture that automatically combines several matching techniques by means of a neural network, performing also ontology debugging to avoid inconsistencies.

Information seeking is the process in which human beings recourse to information resources in order to increase their level of knowledge with respect to their goals. In this paper we offer a methodology for automating the evolution of ontologies and share the results of our experiments in supporting a user in seeking information using interactive systems. The main conclusion of our experiments is that if one narrows down the scope of the domain, ontologies can be extracted with a very high level of precision (more than 90% in some cases).

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.

Due to its extensive potential applications, model management has attracted many research interests and gained great progress. To provide easy-to-use interfaces, we have proposed a graph transformation-based model management approach that provides intuitive interfaces for manipulation of graphical data models. The approach consists of two levels of graphical operators: low-level customizable operators and high-level generic operators, both of which consist of a set of graph transformation rules. Users need to program or tune the low-level operators for desirable results.

This technical report describes how IQL queries are processed in the AutoMed heterogeneous data integration system, and also how data migration can be supported.
We start with an outline of the IQL language in Section 2. We then consider in Section 3 an abstract representation of this textual IQL and describe the ASG class that implements this abstract representation.

While the contents of databases can be easily changed, their organization is typically extremely rigid. Some databases relax the rigidity of database organization somewhat by supporting simple changes to individual schemas. As described in this paper, OTGen supports not only more complex schema changes, but also database reorganization. A database administrator uses a declarative notation to describe mappings between objects created with old versions of schemas and their corresponding representations using new versions.

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.

(Semi-)automatic mapping — also called (semi-)automatic alignment
— of ontologies is a core task to achieve interoperability when two agents or
services use different ontologies. In the existing literature, the focus has so far
been on improving the quality of mapping results. We here consider QOM, Quick
Ontology Mapping, as a way to trade off between effectiveness (i.e. quality)
and efficiency of the mapping generation algorithms. We show that QOM has
lower run-time complexity than existing prominent approaches. Then, we show

Ontology alignment is a prerequisite in order to allow for interoperation between different ontologies and many alignment strategies have been proposed to facilitate the alignment task by (semi-)automatic means. Due to the complexity of the alignment task, manually defined methods for (semi-)automatic alignment rarely constitute an optimal configuration of substrategies from which they have been built. In fact, scrutinizing current ontology alignment methods, one may recognize that most are not optimized for given ontologies.