From Questions to Effective Answers: On the Utility of Knowledge-Driven Querying Systems for Life Sciences Data

We compare two distinct approaches for querying data in the context of the life sciences. The first approach utilizes conventional databases to store the data and intuitive form-based interfaces to facilitate easy querying of the data. These interfaces could be seen as implementing a set of "pre-canned" queries commonly used by the life science researchers that we study. The second approach is based on semantic Web technologies and is knowledge (model) driven. It utilizes a large OWL ontology and same datasets as before but associated as RDF instances of the ontology concepts. An intuitive interface is provided that allows the formulation of RDF triples-based queries. Both these approaches are being used in parallel by a team of cell biologists in their daily research activities, with the objective of gradually replacing the conventional approach with the knowledge-driven one. This provides us with a valuable opportunity to compare and qualitatively evaluate the two approaches. We describe several benefits of the knowledge-driven approach in comparison to the traditional way of accessing data, and highlight a few limitations as well. We believe that our analysis not only explicitly highlights the specific benefits and limitations of semantic Web technologies in our context but also contributes toward effective ways of translating a question in a researcher's mind into precise computational queries with the intent of obtaining effective answers from the data. While researchers often assume the benefits of semantic Web technologies, we explicitly illustrate these in practice

Data Model and Query Constructs for Versatile Web Query Languages

As the Semantic Web is gaining momentum, the need for truly versatile query languages becomes increasingly apparent. A Web query language is called versatile if it can access in the same query program data in different formats (e.g. XML and RDF). Most query languages are not versatile: they have not been specifically designed to cope with both worlds, providing a uniform language and common constructs to query and transform data in various formats. Moreover, most of them do not provide a flexible data model that is powerful enough to naturally convey both Semantic Web data formats (especially RDF and Topic Maps) and XML. This article highlights challenges related to the data model and language constructs for querying both standard Web and Semantic Web data with an emphasis on facilitating sophisticated reasoning. It is shown that Xcerpt’s data model and querying constructs are particularly well-suited for the Semantic Web, but that some adjustments of the Xcerpt syntax allow for even more effective and natural querying of RDF and Topic Maps

Querying a regulatory model for compliant building design audit

The ingredients for an effective automated audit of a building design include a BIM model containing the design information, an electronic regulatory knowledge model, and a practical method of processing these computerised representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of standardisation. The current research project therefore focuses on using an open standard regulatory knowledge and BIM representations in conjunction with open standard executable compliant design workflows to automate the compliance audit process. This paper provides an overview of different approaches to access information from a regulatory model representation. The paper then describes the use of a purpose-built high-level domain specific query language to extract regulatory information as part of the effort to automate manual design procedures for compliance audit

Experiencing OptiqueVQS: A Multi-paradigm and Ontology-based Visual Query System for End Users

This is author's post-print version, published version available on http://link.springer.com/article/10.1007%2Fs10209-015-0404-5Data access in an enterprise setting is a determining factor for value creation processes, such as sense-making, decision-making, and intelligence analysis. Particularly, in an enterprise setting, intuitive data access tools that directly engage domain experts with data could substantially increase competitiveness and profitability. In this respect, the use of ontologies as a natural communication medium between end users and computers has emerged as a prominent approach. To this end, this article introduces a novel ontology-based visual query system, named OptiqueVQS, for end users. OptiqueVQS is built on a powerful and scalable data access platform and has a user-centric design supported by a widget-based flexible and extensible architecture allowing multiple coordinated representation and interaction paradigms to be employed. The results of a usability experiment performed with non-expert users suggest that OptiqueVQS provides a decent level of expressivity and high usability and hence is quite promising