Patrik Schneider

The Fourth Answer Set Programming Competition: Preliminary Report

Answer Set Programming is a well-established paradigm of declarative programming in close relationship with other declarative formalisms such as SAT Modulo Theories, Constraint Handling Rules, PDDL and many others. Since its first informal editions, ASP systems are compared in the nowadays customary ASP Competition. The fourth ASP Competition, held in 2012/2013, is the sequel to previous editions and it was jointly organized by University of Calabria Italy and the Vienna University of Technology Austria. Participants competed on a selected collection of benchmark problems, taken from a variety of research areas and real world applications. The Competition featured two tracks: the Model& Solve Track, held on an open problem encoding, on an open language basis, and open to any kind of system based on a declarative specification paradigm; and the System Track, held on the basis of fixed, public problem encodings, written in a standard ASP language.

Uniform evaluation of nonmonotonic DL-Programs

Nonmonotonic description logic programs are a major formalism for a loose coupling of rules and ontologies, formalized in logic programming and description logics, respectively. While this approach is attractive for combining systems, the impedance mismatch between different reasoning engines and the API-style interfacing are an obstacle to efficient evaluation of dl-programs in general. Uniform evaluation circumvents this by transforming programs into a single formalism, which can be evaluated on a single reasoning engine. In this paper, we consider recent and ongoing work on this approach which uses relational first-order logic (and thus relational database engines) and datalog with negation as target formalisms. Experimental data show that significant performance gains are possible and suggest the potential of this approach.

Lightweight Spatial Conjunctive Query Answering Using Keywords

With the advent of publicly available geospatial data, ontology-based data access (OBDA) over spatial data has gained increasing interest. Spatio-relational DBMSs are used to implement geographic information systems (GIS) and are fit to manage large amounts of data and geographic objects such as points, lines, polygons, etc. In this paper, we extend the Description Logic DL-Lite with spatial objects and show how to answer spatial conjunctive queries (SCQs) over ontologies—that is, conjunctive queries with point-set topological relations such as next and within—expressed in this language. The goal of this extension is to enable an off-the-shelf use of spatio-relational DBMSs to answer SCQs using rewriting techniques, where data sources and geographic objects are stored in a database and spatial conjunctive queries are rewritten to SQL statements with spatial functions. Furthermore, we consider keyword-based querying over spatial OBDA data sources, and show how to map queries expressed as simple keyword lists describing objects of interest to SCQs, using a meta-model for completing the SCQs with spatial aspects. We have implemented our lightweight approach to spatial OBDA in a prototype and show initial experimental results using data sources such as Open Street Maps and Open Government Data Vienna from an associated project. We show that for real-world scenarios, practical queries are expressible under meta-model completion, and that query answering is computationally feasible.

The DBpedia wayback machine

DBpedia is one of the biggest and most important focal point of the Linked Open Data movement. However, in spite of its multiple services, it lacks a wayback mechanism to retrieve historical versions of resources at a given timestamp in the past, thus preventing systems to work on the full history of RDF documents. In this paper, we present a framework that serves this mechanism and is publicly offered through a Web UI and a RESTful API, following the Linked Open Data principles.

Semantically Enriched Multi-Modal Routing

We present an innovative extension to routing: intention-oriented routing which is a direct result of combining classical routing-services with Semantic Web technologies. Thereby, the intention of a user can be easily incorporated into route planning. We highlight two use cases where this hybridization is of great significance: neighborhood routing, where a neighborhood can be explored (e.g. searching for events around your place) and via routing, where errands should be run along a route (e.g. buying the ingredients for dinner on your way home). We outline the combination of different methods to achieve these services, and demonstrate the emerging framework on two case studies, with a prototype extending in-use routing services.

Spatial Ontology-Mediated Query Answering over Mobility Streams

The development of (semi)-autonomous vehicles and communication between vehicles and infrastructure (V2X) will aid to improve road safety by identifying dangerous traffic scenes. A key to this is the Local Dynamic Map (LDM), which acts as an integration platform for static, semi-static, and dynamic information about traffic in a geographical context. At present, the LDM approach is purely database-oriented with simple query capabilities, while an elaborate domain model as captured by an ontology and queries over data streams that allow for semantic concepts and spatial relationships are still missing. To fill this gap, we present an approach in the context of ontology-mediated query answering that features conjunctive queries over DL-Lite\(_A\) ontologies allowing spatial relations and window operators over streams having a pulse. For query evaluation, we present a rewriting approach to ordinary DL-Lite\(_A\) that transforms spatial relations involving epistemic aggregate queries and uses a decomposition approach that generates a query execution plan. Finally, we report on experiments with two scenarios and evaluate our implementation based on the stream RDBMS PipelineDB.

Towards a Semantically Enriched Local Dynamic Map

With the increasing availability of Cooperative Intelligent Transport Systems, the Local Dynamic Map (LDM) is becoming a key technology for integrating static, temporary, and dynamic information in a geographical context. However, existing ideas do not leverage the full potential of the LDM approach, as an LDM contains streaming data and varying implicit information which are not captured by current models. We aim to provide a semantically enriched LDM that applies Semantic Web technologies, in particular ontologies, in combination with spatial stream databases. This allows us to define an enhanced world model, to derive model properties, to infer new information, and to offer expressive query capabilities over streams. We introduce our envisioned architecture which includes an LDM ontology, an integration and annotation framework, and a stream query answering component. We also sketch three application scenarios that illustrate the usability and benefits of our approach, thus we provide an in-depth validation of the scenarios in an experimental prototype.

A Rule-based Framework for Creating Instance Data from OpenStreetMap

Reasoning engines for ontological and rule-based knowledge bases are becoming increasingly important in areas like the Semantic Web or information integration. It has been acknowledged however that judging the performance of such reasoners and their underlying algorithms is difficult due to the lack of publicly available datasets with large amounts of (real-life) instance data. In this paper we describe a framework and a toolbox for creating such datasets, which is based on extracting instances from the publicly available OpenStreetMap (OSM) geospatial database. To this end, we give a formalization of OSM and present a rule-based language to specify the rules to extract instance data from OSM data. The declarative nature of the approach in combination with external functions and parameters allows one to create several variants of the dataset via small modifications of the specification. We describe a highly flexible toolbox to extract instance data from a given OSM map and a given set of rules. We have employed our tools to create benchmarks that have already been fruitfully used in practice.

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.

DC proposal: towards a framework for efficient query answering and integration of geospatial data

Semantic Web technologies are becoming more interleaved with geospatial databases, which should lead to an easier integration and querying of spatial data. This is fostered by a growing amount of publicly available geospatial data like OpenStreetMap. However, the integration can lead to geographic inconsistencies when combining multiple knowledge bases. Having the integration in place, users might not just issue a points-of-interest search, but rather might be interested in regions with specific attributes assigned to them. Though, having large amounts of spatial data available, standard databases and reasoners do not provide the means for (quantitative) spatial queries, or struggle to answer them efficiently. We seek to combine spatial reasoning, (nonmonotonic) logic programming, and ontologies for integrating geospatial databases with Semantic Web technologies. The focus of our investigation will be on a modular design, on efficient processing of large amounts of spatial data, and on enabling default reasoning. We propose a two-tier design related to HEX-programs, which should lead to a plausible trade-off between modularity and efficiency. Furthermore, we consider suitable geo-ontologies to semantically annotate and link different sources. Finally, the findings should lead to a proof-of-concept implementation, which will be tested for efficiency and modularity in artificial and real-world use cases.