Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Abstract

Value creation in an organisation is a timesensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users' technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users' understanding. However, on the one hand, today the most of world's enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity-known as Big Data. This article presents and argues for ontology-based visual query formulation for end users; discusses its feasibility in terms This research is funded by the Seventh Framework Program (FP7) of the European Commission under Grant Agreement 318338, "Optique".

Querying industrial stream-temporal data: An ontology-based visual approach1

“…Such an approach offers flexibility, modularity, and adaptability and enables us to combine multiple representation paradigms, such as forms, diagrams and icons, and interaction paradigms, such as schema navigation, range selection, and matching [12]. A multi-paradigm approach is important to support different user and task types as stated earlier [46,47].…”

“…Since OptiqueVQS is a data retrieval (DR) tool, where a single missing or irrelevant object implies a total failure contrary to information retrieval (IR) [61,5], effectiveness is measured in terms of a binary measure of success (i.e., correct/incorrect query) and efficiency is measured in terms of total time taken to formulate a query [46,47]. The results of the experiment are presented in Table 4.…”

“…P3 queries. The participants wished for the ability to combine multiple queries and connect concepts that are not directly linked (i.e., non-local navigation [39,47]). Participants also wanted attributes and attribute values in W3 to be filtered automatically with respect to previously selected constraints (including type refinement) -i.e., similar to faceted-search (cf.…”

“…In this context, ontology-based visual query formulation is gaining attention as ontologies come with certain benefits compared to visual query formulation over database schemas (cf. [47]). Firstly, ontologies, i.e., semantically rich conceptual domain models, present an intelligible solution by describing the domain of interest on a higher level of abstraction closer to the reality.…”

“…[47]). Therefore, OptiqueVQS [54,53,55], an ontology-based visual query system (VQS), has been extended for stream-temporal querying upon the requirements provided by Siemens AG 1 within an OBDA project called Optique 2 [18,19].…”