Semantic Web: – Interoperability,Usability,Applicability

A lot of tabular data are being published on the Web. Semantic labeling of such data may help in their understanding and exploitation. However, many challenges need to be addressed to do this automatically. With numbers, it can be even harder due to the possible difference in measurement accuracy, rounding errors, and even the frequency of their appearance. Multiple approaches have been proposed in the literature to tackle the problem of semantic labeling of numeric values in existing tabular datasets. However, they also suffer from several shortcomings: closely coupled with entity-linking, rely on table context, need to profile the knowledge graph, and require manual training of the model. Above all, however, they all treat different types of numeric values evenly. In this paper, we tackle these problems and validate our hypothesis: whether taking into account the typology of numeric data in semantic labeling yields better results.

Matching entities between datasets is a crucial step for combining multiple datasets on the semantic web. A rich literature exists on different approaches to this entity resolution problem. However, much less work has been done on how to assess the quality of such entity links once they have been generated. Evaluation methods for link quality are typically limited to either comparison with a ground truth dataset (which is often not available), manual work (which is cumbersome and prone to error), or crowd sourcing (which is not always feasible, especially if expert knowledge is required). Furthermore, the problem of link evaluation is greatly exacerbated for links between more than two datasets, because the number of possible links grows rapidly with the number of datasets.

In this paper, we propose a method to estimate the quality of entity links between multiple datasets. We exploit the fact that the links between entities from multiple datasets form a network, and we show how simple metrics on this network can reliably predict their quality. We verify our results in a large experimental study using six datasets from the domain of science, technology and innovation studies, for which we created a gold standard. This gold standard, available online, is an additional contribution of this paper. In addition, we evaluate our metric on a recently published gold standard to confirm our findings.

Cooperative Intelligent Transport Systems (C-ITS) play an important role for providing the means to collect and exchange spatio-temporal data via V2X-based communication between vehicles and the infrastructure, which will become a central enabler for road safety of (semi)-autonomous vehicles. The Local Dynamic Map (LDM) is a key concept for integrating static and streamed data in a spatial context. The LDM has been semantically enhanced to allow for an elaborate domain model that is captured by a mobility ontology, and for queries over data streams that cater for semantic concepts and spatial relationships. Our approach for semantic enhancement is in the context of ontology-mediated query answering (OQA) and features conjunctive queries over DL-LiteA ontologies that support window operators over streams and spatial relations between spatial objects. In this paper, we show how this approach can be extended to address a wider range of use cases in the three C-ITS scenarios traffic statistics, traffic events detection, and advanced driving assistance systems. We define for the mentioned use cases requirements derived from necessary domain-specific features and report, based on them, on extensions of our query language and ontology model. The extensions include temporal relations, numeric predictions and trajectory predictions as well as optimization strategies such as caching. An experimental evaluation of queries that reflect the requirements has been conducted using the real-world traffic simulation tool PTV Vissim. It provides evidence for the feasibility/efficiency of our approach in the new scenarios.

The Data Quality Vocabulary (DQV) provides a metadata model for expressing data quality. DQV was developed by the Data on the Web Best Practice (DWBP) Working Group of the World Wide Web Consortium (W3C) between 2013 and 2017. This paper aims at providing a deeper understanding of DQV. It introduces its key design principles, components, and the main discussion points that have been raised in the process of designing it. The paper compares DQV with previous quality documentation vocabularies and demonstrates the early uptake of DQV by collecting tools, papers, projects that have exploited and extended DQV.

Meaningful information about an RDF resource can be obtained not only by looking at its properties, but by putting it in the broader context of similar resources. Classic navigation paradigms on the Web of Data that employ a follow-your-nose strategy fail to provide such context, and put strong emphasis on first-level properties, forcing users to drill down in the graph one step at a time. We introduce the concept of semantic paths: starting from a set of resources, we follow and analyse chains of triples and characterize the sets of values at their end. We investigate a navigation strategy based on aggregation, relying on path characteristics to determine the most readable representation. We implement this approach in S-Paths, a browsing tool for linked datasets that systematically identifies the best rated view on a given resource set, leaving users free to switch to another resource set, or to get a different perspective on the same set by selecting other semantic paths to visualize.

The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application’s supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator’s performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation “Validatrr”, based on N3Logic and the EYE reasoner. Our approach – supporting an equivalent number of constraint types compared to the state of the art – better explains the root cause of the violations due to the reasoner’s generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions.

Actors in the Architecture, Engineering, Construction, Owner and Operation (AECOO) industry traditionally exchange building models as files. The Building Information Modelling (BIM) methodology advocates the seamless exchange of all information between related stakeholders using digital technologies. The ultimate evolution of the methodology, BIM Maturity Level 3, envisions interoperable, distributed, web-based, interdisciplinary information exchange among stakeholders across the life-cycle of buildings. The World Wide Web Consortium Linked Building Data Community Group (W3C LBD-CG) hypothesises that the Linked Data models and best practices can be leveraged to achieve this vision in modern web-based applications. In this paper, we introduce the Building Topology Ontology (BOT) as a core vocabulary to this approach. It provides a high-level description of the topology of buildings including storeys and spaces, the building elements they contain, and their web-friendly 3D models. We describe how existing applications produce and consume datasets combining BOT with other ontologies that describe product catalogues, sensor observations, or Internet of Things (IoT) devices effectively implementing BIM Maturity Level 3. We evaluate our approach by exporting and querying three real-life large building models.