Usability of Keyword-Driven Schema-Agnostic Search

Abstract

Abstract. The increasing amount of data on the Web bears potential for addressing complex information needs more effectively. Instead of keyword search and browsing along links between results, users can specify the needs in terms of complex queries and obtain precise answers right away. However, users might not always know the query language and more importantly, the schema underlying the data. Motivated by the burden facing the data Web search users in specifying complex information needs, we identify a particular class of search approaches that follow a paradigm that we refer to as schema-agnostic. Common to these search approaches is that no knowledge about the schema is required to specify complex information needs. We have conducted a systematic study of four popular approaches: (1) simple keyword search, (2) faceted search, (3) result completion, which is based on computing complex answers as candidate results for user provided keywords, and (4) query completion, which is based on computing structured queries as candidate interpretations of user provided keywords. We study these approaches from a process-oriented view to derive the main conceptual steps required for addressing complex information needs. Then, we perform an experimental study based on established conduct of a task-based evaluation to assess the effectiveness, efficiency and usability.

Generating SPARQL queries using templates

“…Keyword search on relational and XML data Currently, keyword-based search is the most popular and convenient way for finding information on the Web [33]. Although the field of IR and database approximately initiated at the same time, keyword-based search was studied mainly in the area of IR; because users of databases are mainly experts who have knowledge about a specific language to interact with a database system.…”

DC Proposal: Automatically Transforming Keyword Queries to SPARQL on Large-Scale Knowledge Bases

“…The successful experience of keyword-based search in document retrieval and the satisfactory research results about the usability of this paradigm [23] are convincing reasons for using the keyword search paradigm to the Semantic Web. Document retrieval approaches use probabilistic foundations combining with ranking algorithms for selecting documents containing those keywords which practically works well.…”

Semantic Technologies for Enterprise Cloud Management

“…Matching of keywords happens on both instance and schema level, which means that a query is suggested if all the keywords match on elements of a subgraph, no matter if these elements are RDF entities or links between these entities. We omit the technical details here, referring the interested reader to [12] for the theoretical background of this search approach.…”

“…While the SPARQL query language is very expressive (i.e., relationally complete [3]), formulating SPARQL queries requires knowledge about the query language and the schema of the underlying data. To equip users with the high precision of structured search without having detailed knowledge about the schema, we support a variety of means to perform ad hoc queries in a schema-agnostic way [12], as dicussed in the following.…”