Rosario A. Uceda-Sosa

From Ontology to Relational Databases

The semantic web envisions a World Wide Web in which data is described with rich semantics and applications can pose complex queries. Ontologies, a cornerstone of the semantic web, have gained wide popularity as a model of information in a given domain that can be used for many purposes, including enterprise integration, database design, information retrieval and information interchange on the World Wide Web. Much of the current focus on ontologies has been on the development of languages such as DAML+OIL and OWL that enable the creation of ontologies and provide extensive semantics for Web data, and on answering intensional queries, that is, queries about the structure of an ontology. However, it is almost certain that the many of the semantic web queries will be extensional and to flourish, the semantic web will need to accommodate the huge amounts of existing data that is described by the ontologies and the applications that operate on them. Given the established record of relational databases to store and query large amounts of data, in this paper we present a set of techniques to provide a lossless mapping of an OWL ontology to a relational schema and the corresponding instances to data. We present preliminary experiments that compare the efficiency of the mapping techniques in terms of query performance.

Building a highly consumable semantic model for smarter cities

Despite enormous progress made in reconciling and simplifying semantic standards, industry-strength models are still few. In this work, we describe our challenges and approach in building semantic models for consumption in assembling large IT solutions in cyber-physical (Smarter Cities) domains. The result, called SCRIBE, is a large-scale semantic model for Smarter Cities based on data gathered from cities worldwide. We will discuss the design tradeoffs, designer/user experience, and some of the pragmatic challenges related to a model that integrates large, complex, heterogeneous data sources.

CLOVE: A Framework to Design Ontology Views

The management and exchange of knowledge in the Internet has become the cornerstone of technological and commercial progress. In this fast-paced environment, the competitive advantage belongs to those businesses and individuals that can leverage the unprecedented richness of web information to define business partnerships, to reach potential customers and to accommodate the needs of these customers promptly and flexibly. The Semantic Web vision is to provide a standard information infrastructure that will enable intelligent applications to automatically or semi-automatically carry out the publication, the searching, and the integration of information on the Web. This is to be accomplished by semantically annotating data and by using standard inferencing mechanisms on this data. This annotation would allow applications to understand, say, dates and time intervals regardless of their syntactic representation. For example, in the e-business context, an online catalog application could include the expected delivery date of a product based on the schedules of the supplier, the shipping times of the delivery company and the address of the customer. The infrastructure envisioned by the Semantic Web would guarantee that this can be done automatically by integrating the information of the online catalog, the supplier and the delivery company. No changes to the online catalog application would be necessary when suppliers and delivery companies change. No syntactic mapping of metadata will be necessary between the three data repositories.

Integrated solution engineering

Integrated Solution Engineering helps developers manage software complexity by offering semi-automated support for capturing and mining relationships among artifacts and/or developer tasks at different stages of the software lifecycle, and by aiding developers in the use and management of the information contained in these relationships. The use of these relationships can facilitate traceability, propagation of change, change impact analysis, evolution, and comprehension.

A knowledge and reasoning toolkit for cognitive applications

This paper presents a knowledge and reasoning toolkit for developing cognitive applications which have significant requirements for managing structured and semi-structured data. Our system provides enhanced querying and reasoning capabilities along with natural language processing support and the ability to automatically extract data from PDF documents. We also have the capability to manage ontologies in a user-friendly way. Our system is implemented as a set of Web services, and we provide enhanced clients to allow applications to easily access our knowledge and reasoning toolkit.

Models for sustainability

This paper describes a semantic model for urban sustainability and its implementation as an ontology. The ontology functions as an integration layer for the heterogeneous data sources used to calculate the set of sustainability indicators. These indicators are integrated in the semantic model so they can be indexed by geographical area, semantic concept, or key sustainability category, and they can be accessed through the ontology vocabulary. Users can explore and query both the model and the instance data mapped to it. At a more abstract level, this approach can help with understanding the potentially contradicting effects of changes in the values of the parameters that are modeled, running alternative scenarios and comparing options, and ultimately, selecting solutions that optimize the performance metrics of interest.