An On-Line Learning to Query System

Abstract

We present an on-line system which learns a SPARQL query from a set of wanted and a set of unwanted results of the query. The sets are extended during a dialog with the user guided by recall and F1 measure. The system leverages SPARQL 1.1 and does not depend on any particular RDF graph.

1 Introduction

A common problem with querying a Linked Data dataset is that the user must have prior knowledge about the vocabulary used by the dataset and know a querying language, e.g. SPARQL [ 3 ]. A typical approach to remedy the problem is to use some tool helping the user to formulate a SPARQL query, using e.g. faceted browsing [ 2 ], natural-language interfaces [ 4 ], visual interfaces [ 7 ] or recommendations [ 1 ]. In all these system the user speci es the query by various means. We propose a di erent approach, where the user only speci es what should and what should not be in the results of the query, and the system takes care of formulating the query. The user does not need to know SPARQL, it is enough for her to be able to distinguish wanted and unwanted results. If the user is already familiar with the data (e.g. knows its representation in some other format), it is quite an easy task for her. The system operates by conducting a dialog with the user. In each part of the dialog, the user is asked about a small set of URIs and for every URI she must decide if it should or should not be present in the results of the nal query. A similar approach was already presented in [ 5 ], where the system used mostly computational power of the computer system of the user. We leverage new features of SPARQL 1.1 and move most of the computation to a SPARQL endpoint, especially the process of query re nement guided by recall and deciding on termination depending on F1 measure.

Thought this work, we use the following pre xes: dbr: for http://dbpedia. org/resource/, dbo: for http://dbpedia.org/ontology/, dbp: for http: //dbpedia.org/property/, dct: for http://purl.org/dc/terms/, xsd: for http://www.w3.org/2001/XMLSchema#.

System Description

A screenshot of the on-line system is presented in Figure 1. The source code of the system is available in a Git 1 repository available at https:// bitbucket.org/jpotoniec/kretr/. An instance of the system is available at https://semantic.cs.put.poznan.pl/ltq/. It uses a SPARQL endpoint set up on Blazegraph2 2.1.1 and loaded with DBpedia 2015-04. Note that the system itself does not depend neither on Blazegraph nor DBpedia, as it can use any SPARQL 1.1 endpoint.

The aim of the system is to build a SPARQL SELECT query with a single variable in the head. The variable has a modi er DISTINCT. The query contains only a WHERE clause (i.e. there is no GROUP BY, ORDER BY etc.), and in the clause there is only a basic graph pattern (BGP, i.e. triple patterns and lter expressions). The undirected graph corresponding to the BGP is a connected 1 https://git-scm.com/ 2 https://www.blazegraph.com/ graph and the lter expressions are of a form variable >=/<= literal. An example of such a query is SELECT DISTINCT ?uri WHERE f ?uri dct:subject dbr:Category:City_counties_of_Poland . ?uri dbo:populationTotal ?anon1.

FILTER(?anon1 >= "205934"^^xsd:nonNegativeInteger). g

A typical work ow with the system is presented in Figure 2. First, the user speci es a small set of URIs that should be present in the results of the nal query, and a small set of URIs that should not be present in the results. Then, the system re nes the query by adding to it a new triple pattern (or a triple pattern and a lter expression) while maintaining recall of at least 0.99, i.e. covering at least 99% of the positive examples. In a typical case of multiple possible re nements, they are sorted by F1 measure and precision and the one with the highest values is chosen. Next, the system generates a few new positive and negative examples. The positive examples are simply selected from the results of the re ned query, while the negative examples are computed by subtracting from the results of the original query the results of the re ned query. In each case we require the found examples to be new, i.e. they must not be already labeled by the user. If nding the new examples is impossible, the re nement is retracted and the next in order is tried. The user is then asked to decide about each example if it should or not be present in the results of the nal query (i.e. the user extends the sets de ned in the beginning). After the user decides, the system checks if the decisions agrees with the query. If they do not, the cycle repeats: the system re nes the query, asks the user and assess the query. Otherwise, the system assumes that the correct query was found. The query is displayed to the user along with the results of the query. If the user decides that the results are not satisfactory, she must add at least one new URI to one of the sets. More details about the algorithm, along with the templates of queries used, is presented in [ 6 ]. During the demo, we will present to the participants how to use the system, what types of queries are possible to learn and what are the limitations. The system has embedded two demo scenarios: the rst one to nd a query to select all capitals of the member states of European Union and the second one to nd a query to select all Polish cities having more than 2000000 citizens. For the presentation, we will use the on-line instance available at https://semantic. cs.put.poznan.pl/ltq/. 4

Conclusion

In this paper we presented a system which is able to learn a SPARQL query from two sets of URIs obtained from the user in a dialog. The system leverages new features of SPARQL 1.1 and does not depend on any particular RDF graph. It also does not require any precomputation before it is ready to use. The source code is publicly available.

Acknowledgement. Jedrzej Potoniec acknowledges the support from the Polish National Science Center (Grant No 2013/11/N/ST6/03065).

References

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