Digitalization of knowledge work in communication-intensive domains such as intellectual property protection poses great challenges but also opportunities to improve today’s working environments. The legal domain is strongly characterized by knowledge work, whereby, despite a common legal framework, creativity of individual experts is decisive. This knowledge-intensive work deals with a great amount of data objects, not only as a working basis, but also as a result. While experts heavily follow individual working styles, they still rely on a vast amount of administrative tasks, which are carried out by the supporting staff. These tasks are expected to be performed regularly, reliably and without errors, despite necessary adjustments to the current case and the changing legal framework. Today, knowledge work and administrative tasks are typically supported by different tools that are hardly integrated. Therefore, the tracing of continuous work processes based on exchanged data objects is a great challenge. This traceability is crucial, not only for legal security reasons, but also to enable mining and learning of applicable knowledge about processes. In this paper, we propose a bottom-up approach, which applies a continuously evolving graph of integrated data objects and tasks to model and store static and dynamic aspects of administrative as well as knowledge work, and test the approach in a real-world setting in the domain of intellectual property. We further present initial results of a novel dependency-based mining approach to learn data-dependent task sequences in the graph-based model and discuss several methods for enabling privacy-preserving sharing and mining.

The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Matching methods are of great importance since they allow us to find the pivot points from which an automatic data integration process can be established. Unlike the most recent developments based on deep learning, this study presents our research on the development of new methods for ontology matching that are accurate and interpretable at the same time. For this purpose, we rely on a symbolic regression model specifically trained to find the mathematical expression that can solve the ground truth accurately, with the possibility of being understood by a human operator and forcing the processor to consume as little energy as possible. The experimental evaluation results show that our approach seems to be promising.

The rapidly increasing use of graph databases for a wide variety of applications demands flexible authorization and fine-grained access control at the level of attributes associated with the basic entities (i.e., accessing subject, requested resource, performed action, and environmental conditions) but also the vertices and edges along a particular access path. We present a solution for authorization policy specification and enforcement in a graph database to apply fine-grained path-specific constraints on graph-structured data. Therefore, we extend the well-established declarative policy definition language eXtensible Access Control Markup Language (XACML) and its architecture to describe path patterns and enforce the policies using the standard functional components of XACML. Our approach, XACML for Graph-structured data (XACML4G), defines an extended XACML grammar for the authorization policy and access request. To enforce XACML4G policies, we relied on the extensibility points of the XACML architecture and added proprietary extensions. We show the significance of our approach by means of a demonstration prototype in the university domain. Finally, we provide an initial evaluation of the expressiveness and performance of XACML4G with regard to XACML.

Data generated from the Internet of Things (IoT) devices that are mostly cheap enough for any specific use case. It shows the ability to gather data about the physical environment and to understand real-time context, combining with other heterogeneous data sources such as sensor networks, social media, crowdsource data collections, etc. Data analytics can enable a massive set of new services for IoT applications. The management of data in an ultra-scale network which is continuously expanding leads to concerns in data analytics and management. The researchers have examined the challenge of interoperability of applications and services among IoT applications to address them. The common problems of interoperability come from different levels, from syntactic to semantic. In this paper, we take a broad view of current IoT analytics work where Semantic Web approaches aim to solve the semantic interoperability by exploring recent studies in IoT systems. The paper taxonomized literature based on the interoperability requirement of the IoT system. This study identifies the opportunity resulting from the convergence of the Semantic Web and IoT data analytics.

Digitalisation of knowledge work, especially in communication-intensive domains is one of the greatest challenges, but also one of the greatest opportunities to improve today's working environments. This demands for a flexible system that supports both knowledge intensive creative work and highly individual processes. Smooth integration is hindered by the lack of the task context in knowledge management systems so far. Furthermore, a model to define and handle mental concepts, which are typically evolving during daily work, is missing, to allow for targeted use of appropriate knowledge in process tasks. In this paper, we propose a bottom-up approach to model and store the static and dynamic aspects of knowledge in terms of data objects and tasks that are connected with each other. The proposed solution leverages the flexibility of a graph-based model to enable open and continuously evolving user-centred processes for knowledge work, but also predefined administrative processes. Besides our approach, we show results from testing a prototypical implementation in a real-life setting in the domain of intellectual property management applications.