Considerable effort has been made to improve the functionality and usability of SPARQL search engines. However, explaining missing items in the results of SPARQL queries or the so-called why-not questions remains in its infancy. Existing explanation models cannot be trivially extended to SPARQL queries because of the SPARQL-specific features in the data model and query operations. In this demonstration, we present a novel explanation system, ANNA (Answering why-Not questioNs for spArql), to explain why-not questions using a divide-and-conquer strategy. ANNA can visualize explanations to help users revise their initial queries to make the expected result-items presented. Experimental results on DBpedia prove that ANNA can generate high-quality explanations within a reasonable amount of time.
This situation illustrates the significance of our system, namely, Answering
whyNot questioNs for spArql (ANNA2). Many explanation models have been created to
answer why-not questions for relational databases, social image searches and
topqueries [
A SPARQL query consists of triple patterns and operators (FILTER, DISTINCT, MINUS, LIMIT, ORDER BY, etc.). The evaluation of over the RDF dataset can be divided into two levels, namely, basic graph pattern (BGP) level and operator level. At the BGP level, the BGP of is evaluated to match the RDF graphs in . If , then the operators use to provide the query result . Given , we represent a why-not question as a mapping , where is a variable in , and the RDF term is a solution of . A mapping indicates why an RDF item does not appear in .
An explanation represents the reason for a why-not question . The explanation for the absence of an item is given in the following two forms: (1) A modified BGP, which is similar to the original BGP. The modified BGP should match an RDF graph from with a variable bound to . (2) A set of tuples, which is denoted by . Each tuple indicates a questionable query operator and the corresponding matched RDF graph that contains the expected item . 3
2 Demonstration is available online at http://kfm.skyclass.net/anna/index.jsp Module I Identifying Why-not Reasons: This module identifies the level from which the expected item is removed in a two-step process. a) All the variables of BGP are replaced in accordance with to generate a why-not BGP . In consideration of the SPARQL query in Section 1, the variable is adjusted to The Nightmare Before Christmas in accordance with . b) is matched to (the dataset for ANNA is the DBpedia data stored by Jena TDB3). If , then the why-not reason is located at the operator level; otherwise, it is located at the BGP level.
Module II Modifying Why-not BGPs: This module aims to identify and modify the
inappropriate triple patterns in , which are blamed for . ANNA
generates a modified why-not BGP via a graph-based approach, as follows:
a) Each triple pattern of is added to initialized as by a biased
breadth-first traversal over the line graph [
Module III Identifying Questionable Operators: This module aims to address
whynot questions at the operator level. Questionable query operators are filtered out, and
is returned and denoted by . The main procedures are as follows:
a) A SPARQL operator tree is constructed by parsing query according to [
The entire system is performed through a web application written in Java. We briefly illustrate how ANNA works through the preceding example.
The user submits a query using the search panel shown in Fig. 3(a). After the results return, the user can pose a why-not question . The procedures are as follows: (i) Select from the drop-down menu (e.g., ). (ii) Fill in the blank with (e.g., ).
The explanation generated by ANNA is returned as shown in Fig. 3(b), and is highlighted in the operator tree shown in Fig. 3(c). For the preceding example, the explanation is a modified BGP generated from as is replaced with .
(b) Visualization of an explanation (a)A screenshot of ANNA for submitting a why-not question (c) An explanation
Fig. 3. Demonstration of ANNA
A total of 61 why-not questions are obtained from 42 SPARQL queries4 to evaluate the effectiveness and efficiency of ANNA. The satisfaction of the explanations is measured by a five-point Likert scale, and 76.5% of the explanations are considered strongly agree. The experimental results prove that ANNA can generate high-quality explanations within a reasonable amount of time at both BGP (approximately 5 s) and operator levels (approximately 1.8 s). 5
For the first time, we develop a novel explanation system called ANNA. Two main lines are prioritized in future work. First, we aim to transform ANNA into a Java library that can be extended to any RDF database. Second, we intend to utilize union and optional graph patterns to address why-not questions for SPARQL queries. Acknowledgements The research was supported in part by the Doctoral Fund of Ministry of Education of China under Grant No. 20130201130002 and No. IRT13035.