Integration with Ontologies

One of today’s hottest IT topics is integration, as bringing together information from different sources and structures is not completely solved. The approach outlined here wants to illustrate how ontologies [Gr93] could help to support the integration process

A Semantic Grid Oriented to E-Tourism

With increasing complexity of tourism business models and tasks, there is a clear need of the next generation e-Tourism infrastructure to support flexible automation, integration, computation, storage, and collaboration. Currently several enabling technologies such as semantic Web, Web service, agent and grid computing have been applied in the different e-Tourism applications, however there is no a unified framework to be able to integrate all of them. So this paper presents a promising e-Tourism framework based on emerging semantic grid, in which a number of key design issues are discussed including architecture, ontologies structure, semantic reconciliation, service and resource discovery, role based authorization and intelligent agent. The paper finally provides the implementation of the framework.Comment: 12 PAGES, 7 Figure

Mappings for the Semantic Web

Mappings usually relate two similar knowledge aware resources. Mapping examples abound in thesauri, databases, and ontologies. Additionally, mapping systems can relate two different knowledge resources, such as databases and ontologies. All these mappings are operationally different and are sometimes named differently— for example, correspondences, semantic bridges, transformations, semantic relations, functions, conversions, and domain-method relation

Comparing human and automatic thesaurus mapping approaches in the agricultural domain

Knowledge organization systems (KOS), like thesauri and other controlled vocabularies, are used to provide subject access to information systems across the web. Due to the heterogeneity of these systems, mapping between vocabularies becomes crucial for retrieving relevant information. However, mapping thesauri is a laborious task, and thus big efforts are being made to automate the mapping process. This paper examines two mapping approaches involving the agricultural thesaurus AGROVOC, one machine-created and one human created. We are addressing the basic question "What are the pros and cons of human and automatic mapping and how can they complement each other?" By pointing out the difficulties in specific cases or groups of cases and grouping the sample into simple and difficult types of mappings, we show the limitations of current automatic methods and come up with some basic recommendations on what approach to use when.Comment: 10 pages, Int'l Conf. on Dublin Core and Metadata Applications 200

Descubrimiento automático de mappings

Dentro de la problemática de la integración de información, los elementos claves son los mappings, unidades que relacionan las diferentes representaciones (ontologías, bases de datos, redes semánticas, etc. ). Y dentro de toda la colección de operaciones que los mappings llevan asociadas en todo su ciclo de vida, el cuello de botella se encuentra en su descubrimiento. Con este trabajo doctoral se pretende dar un paso más en este campo realizando un nuevo modelo de mappings lo menos limitado, y a la vez funcional, posible a diferentes representaciones y lo más versátil para la combinación de técnicas de descubrimiento, de toda índole, ya existentes y de nuevo cuño de manera automática, basándose en un sistema experto previamente construido a costa de evaluaciones sobre casos de uso reales

Gap analysis of ontology mapping tools and techniques

Mapping between ontologies provides a way to overcome any dissimilarities in the terminologies used in two ontologies. Some tools and techniques to map ontologies are available with some semi-automatic mapping capabilities. These tools are employed to join the similar concepts in two ontologies and overcome the possible mismatches.Several types of mismatches have been identified by researchers and certain overlaps can easily be seen in their description. Analysis of the mapping tools and techniques through a mismatches framework reveals that most of the tools and techniques just target the explication side of the concepts in ontologies and a very few of them opt for the conceptualization mismatches. Research therefore needs to be done in the area of detecting and overcoming conceptualization mismatches that may occur during the process of mapping. The automation and reliability of these tools are important because they directly affect the interoperatbility between different knowledge sources

Semantic mappings: out of ontology world limits

Mappings usually relate two similar knowledge representations. Thus, we can find many examples of mappings amid thesauri, databases, ontologies (domain ontologies, top-level and domain ontologies, PSM (Problem Solving Method) and domain ontologies, linguistic and domain ontologies); additionally, we can frequently find systems with mappings that relate two different knowledge representations, for instance, databases and ontologies. All these mappings are operationally different ,and are also named differently (mappings, correspondences, semantic bridges, transformations, semantic relations, functions, conversions, domain-PSM relations), but is there a single definition for these concepts? Can we find common characteristics? This paper analyzes the existing definitions and representation of the term “mapping” (and related terms) in the ontology world and its semantic neighborhood and proposes a new definition and representation of “mapping” for the Semantic Web field

Ontology alignment through argumentation

Currently, the majority of matchers are able to establish simple correspondences between entities, but are not able to provide complex alignments. Furthermore, the resulting alignments do not contain additional information on how they were extracted and formed. Not only it becomes hard to debug the alignment results, but it is also difficult to justify correspondences. We propose a method to generate complex ontology alignments that captures the semantics of matching algorithms and human-oriented ontology alignment definition processes. Through these semantics, arguments that provide an abstraction over the specificities of the alignment process are generated and used by agents to share, negotiate and combine correspondences. After the negotiation process, the resulting arguments and their relations can be visualized by humans in order to debug and understand the given correspondences.(undefined

Framework for automatic generation of ontology mappings

Some of the most outstanding problems in Computer Science (e.g. access to heterogeneous information sources, use of different e-commerce standards, ontology translation, etc.) are often approached through the identification of ontology mappings. A manual mapping generation slows down, or even makes unfeasible, the solution of particular cases of the aforementioned problems via ontology mappings. Some algorithms and formal models for partial tasks of automatic generation of mappings have been proposed. However, an integrated framework to solve this problem is still missing. In this paper, we present a framework for automatic ontology mapping generation, and a partial implementation of it. Our proposal is that this integrated vision can guide, not only our future work, but also the future work of other researchers. In the implementation carried out, we have built a mapping ontology with knowledge on ontology mappings