Semantic Storage: Overview and Assessment

The Semantic Web has a great deal of momentum behind it. The promise of a ‘better web’, where information is given well defined meaning and computers are better able to work with it has captured the imagination of a significant number of people, particularly in academia. Language standards such as RDF and OWL have appeared with remarkable speed, and development continues apace. To back up this development, there is a requirement for ‘semantic databases’, where this data can be conveniently stored, operated upon, and retrieved. These already exist in the form of triple stores, but do not yet fulfil all the requirements that may be made of them, particularly in the area of performing inference using OWL. This paper analyses the current stores along with forthcoming technology, and finds that it is unlikely that a combination of speed, scalability, and complex inferencing will be practical in the immediate future. It concludes by suggesting alternative development routes

User Defined Types and Nested Tables in Object Relational Databases

Bernadette Byrne, Mary Garvey, ‘User Defined Types and Nested Tables in Object Relational Databases’, paper presented at the United Kingdom Academy for Information Systems 2006: Putting Theory into Practice, Cheltenham, UK, 5-7 June, 2006.There has been much research and work into incorporating objects into databases with a number of object databases being developed in the 1980s and 1990s. During the 1990s the concept of object relational databases became popular, with object extensions to the relational model. As a result, several relational databases have added such extensions. There has been little in the way of formal evaluation of object relational extensions to commercial database systems. In this work an airline flight logging system, a real-world database application, was taken and a database developed using a regular relational database and again using object relational extensions, allowing the evaluation of the relational extensions.Peer reviewe

Knowledge Rich Natural Language Queries over Structured Biological Databases

Increasingly, keyword, natural language and NoSQL queries are being used for information retrieval from traditional as well as non-traditional databases such as web, document, image, GIS, legal, and health databases. While their popularity are undeniable for obvious reasons, their engineering is far from simple. In most part, semantics and intent preserving mapping of a well understood natural language query expressed over a structured database schema to a structured query language is still a difficult task, and research to tame the complexity is intense. In this paper, we propose a multi-level knowledge-based middleware to facilitate such mappings that separate the conceptual level from the physical level. We augment these multi-level abstractions with a concept reasoner and a query strategy engine to dynamically link arbitrary natural language querying to well defined structured queries. We demonstrate the feasibility of our approach by presenting a Datalog based prototype system, called BioSmart, that can compute responses to arbitrary natural language queries over arbitrary databases once a syntactic classification of the natural language query is made