Sparklis is a Semantic Web tool that helps users explore SPARQL endpoints by guiding them in the interactive building of questions and answers, from simple ones to complex ones. It combines the ne-grained guidance of faceted search, most of the expressivity of SPARQL, and the readability of (controlled) natural languages. No endpoint-speci c con guration is necessary, and no knowledge of SPARQL and the data schema is required from users. This demonstration paper is a companion to the research paper [2].
Sparklis re-uses and generalizes the interaction model of Faceted Search
(FS) [
step, the system gives a set of suggestions to re ne the current selection, and
users only have to pick a suggestion according to their preferences. The
suggestions are speci c to the selection, and therefore support exploratory search [
To overcome expressivity limitations of FS and existing extensions for the
Semantic Web (e.g., gFacet [
To overcome the lack of readability of SPARQL queries for most users,
Sparklis queries and suggestions are verbalized in natural language so that SPARQL
queries never need to be shown to users. This makes Sparklis a kind of Natural
Language Interface (NLI), like PowerAqua [
highest-to-lowest/focus on a Writer/that has a nationality. Note that di erent insertion orderings are possible for a same question. Navigation buttons allow to move backward/forward in the construction history. A permalink to the current navigation state (endpoint+question) can be generated at any time. To switch to another SPARQL endpoint, it is enough to input its URL in the entry eld. The query focus is moved simply by clicking on di erent parts of the question, or on di erent table column headers. Every suggestion in the three lists, as well as every table cell, can be inserted or applied to the current focus by clicking it. The rst suggestion list contains entities (individuals and literals). The second list contains concepts (classes and properties). The third list contains logical connectives, sorting modi ers, and aggregation operators. Each suggestion list is equipped with an immediate-feedback ltering mechanism to quickly locate suggestions in long lists. With the rst list, lters can be inserted into the query with di erent lter operators listed in a drop-down menu (e.g., matches, higher or equal than, before). Questions and suggestions use indentation to disambiguate di erent possible groupings and improve readability, and syntax coloring to distinguish between the di erent kinds of words. 4
Portability. Sparklis conforms to the SPARQL standard, and requires no preprocessing or con guration to explore an endpoint. It entirely relies on the endpoint to discover data and its schema. The main limitation is that URIs are displayed through their local names, which is not always readable.
Expressivity. Sparklis covers many features of SPARQL: basic graph patterns (including cycles), basic lters, UNION, OPTIONAL, NOT EXISTS, SELECT, ORDER BY, multiple aggregations with GROUP BY. Almost all queries of the QALD2 challenge can be answered. Uncovered features are expressions, named graphs, nested queries, queries returning RDF graphs, and updates.
Scalability. Sparklis is responsive on the largest well-known endpoint: DBpedia. Among the 100 QALD-3 questions, half can be answered in less than 30 seconds (wall-clock time including user interaction and system computations). 5
The demonstration has shown to participants how QALD questions over DBpedia can be answered in a step-by-step process. Those questions cover various retrieval tasks: basic facts (Give me the homepage of Forbes), entity lists (Which rivers ow into a German lake?), counts (How many languages are spoken in Colombia?), optimums (Which of Tim Burton's lms had the highest budget?). More complex analytical question answering has also been demonstrated (Give me the total runtime, from highest to lowest, of lms per director and per country). Participants were also given the opportunity to explore any SPARQL endpoint of their choice.