Semantic Web: – Interoperability,Usability,Applicability

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 [Web Semantics: Science, Services and Agents on the World Wide Web17 (2012), 25–32] 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.

Measurement units and their relations like conversions or quantity kinds play an important role in many applications. Thus, many ontologies covering this area have been developed. Consequently, for new projects aiming at reusing one of these ontologies, the process of evaluating them has become more and more time consuming and cumbersome. We evaluated eight well known ontologies for measurement units and the relevant parts of the Wikidata corpus. We automatically collected descriptive statistics about the ontologies and scanned them for potential errors, using an extensible collection of scripts. The computational results were manually reviewed, which uncovered several issues and misconceptions in the examined ontologies. The issues were reported to the ontology authors. This caused new bugfix releases in three cases.

In this paper we will present the evaluation results including statistics as well as an overview of detected issues. We thereby want to enable a well-founded decision upon the unit ontology to use. Further, we hope to prevent errors in the future by describing some pitfalls in ontology development – not limited to the domain of measurement units.

The field of Complex Event Processing (CEP) deals with the techniques and tools developed to efficiently process pattern-based queries over data streams. The Semantic Web, through its standards and technologies, is in constant pursuit to provide solutions for such paradigm while employing the RDF data model. The integration of Semantic Web technologies in this context can handle the heterogeneity, integration and interpretation of data streams at the semantic level. In this paper, we propose and implement a new query language, called SPAseq, that extends SPARQL with new Semantic Complex Event Processing (SCEP) operators that can be evaluated over RDF graph-based events. The novelties of SPAseq include (i) the separation of general graph pattern matching constructs and temporal operators; (ii) the support for RDF graph-based events and multiple RDF graph streams; (iii) the expressibility of temporal operators such as Kleene+, conjunction, disjunction and event selection strategies; and (iv) the operators to integrate background information and streaming RDF graph streams. Hence, SPAseq enjoys good expressiveness compared with the existing solutions. Furthermore, we provide an efficient implementation of SPAseq using a non-deterministic automata (NFA) model for an efficient evaluation of the SPAseq queries. We provide the syntax and semantics of SPAseq and based on this, we show how it can be implemented in an efficient manner. Moreover, we also present an experimental evaluation of its performance, showing that it improves over state-of-the-art approaches.

Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. Spatial data on the Web is becoming ubiquitous and voluminous with the rapid growth of location-based services, spatial technologies, dynamic location-based data and services published by different organizations. However, the heterogeneity and the peculiarities of spatial data, such as the use of different coordinate reference systems, make it difficult for data users, Web applications, and services to discover, interpret and use the information in the large and distributed system that is the Web. To make spatial data more effectively available, this paper summarizes the work of the joint W3C/OGC Working Group on Spatial Data on the Web that identifies 14 best practices for publishing spatial data on the Web. The paper extends that work by presenting the identified challenges and rationale for selection of the recommended best practices, framed by the set of principles that guided the selection. It describes best practices that are employed to enable publishing, discovery and retrieving (querying) spatial data on the Web, and identifies some areas where a best practice has not yet emerged.

One of the major issues encountered in the generation of knowledge bases is the integration of data coming from a collection of heterogeneous data sources. A key essential task when integrating data instances is the entity matching. Entity matching is based on the definition of a similarity measure among entities and on the classification of the entity pair as a match if the similarity exceeds a certain threshold. This parameter introduces a trade-off between the precision and the recall of the algorithm, as higher values of the threshold lead to higher precision and lower recall, and lower values lead to higher recall and lower precision. In this paper, we propose a stacking approach for threshold-based classifiers. It runs several instances of classifiers corresponding to different thresholds and use their predictions as a feature vector for a supervised learner. We show that this approach is able to break the trade-off between the precision and recall of the algorithm, increasing both at the same time and enhancing the overall performance of the algorithm. We also show that this hybrid approach performs better and is less dependent on the amount of available training data with respect to a supervised learning approach that directly uses properties’ similarity values. In order to test the generality of the claim, we have run experimental tests using two different threshold-based classifiers on two different data sets. Finally, we show a concrete use case describing the implementation of the proposed approach in the generation of the 3cixty Nice knowledge base.

In this paper we are concerned with developing information extraction models that support the extraction of common sense knowledge from a combination of unstructured and semi-structured datasets. Our motivation is to extract manipulation-relevant knowledge that can support robots’ action planning. We frame the task as a relation extraction task and, as proof-of-concept, validate our method on the task of extracting two types of relations: locative and instrumental relations. The locative relation relates objects to the prototypical places where the given object is found or stored. The second instrumental relation relates objects to their prototypical purpose of use. While we extract these relations from text, our goal is not to extract specific textual mentions, but rather, given an object as input, extract a ranked list of locations and uses ranked by ‘prototypicality’. We use distributional methods in embedding space, relying on the well-known skip-gram model to embed words into a low-dimensional distributional space, using cosine similarity to rank the various candidates. In addition, we also present experiments that rely on the vector space model NASARI, which compute embeddings for disambiguated concepts and are thus semantically aware. While this distributional approach has been published before, we extend our framework by additional methods relying on neural networks that learn a score to judge whether a given candidate pair actually expresses a desired relation. The network thus learns a scoring function using a supervised approach. While we use a ranking-based evaluation, the supervised model is trained using a binary classification task. The resulting score from the neural network and the cosine similarity in the case of the distributional approach are both used to compute a ranking.

We compare the different approaches and parameterizations thereof on the task of extracting the above mentioned relations. We show that the distributional similarity approach performs very well on the task. The best performing parameterization achieves an NDCG of 0.913, a Precision@1 of 0.400 and a Precision@3 of 0.423. The performance of the supervised learning approach, in spite of having being trained on positive and negative examples of the relation in question, is not as good as expected and achieves an NCDG of 0.908, a Precision@1 of 0.454 and a Precision@3 of 0.387, respectively.

Knowledge bases are in widespread use for aiding tasks such as information extraction and information retrieval, for example in Web search. However, knowledge bases are known to be inherently incomplete, where in particular tail entities and properties are under-represented. As a complimentary data source, embedded entity markup based on Microdata, RDFa, and Microformats have become prevalent on the Web and constitute an unprecedented source of data with significant potential to aid the task of knowledge base augmentation (KBA). RDF statements extracted from markup are fundamentally different from traditional knowledge graphs: entity descriptions are flat, facts are highly redundant and of varied quality, and, explicit links are missing despite a vast amount of coreferences. Therefore, data fusion is required in order to facilitate the use of markup data for KBA. We present a novel data fusion approach which addresses these issues through a combination of entity matching and fusion techniques geared towards the specific challenges associated with Web markup. To ensure precise and non-redundant results, we follow a supervised learning approach based on a set of features considering aspects such as quality and relevance of entities, facts and their sources. We perform a thorough evaluation on a subset of the Web Data Commons dataset and show significant potential for augmenting existing knowledge bases. A comparison with existing data fusion baselines demonstrates superior performance of our approach when applied to Web markup data.

Novel Internet of Things (IoT) applications and services rely on an intelligent understanding of the environment leveraging data gathered via heterogeneous sensors and micro-devices. Though increasingly effective, Machine Learning (ML) techniques generally do not go beyond classification of events with opaque labels, lacking machine-understandable representation and explanation of taxonomies. This paper proposes a framework for semantic-enhanced data mining on sensor streams, amenable to resource-constrained pervasive contexts. It merges an ontology-based characterization of data distributions with non-standard reasoning for a fine-grained event detection. The typical classification problem of ML is treated as a resource discovery by exploiting semantic matchmaking. Outputs of classification are endowed with computer-processable descriptions in standard Semantic Web languages, while explanation of matchmaking outcomes motivates confidence on results. A case study on road and traffic analysis has allowed to validate the proposal and achieve an assessment with respect to state-of-the-art ML algorithms.