We investigate the use of Web agents that use planning to achieve goals involving preconditions on the dynamic Web. We implement our approach, AEPIS, based on Semantic Web technologies and rely on environmental information evaluated exclusively at runtime to take dynamic changes into account. We evaluate AEPIS in static and dynamic environments, based on the Web of Things, and compare its performance to a rule-based and an agent using large language models. We find that AEPIS combines adaptivity with rapid planning at scale, even in a dynamic environment.

This article introduces foundational components for decentralized B2B data sharing based on the solid protocol, emphasizing data sovereignty, security, and interoperability. These components are: (1) Authorization app (AuthApp) – facilitating granular control and compliance in access granting and revocation processes; (2) rights delegation proxy (RDP) – supporting controlled delegation of rights, enabling natural persons to act on behalf of organizations while ensuring privacy and traceability; (3) data provisioning proxy (DPP) – allowing seamless and secure data provisioning across organizations while masking the identity of upstream data sources to protect business interests. The components enable the creation of end-to-end, standards-based, flexible data value chains. We validate their applicability through a real-world financial services use case involving loan processing, which illustrates data sharing and protection challenges in B2B ecosystems.

Many current web pages include structured data which can directly be processed and used. Search engines, in particular, gather that structured data and provide question answering capabilities over the integrated data with an entity-centric presentation of the results. Due to the decentralized nature of the web, multiple structured data sources can provide similar information about an entity. But data from different sources may involve different vocabularies and modeling granularities, which makes integration difficult. We present FusE, an approach that identifies similar entity-specific data across sources, independent of the vocabulary and data modeling choices. We apply our method along the scenario of a trustable knowledge panel, conduct experiments in which we identify and process entity data from web sources, and compare the output to a competing system. The results underline the advantages of the presented entity-centric data fusion approach.

We present an ontology for representing workflows over components with Read-Write Linked Data interfaces and give an operational semantics to the ontology via a rule language. Workflow languages have been successfully applied for modelling behaviour in enterprise information systems, in which the data is often managed in a relational database. Linked Data interfaces have been widely deployed on the web to support data integration in very diverse domains, increasingly also in scenarios involving the Internet of Things, in which application behaviour is often specified using imperative programming languages. With our work we aim to combine workflow languages, which allow for the high-level specification of application behaviour by non-expert users, with Linked Data, which allows for decentralised data publication and integrated data access. We show that our ontology is expressive enough to cover the basic workflow patterns and demonstrate the applicability of our approach with a prototype system that observes pilots carrying out tasks in a mixed-reality aircraft cockpit. On a synthetic benchmark from the building automation domain, the runtime scales linearly with the size of the number of Internet of Things devices.

The vision of the Internet of Things (IoT) promises the capability of connecting billions of devices, resources and things together. In the realisation of this vision, we are currently neglecting the interoperability between devices that is caused by a heterogeneous landscape of things and which leads to the proliferation of isolated islands of custom IoT solutions. A first step towards enabling some interoperability is to connect things to the Web and to use the Web stack, thereby conceiving the socalled Web of Things (WoT). However, even when a homogeneous access is reached through Web protocols, a common understanding is still missing. In addition, decentralised applications, advocated by the IoT vision, and a-priori unknown requirements of specific integration scenarios demand new concepts for the adaptation of things at runtime. Our work focuses on two main aspects: overcoming not only data but also device and interface heterogeneity, and enabling adaptable and scalable decentralised WoT applications. To this end we present an approach for realising decentralised WoT applications based on three main building blocks: 1) semantics of the devices' capabilities and interfaces, 2) rules to enable embedding controller logic within device's interfaces for supporting a decentralised applications, and 3) support for reconfiguring the controller logic at runtime for customising and adapting the application. We show how our approach can be applied by introducing a reference architecture, provide a thorough evaluation in terms of a proof-of-concept implementation of an example use case, and performance tests.

With the rise of the Semantic Web more and more data become available encoded using the Semantic Web standard RDF. RDF is faced towards machines: de-signed to be easily processable by ma-chines it is difficult to be understood by casual users. Transforming RDF data into human-comprehensible text would facil-itate non-experts to assess this informa-tion. In this paper we present a language-independent method for extracting RDF verbalization templates from a parallel corpus of text and data. Our method is based on distant-supervised simultaneous multi-relation learning and frequent maxi-mal subgraph pattern mining. We demon-strate the feasibility of our method on a parallel corpus of Wikipedia articles and DBpedia data for English and German. 1

The Smart Grid aims at making the current electricity grid more efficient, featuring an IT-layer that includes communication flows between a multitude of stakeholders. Its infrastructure is likely to be integrated with other IT-based systems. We argue that in order to enable the integration between already existing large-scale information networks (e.g., the Internet or the Web) and newly developed IT infrastructures such as Smart Cities, means for machine-understandable representation of data are required. Since these “smart” systems will exchange large volumes of highly sensitive data (billing and personal data), users should have the ability to specify their intent on how their data can be shared. Thus, systems require built-in mechanisms for protecting data. In this paper, we outline a Smart Grid architecture based on Semantic Web (Linked Data) technologies, and present mechanisms to allow for automated access control and enforcement of privacy rights on a technical level.

An increasing amount of applications build their functionality on the utilisation and manipulation of web resources. Consequently REST gains popularity with a resource-centric interaction architecture that draws its flexibility from links between resources. Linked Data offers a uniform data model for REST with self-descriptive resources that can be leveraged to avoid a manual ad-hoc development of web-based applications. For declaratively specifying interactions between web resources we introduce Data-Fu, a lightweight declarative rule language with state transition systems as formal grounding. Data-Fu enables the development of data-driven applications that facilitate the RESTful manipulation of read/write Linked Data resources. Furthermore, we describe an interpreter for Data-Fu as a general purpose engine that allows to perform described interactions with web resources by orders of magnitude faster than a comparable Linked Data processor.

The advent of the Web of Data kindled interest in link-traversal (or lookup-based) query processing methods, with which queries are answered via dereferencing a potentially large number of small, interlinked sources. While several algorithms for query evaluation have been proposed, there exists no notion of completeness for results of so-evaluated queries. In this paper, we motivate the need for clearly-defined completeness classes and present several notions of completeness for queries over Linked Data, based on the idea of authoritativeness of sources, and show the relation between the different completeness classes.

Linked Data refers to data published in accordance with a number of principles rooted in web standards. In the past few years we have witnessed a tremendous growth in Linked Data publishing on the web, leading to tens of billions of data items published online. Querying the data is a key functionality required to make use of the wealth of rich interlinked data. The goal of the tutorial is to introduce, motivate, and detail techniques for querying heterogeneous structured data from across the web. Our tutorial aims to introduce database researchers and practitioners to the new publishing paradigm on the web, and show how the abundance of data published as Linked Data can serve as fertile ground for database research and experimentation. As such, the tutorial focuses on applying database techniques to processing Linked Data, such as optimized indexing and query processing methods in the centralized setting as well as distributed approaches for querying. At the same time, we make the connection from Linked Data best practices to established technologies in distributed databases and the concept of Dataspaces and show differences as well as commonalities between the fields.

Typical approaches for querying structured Web Data collect (crawl) and pre-process (index) large amounts of data in a central data repository before allowing for query answering. However, this time-consuming pre-processing phase however leverages the benefits of Linked Data -- where structured data is accessible live and up-to-date at distributed Web resources that may change constantly -- only to a limited degree, as query results can never be current. An ideal query answering system for Linked Data should return current answers in a reasonable amount of time, even on corpora as large as the Web. Query processors evaluating queries directly on the live sources require knowledge of the contents of data sources. In this paper, we develop and evaluate an approximate index structure summarising graph-structured content of sources adhering to Linked Data principles, provide an algorithm for answering conjunctive queries over Linked Data on theWeb exploiting the source summary, and evaluate the system using synthetically generated queries. The experimental results show that our lightweight index structure enables complete and up-to-date query results over Linked Data, while keeping the overhead for querying low and providing a satisfying source ranking at no additional cost.

In this article the authors discuss the challenges of performing reasoning on large scale RDF datasets from the Web. Using ter-Horst’s pD* fragment of OWL as a base, the authors compose a rule-based framework for application to web data: they argue their decisions using observations of undesirable examples taken directly from the Web. The authors further temper their OWL fragment through consideration of “authoritative sources” which counter-acts an observed behaviour which they term “ontology hijacking”: new ontologies published on the Web re-defining the semantics of existing entities resident in other ontologies. They then present their system for performing rule-based forward-chaining reasoning which they call SAOR: Scalable Authoritative OWL Reasoner. Based upon observed characteristics of web data and reasoning in general, they design their system to scale: the system is based upon a separation of terminological data from assertional data and comprises of a lightweight in-memory index, on-disk sorts and file-scans. The authors evaluate their methods on a dataset in the order of a hundred million statements collected from real-world Web sources and present scale-up experiments on a dataset in the order of a billion statements collected from the Web.

Search engines focusing on particular media types face difficulties in discovering suitable URIs on the Web. Since the engines are only interested in a small fraction of the Web, a crawler should use heuristics to concentrate on that fraction. To devise such a heuristic, we postulate four hypotheses based on RFCs and W3C recommendations to find cues for certain content types. Tests on a corpus of 22m files (793GB content size) containing 630m URIs show that for the content types text, image, and application, the recommendations are mostly being followed, while results for audio and video are much less consistent. Our findings and recommendations can be implemented as heuristics for efficient discovery of structured content on the Web on top of existing crawlers.

Publishing information on the Semantic Web using common formats enables data to be linked together, integrated and reused. In order to fully leverage the potential for interlinking data by reusing existing schemas, an intuitive way of viewing current usage of RDF vocabularies is required. We present a system which allows a user to view the most frequently occurring namespaces and classes in a large Semantic Web dataset, and the main linkage patterns that exist between them. Users can select a namespace of interest in order to examine usage of a particular ontology, and see how it is being combined with other vocabularies.

Current search engines do not fully leverage semantically rich datasets, or specialise in indexing just one domain-specific dataset.We present a search engine that uses the RDF data model to enable interactive query answering over richly structured and interlinked data collected from many disparate sources on the Web.

In this poster we discuss a graphical notation for representing queries for semistructured data. We try to strike a balance between expressiveness of the query language and simplicity and understandability of the graphical notation. We present the primitives of the notation by means of examples.