Semantic Web Languages and Semantic Web Services as Application Areas for Answer Set Programming

In the Semantic Web and Semantic Web Services areas there are still unclear issues concerning an appropriate language. Answer Set Programming and ASP engines can be particularly interesting for Ontological Reasoning, especially in the light of ongoing discussions of non-Monotonic extensions for Ontology Languages. Previously, the main concern of discussions was around OWL and Description Logics. Recently many extensions and suggestions for Rule Languages and Semantic Web Languages pop up, particularly in the the context of Semantic Web Services, which involve the meta-data description of Services instaead of static data on the Web only. These lanuages involve SWRL, WSML, SWSL-Rules, etc. I want to give an outline of languages, challenges and initiatives in this area and where I think Answer Set Programming research can hook in. (30min)

A Perfect Match for Reasoning, Explanation, and Reason Maintenance

Path query languages have been previously shown to com- plement RDF rule languages in a natural way and have been used as a means to implement the RDFS derivation rules. RPL is a novel path query language specifically designed to be incorporated with RDF rules and comes in three avors: Node-, edge- and path- avored expressions allow to express conditional regular expressions over the nodes, edges, or nodes and edges appearing on paths within RDF graphs. Providing reg- ular string expressions and negation, RPL is more expressive than other RDF path languages that have been proposed. We give a compositional semantics for RPL and show that it can be evaluated efficiently, while several possible extensions of it cannot

Enabling Spatio-Temporal Search in Open Data

Intuitively, most datasets found in Open Data are organised by spatio-temporal scope, that is, single datasets provide data for a certain region, valid for a certain time period. For many use cases (such as for instance data journalism and fact checking) a pre-dominant need is to scope down the relevant datasets to a particular period or region. Therefore, we argue that spatio-temporal search is a crucial need for Open Data portals and across Open Data portals, yet - to the best of our knowledge - no working solution exists. We argue that - just like for for regular Web search - knowledge graphs can be helpful to significantly improve search: in fact, the ingredients for a public knowledge graph of geographic entities as well as time periods and events exist already on the Web of Data, although they have not yet been integrated and applied - in a principled manner - to the use case of Open Data search. In the present paper we aim at doing just that: we (i) present a scalable approach to construct a spatio-temporal knowledge graph that hierarchically structures geographical, as well as temporal entities, (ii) annotate a large corpus of tabular datasets from open data portals, (iii) enable structured, spatio-temporal search over Open Data catalogs through our spatio-temporal knowledge graph, both via a search interface as well as via a SPARQL endpoint, available at data.wu.ac.at/odgraphsearch/Series: Working Papers on Information Systems, Information Business and Operation

A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language -- AnQL -- that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics

Geo-Semantic Labelling of Open Data. SEMANTiCS 2018-14th International Conference on Semantic Systems

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use di ff erent schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities. This knowledge graph does not yet readily exist, though, or at least, not a single one: so, we integrate and interlink several datasets to construct our (extensible) base geo-entities knowledge graph: (i) the openly available geospatial data repository GeoNames, (ii) the map service OpenStreetMap, (iii) country-specific sets of postal codes, and (iv) the European Union's classification system NUTS. As a second step, this base knowledge graph is used to add semantic labels to the open datasets, i.e., we heuristically disambiguate the geo-entities in CSV columns using the context of the labels and the hierarchical graph structure of our base knowledge graph. Finally, in order to interact with and retrieve the content, we index the datasets and provide a demo user interface. Currently we indexed resources from four Open Data portals, and allow search queries for geo-entities as well as full-text matches at http://data.wu.ac.at/odgraph/

Doc2RDFa: Semantic Annotation for Web Documents

Ever since its conception, the amount of data published on the worldwide web has been rapidly growing to the point where it has become an important source of both general and domain specific information. However, the majority of documents published online are not machine readable by default. Many researchers believe that the answer to this problem is to semantically annotate these documents, and thereby contribute to the linked "Web of Data". Yet, the process of annotating web documents remains an open challenge. While some efforts towards simplifying this process have been made in the recent years, there is still a lack of semantic content creation tools that integrate well with information worker toolsets. Towards this end, we introduce Doc2RDFa, an HTML rich text processor with the ability to automatically and manually annotate domain-specific Content

dlvhex-sparql: A SPARQLcompliant query engine based on dlvhex

Abstract. This paper describes the dlvhex SPARQL plugin, a query processor for the upcoming Semantic Web query language standard by W3C. We report on the implementation of this languages using dlvhex, a flexible plugin system on top of the DLV solver. This work advances our earlier translation based on the semantics by Perez et al. towards an engine which is fully compliant to the official SPARQL specification. As it turns out, the differences between these two definitions of SPARQL, which might seem moderate at first glance, need some extra machinery. We also briefly report the status of implementation, and extensions currently being implemented, such as handling of aggregates, nested CONSTRUCT queries in the spirit of networked RDF graphs, or partially support of RDFS entailment. For such extensions a tight integration of SPARQL query processing and Answer-Set Programming, the underlying logic programming formalism of our engine, turns out to be particularly useful, as the resulting programs can actually involve unstratified negation.