NL-Graphs: A Hybrid Approach toward Interactively Querying Semantic Data

Abstract

Abstract. A variety of query approaches have been proposed by the semantic web community to explore and query semantic data. Each was developed for a specific task and employed its own interaction mechanism; each query mechanism has its own set of advantages and drawbacks. Most semantic web search systems employ only one approach, thus being unable to exploit the benefits of alternative approaches. Motivated by a usability and interactivity perspective, we propose to combine two query approaches (graph-based and natural language) as a hybrid query approach. In this paper, we present NL-Graphs which aims to exploit the strengths of both approaches, while ameliorating their weaknesses. NL-Graphs was conceptualised and developed from observations, and lessons learned, in several evaluations with expert and casual users. The results of evaluating our approach with expert and casual users on a large semantic dataset are very encouraging; both types of users were highly satisfied and could effortlessly use the hybrid approach to formulate and answer queries. Indeed, success rates showed they were able to successfully answer all the evaluation questions.

Ontological databases with faceted queries

“…However, such expressive languages as SPARQL are not well-suited for end-users. Thus, we observe attempts to develop interactive graphic-oriented ontology query languages such as, for example, ViziQuer [78], SPARQLGraph [62], OptiqueVQS [66,73], SEWASIE [10], OntoVQL [31], NL-Graphs [27], K-search [12], which present ontology views combined with form-based query entry interfaces. A promising alternative to the aforementioned languages is approaches based on faceted search resulting in faceted queries [7,70].…”

Survey on challenges of Question Answering in the Semantic Web

“…Another extension of TBSL is ISOFT [108], which uses explicit semantic analysis to help bridging the lexical gap. NL-Graphs [47] combines SQA with an interactive visualization of the graph of triple patterns in the query which is close to the SPARQL query structure yet still intuitive to the user. Users that find errors in the query structure can either reformulate the query or modify the query graph.…”