=Paper=
{{Paper
|id=None
|storemode=property
|title=SPARKLIS: a SPARQL Endpoint Explorer for Expressive Question Answering
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_39.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/Ferre14a
}}
==SPARKLIS: a SPARQL Endpoint Explorer for Expressive Question Answering==
https://ceur-ws.org/Vol-1272/paper_39.pdf
Sparklis: a SPARQL Endpoint Explorer
for Expressive Question Answering
Sébastien Ferré
IRISA, Université de Rennes 1
Campus de Beaulieu, 35042 Rennes cedex, France
Email: ferre@irisa.fr
Abstract. 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 com-
bines the fine-grained guidance of faceted search, most of the expressiv-
ity of SPARQL, and the readability of (controlled) natural languages.
No endpoint-specific configuration is necessary, and no knowledge of
SPARQL and the data schema is required from users. This demonstra-
tion paper is a companion to the research paper [2].
1 Motivation
A wealth of semantic data is accessible through SPARQL endpoints. DBpedia
alone contains several billions of triples covering all sorts of topics (e.g., people,
places, buildings, species, films, books). Although different endpoints may use
different vocabularies and ontologies, they all share a common interface to access
and retrieve semantic data: the SPARQL query language. In addition to being a
widely-adopted W3C standard, the advantages of SPARQL are its expressivity,
especially since version 1.1, and its scalability for large RDF stores thanks to
highly optimized SPARQL engines (e.g., Virtuoso, Jena TDB). Its main draw-
back is that writing SPARQL queries is a tedious and error-prone task, and is
largely unaccessible to most potential users of semantic data.
Our motivation in developing Sparklis1 , shared by many other developers
of Semantic Web tools and applications, is to unleash access to semantic data by
making it easier to define and send SPARQL queries to endpoints. The novelty
of Sparklis is to combine in an integrated fashion different search paradigms:
Faceted Search (FS), Query Builders (QB), and Natural Language Interfaces
(NLI). That integration is the key to reconcile properties for which there is
generally a trade-off in existing systems: user guidance, expressivity, readability
of queries, scalability, and portability to different endpoints [2].
2 Principles
Sparklis re-uses and generalizes the interaction model of Faceted Search
(FS) [8], where users are guided step-by-step in the selection of items. At each
1
Online at http://www.irisa.fr/LIS/ferre/sparklis/osparklis.html
step, the system gives a set of suggestions to refine the current selection, and
users only have to pick a suggestion according to their preferences. The sugges-
tions are specific to the selection, and therefore support exploratory search [7]
by providing overview and feedback during the search process.
To overcome expressivity limitations of FS and existing extensions for the Se-
mantic Web (e.g., gFacet [4], VisiNav [3], SemFacet [1]), we have generalized it
to Query-based Faceted Search (QFS), where the selection of items is replaced
by a structured query. The latter is built step-by-step through the successive
choices of the user. This makes Sparklis a kind of Query Builder (QB), like
SemanticCrystal [5]. QBs have the advantage to allow for a high expressivity
while assisting users about syntax, e.g. avoiding syntax errors, listing eligible
constructs. However, the FS-based guidance of Sparklis is more fine-grained
than in QBs. Sparklis avoids vocabulary errors by retrieving the URIs and
literals right from the SPARQL endpoint. It needs not be configured for a par-
ticular dataset, and dynamically discovers the data schema. In fact, Sparklis
only allows the building of queries that do return results, preventing users to fall
on empty results. That is because system suggestions are computed for the in-
dividual results, not for their common class. In fact, Sparklis is as much about
building answers as about building questions.
To overcome the lack of readability of SPARQL queries for most users, Spark-
lis 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 [6]. The important difference is that
questions are built through successive user choices in Sparklis instead of be-
ing freely input in NLIs. Sparklis interaction makes question formulation more
constrained, slower, and less spontaneous, but it provides guidance and safeness
with intermediate answers and suggestions at each step. Moreover, it avoids
the hard problem of NL understanding: i.e., ambiguities, out-of-scope questions.
A few NLI systems, like Ginseng [5], are based on a controlled NL and auto-
completion to suggest the next words in a question. However, their suggestions
are not fine-grained like with FS, and less flexible because they only apply to the
end of the question. In Sparklis, questions form complete sentences at any step
of the search; and suggestions are not words but meaningful phrases (e.g., that
has a director), and can be inserted at any position in the current question.
3 User Interface and Interaction
Figure 1 is a Sparklis screenshot taken during an exploration of book
writers in DBpedia. From top to bottom, the user interface contains (1)
navigation buttons and the endpoint URL, (2) the current question and
the current focus as a subphrase (highlighted in green), (3) three lists
of suggestions for insertion at the focus, and (4) the table of answers.
The shown question and answer have been built in 10 steps (8 inser-
tions and 2 focus moves): a Writer/that has a birthDate/after 1800/focus
on a Writer/that is the author of something/a Book/a number of/the
Fig. 1. Sparklis screenshot: a list of writers with their birth date (after 1800), nation-
ality, and (decreasing) number of written books. Current focus is on writer’s nationality.
highest-to-lowest/focus on a Writer/that has a nationality. Note that
different insertion orderings are possible for a same question. Navigation but-
tons 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 field. The query focus is moved simply by clicking on different parts of
the question, or on different 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 first suggestion list contains entities (individuals and liter-
als). The second list contains concepts (classes and properties). The third list
contains logical connectives, sorting modifiers, and aggregation operators. Each
suggestion list is equipped with an immediate-feedback filtering mechanism to
quickly locate suggestions in long lists. With the first list, filters can be inserted
into the query with different filter operators listed in a drop-down menu (e.g.,
matches, higher or equal than, before). Questions and suggestions use in-
dentation to disambiguate different possible groupings and improve readability,
and syntax coloring to distinguish between the different kinds of words.
4 Performances and Limitations
Portability. Sparklis conforms to the SPARQL standard, and requires no pre-
processing or configuration to explore an endpoint. It entirely relies on the end-
point 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 pat-
terns (including cycles), basic filters, 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: DB-
pedia. 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 Demonstration
The demonstration has shown to participants how QALD questions over DB-
pedia 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 flow into a German lake?), counts (How many languages are spoken in
Colombia?), optimums (Which of Tim Burton’s films had the highest budget?).
More complex analytical question answering has also been demonstrated (Give
me the total runtime, from highest to lowest, of films per director and per coun-
try). Participants were also given the opportunity to explore any SPARQL end-
point of their choice.
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2
http://greententacle.techfak.uni-bielefeld.de/~cunger/qald/