Journal of Web Semantics最新文献

Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the $21\mathrm{st}$ century. However, in many applications, users require an explanation of the Artificial Intelligence’s decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.

Following the establishment in 2006 of a representational standard for the computational handling of structures of argumentation, the Argument Interchange Format, it became possible to develop a vision for the coherent integration of multifarious services, components and tools that create, consume, navigate, analyse, evaluate and manipulate arguments and debates. This vision was the Argument Web with theoretical foundations laid by Rahwan et al. (2007), and practical engineering work described by Bex et al. (2013). Over the intervening period, the key challenge has been to demonstrate the practical and societal value of the Argument Web by taking its tools and applications to larger audiences. This paper lays out three approaches by which the Argument Web has been scaled up in this way, each in partnership with the BBC, and each with different kinds of evaluation and impact. Transitioning these technologies to large user groups paves the way for broader-scale uptake of the Argument Web and heralds the translation from lab to real-world application for a substantial research community working in argument technology.

A broad variety of audio content is available online through an increasing number of repositories and platforms. Resources such as music tracks, recorded sounds or instrument samples may be accessed by users for tasks ranging from customised music listening and exploration, to music making and sound design using existing sounds and samples. However, each online repository offers its own API and represents information through its own data model, making it difficult for applications to exploit the plurality of online audio and music content on the web. A crucial step toward integrating audio repositories in a flexible manner is a shared basis for modelling the data therein. This paper describes and extends the Audio Commons Ontology, a common data model designed to integrate existing repositories in the audio media domain. The ontology is designed with the involvement of users through surveys and requirements analyses, and evaluated in-use, by demonstrating how it supports the integration of four relevant repositories with heterogeneous APIs and data models. While this work proves the concept in the audio domain, our proposed methodology may transfer across a broad range of media integration tasks.

While the early phase of the Semantic Web put emphasis on conceptual modeling through ontology classes, and the recent years saw the rise of loosely structured, instance-level knowledge graphs (used even for modeling concepts), in this paper, we focus on a third kind of concept modeling: via code lists, primarily those embedded in ontologies and vocabularies. We attempt to characterize the candidate structures for code lists based on our observations in OWL ontologies. Our main contribution is then an approach implemented as a series of SPARQL queries and a lightweight web application that can be used to browse and detect potential code lists in ontologies and vocabularies, in order to extract and enhance them, and to store them in a stand-alone knowledge base. The application allows inspecting query results coming from the Linked Open Vocabularies catalog dataset. In addition, we describe a complementary bottom-up analysis of potential code lists. We also provide in this paper a demonstration of the dominant nature of embedded codes from the aspect of ontological universals and their alternatives for modeling code lists.

Data streams are becoming omnipresent on the Web. The Stream Reasoning (SR) paradigm, which combines Stream Processing with Semantic Web techniques, has been successful in processing these data streams. The progress in SR research has led to several applications in domains such as the Internet of Things, social media analysis, Smart Cities, and many others. Each of these applications produces and consumes data streams, however, there are no fixed guidelines on how to manage data streams on the Web, as there are for their static counterparts. More specifically, there is no fixed life cycle for Streaming Linked Data (SLD) yet. Tommasini et al. (2020) introduced an initial proposal for a SLD life cycle, however, it has not been verified if the proposed life cycle captures existing applications and no guidelines were given for each step.

In this paper, we survey existing SR applications and identify if the life cycle proposed by Tommasini et al. fully captures the surveyed applications. Based on our analysis, we found that some of the steps needed reordering or being split up. This paper proposes an update of the life cycle and surveys the existing literature for each life cycle step while proposing a number of guidelines and best practices. Compared to the initial proposal by Tommasini et al., we drill down into the details of the processing step which was previously neglected. The updated life cycle and guidelines serves as a blueprint for future SR applications. A life cycle for SLD that allows to efficiently manage data streams on the web, brings us a step closer to the realization of the SR vision.

The query containment problem is a fundamental computer science problem which was originally defined for relational queries. With the growing popularity of the sparql query language, it became relevant and important in this new context: reliable and efficient sparql query containment solvers may have various applications within static analysis of queries, especially in the area of query optimizations and refactoring. In this paper, we present a new approach for solving the query containment problem in sparql. The approach is based on reducing the query containment problem to the satisfiability problem in first order logic. It covers a wide range of the sparql language constructs, including union of conjunctive queries, blank nodes, projections, subqueries, clauses from, filter, optional, graph, etc. It also covers containment under rdf schema entailment regime, and it can deal with the subsumption relation. We describe an implementation of the approach, an open source solver SpeCS and its thorough experimental evaluation on two relevant benchmarks, Query Containment Benchmark and SQCFramework. As a side result, SpeCS identified incorrect test cases within both benchmarks, which were manually checked, confirmed and fixed, resulting in better and more reliable benchmarks. The evaluation also shows that SpeCS is highly efficient and that compared to the state-of-the-art solvers, it gives more precise results in a shorter amount of time. In addition, SpeCS has the highest coverage of the supported language constructs.