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Unveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings
Industry 4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of empowering interoperability in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans∗ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.
Quality evaluation of open educational resources
Open Educational Resources (OER) are free and open-licensed educational materials widely used for learning. OER quality assessment has become essential to support learners and teachers in finding high-quality OERs, and to enable online learning repositories to improve their OERs. In this work, we establish a set of evaluation metrics that assess OER quality in OER authoring tools. These metrics provide guidance to OER content authors to create high-quality content. The metrics were implemented and evaluated within SlideWiki, a collaborative OpenCourseWare platform that provides educational materials in presentation slides format. To evaluate the relevance of the metrics, a questionnaire is conducted among OER expert users. The evaluation results indicate that the metrics address relevant quality aspects and can be used to determine the overall OER quality.
An Approach to Evaluate User Interfaces in a Scholarly Knowledge Communication Domain
The amount of research articles produced every day is overwhelming: scholarly knowledge is getting harder to communicate and easier to get lost. A possible solution is to represent the information in knowledge graphs: structures representing knowledge in networks of entities, their semantic types, and relationships between them. But this solution has its own drawback: given its very specific task, it requires new methods for designing and evaluating user interfaces. In this paper, we propose an approach for user interface evaluation in the knowledge communication domain. We base our methodology on the well-established Cognitive Walkthough approach but employ a different set of questions, tailoring the method towards domain-specific needs. We demonstrate our approach on a scholarly knowledge graph implementation called Open Research Knowledge Graph (ORKG).
Creating and Capturing Artificial Emotions in Autonomous Robots and Software Agents
This paper presents ARTEMIS, a control system for autonomous robots or software agents. ARTEMIS is able to create and capture artificial emotions during interactions with its environment, and we describe the underlying mechanisms for this. The control system also realizes the capturing of knowledge about its past artificial emotions. A specific interpretation of a knowledge graph, called an Agent Knowledge Graph, represents these artificial emotions. For this, we devise a formalism which enriches the traditional factual knowledge in knowledge graphs with the representation of artificial emotions. As proof of concept, we realize a concrete software agent based on the ARTEMIS control system. This software agent acts as a user assistant and executes the user’s orders. The environment of this user assistant consists of autonomous service agents. The execution of user’s orders requires interaction with these autonomous service agents. These interactions lead to artificial emotions within the assistant. The first experiments show that it is possible to realize an autonomous agent with plausible artificial emotions with ARTEMIS and to record these artificial emotions in its Agent Knowledge Graph. In this way, autonomous agents based on ARTEMIS can capture essential knowledge that supports successful planning and decision making in complex dynamic environments and surpass emotionless agents.