Maxime Lefrançois

A SPARQL Extension for Generating RDF from Heterogeneous Formats

RDF aims at being the universal abstract data model for structured data on the Web. While there is effort to convert data in RDF, the vast majority of data available on the Web does not conform to RDF. Indeed, exposing data in RDF, either natively or through wrappers, can be very costly. Furthermore, in the emerging Web of Things, resource constraints of devices prevent from processing RDF graphs. Hence one cannot expect that all the data on the Web be available as RDF anytime soon. Several tools can generate RDF from non-RDF data, and transformation or mapping languages have been designed to offer more flexible solutions (GRDDL, XSPARQL, R2RML, RML, CSVW, etc.). In this paper, we introduce a new language, SPARQL-Generate, that generates RDF from: (i) a RDF Dataset, and (ii) a set of documents in arbitrary formats. As SPARQL-Generate is designed as an extension of SPARQL 1.1, it can provably: (i) be implemented on top on any existing SPARQL engine, and (ii) leverage the SPARQL extension mechanism to deal with an open set of formats. Furthermore, we show evidence that (iii) it can be easily learned by knowledge engineers that know SPARQL 1.1, and (iv) our first naive open source implementation performs better than the reference implementation of RML for big transformations.

Supporting Arbitrary Custom Datatypes in RDF and SPARQL

In the Resource Description Framework, literals are composed of a UNICODE string the lexical form, a datatype IRI, and optionally, when the datatype IRI is rdf:langString, a language tag. Any IRI can take the place of a datatype IRI, but the specification only defines the precise meaning of a literal when the datatype IRI is among a predefined subset. Custom datatypes have reported use on the Web of Data, and show some advantages in representing some classical structures. Yet, their support by RDF processors is rare and implementation specific. In this paper, we first present the minimal set of functions that should be defined in order to make a custom datatype usable in query answering and reasoning. Based on this, we discuss solutions that would enable: i data publishers to publish the definition of arbitrary custom datatypes on the Web, and ii generic RDF processor or SPARQL query engine to discover custom datatypes on-the-fly, and to perform operations on them accordingly. Finally, we detail a concrete solution that targets arbitrarily complex custom datatypes, we overview its implementation in Jena and ARQ, and we report the results of an experiment on a real world DBpedia use case.

Publishing real-time microgrid consumption data on the web of Linked Data

Decentralising the power systems management brings clear advantages for the grid and for the stakeholders (consumers, generators, operational and management actors). The grid decentralisation can be achieved using microgrids. Some microgrids emerge worldwide, working as small energy islands. It is admitted that ontologies and open (Semantic) Web standards can form a basis for advanced communication architectures in smart grids. True, ontologies enable semantic interoperability and logical reasoning; the Linked Data principles enable the discovery of new information on the web. This paper reports on the use of these formalisms and principles to develop a new information system for a microgrid site already in place. More specifically, we developed a new ontology to represent time series of multiple observations, and made real-time consumption data available on the web as Linked Data. This enables consumption reporting, and enables other researchers to test their algorithms against real-time consumption data.

SCORVoc: Vocabulary-Based Information Integration and Exchange in Supply Networks

Advanced, highly specialized economies require instant, robust and efficient information flows within its value-added and Supply Chain networks. Especially also in the context of the recent Industry 4.0, smart manufacturing or cyber-physical systems initiatives more efficient and effective information exchange in supply networks is of paramount importance. The Supply Chain Operation Reference (SCOR) is a cross-industry approach to lay the groundwork for this goal by defining a conceptual model for Supply Chain related information. Semantics-based approaches could facilitate information flows in supply networks, and enable to analyze, monitor and optimize Supply Chains (in particular for robustness). This paper first reviews existing formalizations of the Supply Chain Councils SCOR standard. It then introduces the SCORVoc RDFS vocabulary which fully formalizes the latest SCOR standard, while over-coming the identified limitations of existing work. SCORVoc is operationalized by a set of SPARQL queries, that enable to evaluate metrics and key performance indicator (KPIs) defined by SCOR, on-the-fly, in an information systems that adheres to the vocabulary. Finally, we define concrete test scenarios and implement a synthetic benchmark to demonstrate the practicality of SCORVoc.

Flexible RDF Generation from RDF and Heterogeneous Data Sources with SPARQL-Generate

RDF aims at being the universal abstract data model for structured data on the Web. While there is effort to convert data in RDF, the vast majority of data available on the Web does not conform to RDF. Indeed, exposing data in RDF, either natively or through wrappers, can be very costly. In this context, transformation or mapping languages that define generation of RDF from non-RDF data represent an efficient solution. Furthermore, the declarative aspect of these solutions makes them easy to adapt to any change in the input data model, or in the output knowledge model. This paper introduces a novel such transformation language (SPARQL-Generate), an extension of SPARQL for querying not only RDF datasets but also documents in arbitrary formats. Its implementation on top of Apache Jena currently covers use cases from related work and more, and enables to query and transform web documents in XML, JSON, CSV, HTML, CBOR, and plain text with regular expressions.

SOSA: A Lightweight Ontology for Sensors, Observations, Samples, and Actuators

Abstract The Sensor, Observation, Sample, and Actuator (SOSA) ontology provides a formal but lightweight general-purpose specification for modellingthe interaction between the entities involved in the acts of observation, actuation, and sampling. SOSA is the result of rethinking the W3C-XG Semantic Sensor Network (SSN) ontology based on changes in scope and target audience, technical developments, and lessons learned over the past years. SOSA also acts as a replacement of SSN’s Stimulus Sensor Observation (SSO) core. It has been developed by the first joint working group of the Open Geospatial Consortium (OGC) and the World Wide Web Consortium (W3C) on Spatial Data on the Web. In this work, we motivate the need for SOSA, provide an overview of the main classes and properties, and briefly discuss its integration with the new release of the SSN ontology as well as various other alignments to specifications such as OGC’s Observations and Measurements (O&M), Dolce-Ultralite (DUL), and other prominent ontologies. We will also touch upon common modelling problems and application areas related to publishing and searching observation, sampling, and actuation data on the Web. The SOSA ontology and standard can be accessed at https://www.w3.org/TR/vocab-ssn/ .

Computer-Assisted Ontology Construction System: Focus on Bootstrapping Capabilities

In this research, we investigate the problem of ontology construction in both automatic and semi-automatic approaches. There are two key issues for the ontology construction process: the cold start problem (i.e. starting the development of an ontology from a blank page) and the lack of availability of domain experts. We describe a functionality for ontology construction based on the bootstrapping feature. For this feature, we take advantage of large public knowledge bases. We report on a comparative study between our system and the existing ones on the wine ontology.

The Unified Code for Units of Measure in RDF: cdt:ucum and other UCUM Datatypes

Being able to describe quantity values and their units is a requirement that is common to many applications in several industrial sectors such as manufacturing, transport and logistics, personal and public health, smart cities, energy, environment, buildings, agriculture. Different ontologies have been developed to describe units, their relations, and quantities with their values. In this paper we propose an alternative approach that leverages the Unified Code of Units of Measure, a code system intended to include all units of measures being contemporarily used in international sciences, engineering, and business. Our approach consists of a main UCUM datatype identified by IRI http://w3id.org/lindt/custom_datatypes#ucum, abbreviated as cdt:ucum. This datatype can be used for lightweight encoding and querying of quantity values, in a wide range of applications where representing and reasoning with quantity kinds and values is more important than reasoning with units. We compare our approach with existing approaches, and demonstrate it with our implementation on top of Apache Jena and an online testing tool.

The modular SSN ontology: A joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation

The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [9] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.