Critical Life Experiences that Mold a Person into a Global Scholar

Global Scholar Toni Fuss Kirkwood Tucker shares her experiences in Nazi Germany. This column contains an excerpt of Toni's presentation her award luncheon

Context-Free Path Queries on RDF Graphs

Navigational graph queries are an important class of queries that canextract implicit binary relations over the nodes of input graphs. Most of the navigational query languages used in the RDF community, e.g. property paths in W3C SPARQL 1.1 and nested regular expressions in nSPARQL, are based on the regular expressions. It is known that regular expressions have limited expressivity; for instance, some natural queries, like same generation-queries, are not expressible with regular expressions. To overcome this limitation, in this paper, we present cfSPARQL, an extension of SPARQL query language equipped with context-free grammars. The cfSPARQL language is strictly more expressive than property paths and nested expressions. The additional expressivity can be used for modelling graph similarities, graph summarization and ontology alignment. Despite the increasing expressivity, we show that cfSPARQL still enjoys a low computational complexity and can be evaluated efficiently.Comment: 25 page

Complexity of answering counting aggregate queries over DL - Lite

The ontology based data access model assumes that users access data by means of an ontology, which is often described in terms of description logics. As a consequence, languages for managing ontologies now need algorithms not only to decide standard reasoning problems, but also to answer database-like queries. However, fundamental database aggregate queries, such as the ones using functions COUNT and COUNT DISTINCT, have received very little attention in this context, and even defining appropriate semantics for their answers over ontologies appears to be a non-trivial task. Our goal is to study the problem of answering database queries with aggregation in the context of ontologies. This paper presents an intuitive semantics for answering counting queries, followed by a comparison with similar approaches that have been taken in different database contexts. Afterwards, it exhibits a thorough study of the computational complexity of evaluating counting queries conforming to this semantics. Our results show that answering such queries over ontologies is decidable, but generally intractable. However, our semantics promotes awareness on the information that can be obtained by querying ontologies and raises the need to look for suitable approximations or heuristics in order to allow efficient evaluation of this widely used class of queries

Complexity and expressive power of weakly well-designed SPARQL

SPARQL is the standard query language for RDF data. The distinctive feature of SPARQL is the OPTIONAL operator, which allows for partial answers when complete answers are not available due to lack of information. However, optional matching is computationally expensive—query answering is PSPACE-complete. The well-designed fragment of SPARQL achieves much better computational properties by restricting the use of optional matching—query answering becomes coNP-complete. On the downside, well-designed SPARQL captures far from all real-life queries—in fact, only about half of the queries over DBpedia that use OPTIONAL are well-designed. In the present paper, we study queries outside of well-designed SPARQL. We introduce the class of weakly well-designed queries that subsumes welldesigned queries and includes most common meaningful non-well-designed queries: our analysis shows that the new fragment captures over 99% of DBpedia queries with OPTIONAL. At the same time, query answering for weakly well-designed SPARQL remains coNP-complete, and our fragment is in a certain sense maximal for this complexity. We show that the fragment’s expressive power is strictly in-between well-designed and full SPARQL. Finally, we provide an intuitive normal form for weakly well-designed queries and study the complexity of containment and equivalence

Complexity of answering counting aggregate queries over DL - Lite

The ontology based data access model assumes that users access data by means of an ontology, which is often described in terms of description logics. As a consequence, languages for managing ontologies now need algorithms not only to decide standard reasoning problems, but also to answer database-like queries. However, fundamental database aggregate queries, such as the ones using functions COUNT and COUNT DISTINCT, have received very little attention in this context, and even defining appropriate semantics for their answers over ontologies appears to be a non-trivial task. Our goal is to study the problem of answering database queries with aggregation in the context of ontologies. This paper presents an intuitive semantics for answering counting queries, followed by a comparison with similar approaches that have been taken in different database contexts. Afterwards, it exhibits a thorough study of the computational complexity of evaluating counting queries conforming to this semantics. Our results show that answering such queries over ontologies is decidable, but generally intractable. However, our semantics promotes awareness on the information that can be obtained by querying ontologies and raises the need to look for suitable approximations or heuristics in order to allow efficient evaluation of this widely used class of queries

Answering counting aggregate queries over ontologies of the DL-Lite family

One of the main applications of description logics is the ontology-based data access model, which requires algorithms for query answering over ontologies. In fact, some description logics, like those in the DL-Lite family, are designed so that simple queries, such as conjunctive queries, are efficiently computable. In this paper we study counting aggregate queries over ontologies, i.e. queries which use aggregate functions COUNT and COUNT DISTINCT. We propose an intuitive semantics for certain answers for these queries, which conforms to the open world assumption. We compare our semantics with other approaches that have been proposed in different contexts. We establish data and combined computational complexity for the problems of answering counting aggregate queries over ontologies for several variants of DL-Lite

Complexity and expressive power of weakly well-designed SPARQL

SPARQL is the standard query language for RDF data. The distinctive feature of SPARQL is the OPTIONAL operator, which allows for partial answers when complete answers are not available due to lack of information. However, optional matching is computationally expensive—query answering is PSPACE-complete. The well-designed fragment of SPARQL achieves much better computational properties by restricting the use of optional matching—query answering becomes coNP-complete. On the downside, well-designed SPARQL captures far from all real-life queries—in fact, only about half of the queries over DBpedia that use OPTIONAL are well-designed. In the present paper, we study queries outside of well-designed SPARQL. We introduce the class of weakly well-designed queries that subsumes welldesigned queries and includes most common meaningful non-well-designed queries: our analysis shows that the new fragment captures over 99% of DBpedia queries with OPTIONAL. At the same time, query answering for weakly well-designed SPARQL remains coNP-complete, and our fragment is in a certain sense maximal for this complexity. We show that the fragment’s expressive power is strictly in-between well-designed and full SPARQL. Finally, we provide an intuitive normal form for weakly well-designed queries and study the complexity of containment and equivalence

Estimating the cardinality of conjunctive queries over RDF data using graph summarisation

Estimating the cardinality (i.e., the number of answers) of conjunctive queries is particularly difficult in RDF systems: queries over RDF data are navigational and thus tend to involve many joins. We present a new, principled cardinality estimation technique based on graph summarisation. We interpret a summary of an RDF graph using a possible world semantics and formalise the estimation problem as computing the expected cardinality over all RDF graphs represented by the summary, and we present a closed-form formula for computing the expectation of arbitrary queries. We also discuss approaches to RDF graph summarisation. Finally, we show empirically that our cardinality technique is more accurate and more consistent, often by orders of magnitude, than the state of the art. </p

Estimating the cardinality of conjunctive queries over RDF data using graph summarisation

Estimating the cardinality (i.e., the number of answers) of conjunctive queries is particularly difficult in RDF systems: queries over RDF data are navigational and thus tend to involve many joins. We present a new, principled cardinality estimation technique based on graph summarisation. We interpret a summary of an RDF graph using a possible world semantics and formalise the estimation problem as computing the expected cardinality over all RDF graphs represented by the summary, and we present a closed-form formula for computing the expectation of arbitrary queries. We also discuss approaches to RDF graph summarisation. Finally, we show empirically that our cardinality technique is more accurate and more consistent, often by orders of magnitude, than the state of the art. </p

Static analysis of navigational XPath over graph databases

Most query languages for graph databases rely on exploring the topological properties of the data by using paths. However, many applications require more complex patterns to be matched against the graph to obtain desired results. For this reason a version of the standard XML query language XPath has been adapted to work over graphs. In this paper we study static analysis aspects of this language, concentrating on problems such as containment, equivalence and satisfiability. We show that for the full language all of the problems are undecidable. By restricting the language we then obtain several natural fragments whose complexity ranges from PSpace-complete to ExpTime-complete