Stefan Bischof

RDFS with Attribute Equations via SPARQL Rewriting

In addition to taxonomic knowledge about concepts and properties typically expressible in languages such as RDFS and OWL, implicit information in an RDF graph may be likewise determined by arithmetic equations. The main use case here is exploiting knowledge about functional dependencies among numerical attributes expressible by means of such equations. While some of this knowledge can be encoded in rule extensions to ontology languages, we provide an arguably more flexible framework that treats attribute equations as first class citizens in the ontology language. The combination of ontological reasoning and attribute equations is realized by extending query rewriting techniques already successfully applied for ontology languages such as (the DL-Lite-fragment of) RDFS or OWL, respectively. We deploy this technique for rewriting SPARQL queries and discuss the feasibility of alternative implementations, such as rule-based approaches.

Improve efficiency of mapping data between XML and RDF with XSPARQL

XSPARQL is a language to transform data between the tree-based XML format and the graph-based RDF format. XML is a widely adopted data exchange format which brings its own query language XQuery along. RDF is the standard data format of the Semantic Web with SPARQL being the corresponding query language. XSPARQL combines XQuery and SPARQL to a unified query language which provides a more intuitive and maintainable way to translate data between the two data formats. A naive implementation of XSPARQL can be inefficient when evaluating nested queries. However, such queries occur often in practice when dealing with XML data. We present and compare several approaches to optimise nested queries. By implementing these optimisations we improve efficiency up to two orders of magnitude in a practical evaluation.

Optimising XML---RDF data integration: a formal approach to improve XSPARQL efficiency

The Semantic Web provides a wealth of open data in RDF format. XML remains a widespread format for data exchange. When combining data of these two formats several problems arise due to representational incompatibilities. The query language XSPARQL, which is built by combining XQuery and SPARQL, addresses some of these problems. However the evaluation of complex XSPARQL queries by a naive implementation shows slow response times. Establishing an integrated formal model for a core fragment of XSPARQL will allow us to improve performance of query answering by defining query equivalences.

A public transportation journey planner enabled by IoT data analytics

Using the CityPulse framework we developed a proof of concept application for Braşov public transportation company. The application provides route recommendations and incident notifications for the citizens who travel by bus. This is achieved by processing in real-time the data streams about bus arrivals in stations and the incidents reported by citizens. The proof of concept application was presented to the representatives of Braşov municipality and public transport company.

Enriching Integrated Statistical Open City Data by Combining Equational Knowledge and Missing Value Imputation

Several institutions collect statistical data about cities, regions, and countries for various purposes. Yet, while access to high quality and recent such data is both crucial for decision makers and a means for achieving transparency to the public, all too often such collections of data remain isolated and not re-useable, let alone comparable or properly integrated. In this paper we present the Open City Data Pipeline, a focused attempt to collect, integrate, and enrich statistical data collected at city level worldwide, and re-publish the resulting dataset in a re-useable manner as Linked Data. The main features of the Open City Data Pipeline are: (i) we integrate and cleanse data from several sources in a modular and extensible, always up-to-date fashion; (ii) we use both Machine Learning techniques and reasoning over equational background knowledge to enrich the data by imputing missing values, (iii) we assess the estimated accuracy of such imputations per indicator. Additionally, (iv) we make the integrated and enriched data, including links to external data sources, such as DBpedia, available both in a web browser interface and as machine-readable Linked Data, using standard vocabularies such as QB and PROV. Apart from providing a contribution to the growing collection of data available as Linked Data, our enrichment process for missing values also contributes a novel methodology for combining rule-based inference about equational knowledge with inferences obtained from statistical Machine Learning approaches. While most existing works about inference in Linked Data have focused on ontological reasoning in RDFS and OWL, we believe that these complementary methods and particularly their combination could be fruitfully applied also in many other domains for integrating Statistical Linked Data, independent from our concrete use case of integrating city data.