The paper presents the BOLD (Buildings on Linked Data) benchmark for Linked Data agents, next to the framework to simulate dynamic Linked Data environments, using which we built BOLD. The BOLD benchmark instantiates the BOLD framework by providing a read-write Linked Data interface to a smart building with simulated time, occupancy movement and sensors and actuators around lighting. On the Linked Data representation of this environment, agents carry out several specified tasks, such as controlling illumination. The simulation environment provides means to check for the correct execution of the tasks and to measure the performance of agents. We conduct measurements on Linked Data agents based on condition-action rules.

A bounded description of a resource is a useful default subgraph that represents relevant information. When accessing RDF knowledge graphs, bounded descriptions are returned for SPARQL DESCRIBE queries or for HTTP GET requests on Linked Data. Concise Bounded Description (CBD) is the de facto default, but only provides conditional reachability of connected resources, limiting its usefulness for knowledge graph traversal. This paper outlines alternative bounded description specifications consisting of chained and parallel combinations of construction steps. Furthermore, characteristics of bounded description specifications are introduced, including ontology awareness, applicability, and reachability. The evaluation challenges the traversal of a knowledge graph with n-ary relations using retrieved bounded descriptions. Results confirm limitations of the CBD specification, highlight the impact of reachability, and bring attention to ontology-aware and set-specific bounded descriptions.

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 run-time 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.

We present how data collected from Internet of Things (IoT) devices adhering to the FAIR data principles forms the foundation for data analytics applications at Munich Airport. We describe how the collected IoT data is annotated, how our APIs are structured, present two data analytics applications currently in use to analyze FAIR IoT data for process optimization, and share lessons learned.

The emergence of Large Language Models (LLMs) brought new approaches to Knowledge Graph Question Answering (KGQA), chasing the vision of querying structured data using natural language. While existing work focuses on improving KGQA approaches, this paper explores the impact of different knowledge graph representations. We consider three dimensions of representation: (i) subsets, (ii) modeling, and (iii) annotations, hypothesizing that different variations impact the F1 scores of KGQA systems. We conduct experiments on a custom knowledge graph featuring integrated data and n-ary relations. Results demonstrate an improvement in the F1 score from 17.6

Event data preparation is a critical yet time-consuming phase in process mining projects, often slowed down by complex relational data models and a lack of domain knowledge. This paper presents EVErPREP, a novel workflow model that leverages Event Knowledge Graphs to enhance event data preparation for event logs. EVErPREP uses Semantic Web technologies to improve the exploration, extraction, and processing of event data, ultimately improving the quality and interpretability of event data and event logs. The approach is evaluated through a case study at Munich Airport’s Baggage Handling System, demonstrating its effectiveness in reducing complexity and improving explainability in event data preparation. By providing a more structured and semantically enriched foundation for process mining, EVErPREP showcases increased efficiency and effectiveness of process mining projects through a semantically enriched foundation.

While wearables generate valuable health data, proprietary ecosystems limit interoperability and user control. We address this challenge with a user-friendly Android application that seamlessly collects data from diverse wearables via the Web of Things (WoT), converts the collected data into interoperable RDF using the SOSA/SSN ontology, and stores RDF in user-controlled Solid servers. Unlike existing solutions, our approach includes mapping the data to established ontologies and provides a user interface, empowering everyday users to explore their health data through interactive visualizations. We showcase the application's functionalities through live demonstrations - code, demo videos, and an installable apk are publicly available at https://github. com/derwehr/WoT-Solid/.

Computer-Using Agents (CUA) enable users to automate increasingly-complex tasks using graphical interfaces such as browsers. As many potential tasks require personal data, we propose Computer-Using Personal Agents (CUPAs) that have access to an external repository of the user's personal data. Compared with CUAs, CUPAs offer users better control of their personal data, the potential to automate more tasks involving personal data, better interoperability with external sources of data, and better capabilities to coordinate with other CUPAs in order to solve collaborative tasks involving the personal data of multiple users.

In this paper, we present a processing pipeline for transforming natural language annotations in RDF graphs into machine-readable and interoperable semantic annotations. The pipeline uses Named Entity Recognition (NER) and Entity Linking (EL) techniques based on a foundational Large Language Model (LLM), combined with a Knowledge Graph (KG) based knowledge injection approach for entity disambiguation and self-verification. Through a running example in the paper, we demonstrate that the pipeline can increase the number of semantic annotations in an RDF graph derived from information contained in natural language annotations. The evaluation of the proposed pipeline shows that the LLM-based NER approach produces results comparable to those of fine-tuned NER models. Furthermore, we show that the pipeline using a chain-of-thought prompting style with factual information retrieved via link traversal from an external KG achieves better entity disambiguation and linking than both a pipeline without chain-of-thought prompting and an approach relying only on information within the LLM.

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.

This is a summary of the First International Workshop on Leveraging Semantics for Transparency in Industrial Systems co-located with the 21st International Conference on Semantic Systems (SEMANTICS)

The Internet of Things has created the need for scalable, distributed detection of complex events across organizational boundaries. We present a RESTful architecture that enables distributed detection of complex events on streams of Linked Data. Our approach transforms declarative event patterns expressed in a DatalogMTL-based temporal logic formalism into a network of stream containers and reasoning agents that can operate across organizational boundaries. Key contributions include: (1) A modular architecture based on the Linked Data Platform for federated stream processing, (2) A method for transforming declarative patterns into executable components, (3) A formal model using Colored Stochastic Petri Nets to validate correctness and analyze performance, and (4) an implementation and experimental validation of our approach. Experimental results demonstrate that our system achieves high throughput through parallel processing while maintaining a predictable latency that scales linearly with program depth.

Purpose: Existing approaches for generating Knowledge Graphs (KGs) from file-based, non-RDF data using declarative mappings are either limited by language-specific engines or lack optimization with language-independent relational algebra backends, resulting in suboptimal performance. This research proposes an integrated framework that tightly couples logical and physical plan optimizations, enabling high-performance, language-agnostic RDF graph construction. Methodology: We formalize the KG construction process using relational algebra with a dedicated RDF term generation function within the projection operator, resulting in one of two canonicalized logical plans, one with a join and one without. We then introduce tightly coupled physical operators used to define concrete execution pipelines. We propose and evaluate two optimizations, logical-level constant-folding to reduce redundant computations and a physical-level heuristic scheduling strategy to optimize concurrent execution. We implemented the optimizations in a new backend engine called konverter and benchmarked the engine with an RML frontend against two comparable engines, Morph-KGC and FlexRML. Findings: Empirical results show that constant-folding improves performance by approximately 7.4% and heuristic scheduling by approximately 14.7% compared to a worst case scenario, with minimal additional memory overhead. Overall, konverter outperforms the current state-of-the-art in performance FlexRML, reducing execution time by 61.5% and peak memory usage by 25.1%, though it currently only supports CSV files, a limitation we aim to address in future work. Value: The proposed framework and optimizations provide a formal and practically validated approach to optimizing the execution of declarative mappings for KG construction. The konverter engine demonstrates the potential for building high-performance, language-agnostic engines for enterprise KG construction.

This work evaluates the capabilities of Large Language Models (LLMs) in semantic reasoning tasks. We construct a knowledge graph that represents a real-world Internet of Things (IoT) environment and define various reasoning rules for device identification use cases. We test the performance of three LLMs: Llama-3.1, Qwen-2.5, GPT4o, based on different levels of fine-grained rule descriptions in the input context and the increasing rule complexity. We use a rule-based reasoner, Apache Jena, to generate the ground truth data for each reasoning rule. The results show that while LLMs are effective in retrieving direct links in the knowledge graph, they struggle with highly customized, complex reasoning rules involving multiple triple patterns. This work provides an application and a baseline for using LLMs to enable semantic reasoning for device identification in IoT environments.

We investigate the adaption of agents using plans on the Web despite its large and dynamic nature, as well as agents' constrained perception. Based on Semantic Web technologies and affordances, we compare how agents choose appropriate actions to adapt to their environment by condition-action rules or suggested actions of large language models. We conduct experiments on execution cost and plan stability distance to see whether agents choose appropriate actions to adapt their plans. We find that cost and stability of rule-based and LLMs for adaptation with affordances are close together, while performance differs greatly.