A Knowledge Graph Based Integration Approach for Industry 4.0

The fourth industrial revolution, Industry 4.0 (I40) aims at creating smart factories employing among others Cyber-Physical Systems (CPS), Internet of Things (IoT) and Artificial Intelligence (AI). Realizing smart factories according to the I40 vision requires intelligent human-to-machine and machine-to-machine communication. To achieve this communication, CPS along with their data need to be described and interoperability conflicts arising from various representations need to be resolved. For establishing interoperability, industry communities have created standards and standardization frameworks. Standards describe main properties of entities, systems, and processes, as well as interactions among them. Standardization frameworks classify, align, and integrate industrial standards according to their purposes and features. Despite being published by official international organizations, different standards may contain divergent definitions for similar entities. Further, when utilizing the same standard for the design of a CPS, different views can generate interoperability conflicts. Albeit expressive, standardization frameworks may represent divergent categorizations of the same standard to some extent, interoperability conflicts need to be resolved to support effective and efficient communication in smart factories. To achieve interoperability, data need to be semantically integrated and existing conflicts conciliated. This problem has been extensively studied in the literature. Obtained results can be applied to general integration problems. However, current approaches fail to consider specific interoperability conflicts that occur between entities in I40 scenarios. In this thesis, we tackle the problem of semantic data integration in I40 scenarios. A knowledge graphbased approach allowing for the integration of entities in I40 while considering their semantics is presented. To achieve this integration, there are challenges to be addressed on different conceptual levels. Firstly, defining mappings between standards and standardization frameworks; secondly, representing knowledge of entities in I40 scenarios described by standards; thirdly, integrating perspectives of CPS design while solving semantic heterogeneity issues; and finally, determining real industry applications for the presented approach. We first devise a knowledge-driven approach allowing for the integration of standards and standardization frameworks into an Industry 4.0 knowledge graph (I40KG). The standards ontology is used for representing the main properties of standards and standardization frameworks, as well as relationships among them. The I40KG permits to integrate standards and standardization frameworks while solving specific semantic heterogeneity conflicts in the domain. Further, we semantically describe standards in knowledge graphs. To this end, standards of core importance for I40 scenarios are considered, i.e., the Reference Architectural Model for I40 (RAMI4.0), AutomationML, and the Supply Chain Operation Reference Model (SCOR). In addition, different perspectives of entities describing CPS are integrated into the knowledge graphs. To evaluate the proposed methods, we rely on empirical evaluations as well as on the development of concrete use cases. The attained results provide evidence that a knowledge graph approach enables the effective data integration of entities in I40 scenarios while solving semantic interoperability conflicts, thus empowering the communication in smart factories

Git4Voc: Git-based Versioning for Collaborative Vocabulary Development

Collaborative vocabulary development in the context of data integration is the process of finding consensus between the experts of the different systems and domains. The complexity of this process is increased with the number of involved people, the variety of the systems to be integrated and the dynamics of their domain. In this paper we advocate that the realization of a powerful version control system is the heart of the problem. Driven by this idea and the success of Git in the context of software development, we investigate the applicability of Git for collaborative vocabulary development. Even though vocabulary development and software development have much more similarities than differences there are still important differences. These need to be considered within the development of a successful versioning and collaboration system for vocabulary development. Therefore, this paper starts by presenting the challenges we were faced with during the creation of vocabularies collaboratively and discusses its distinction to software development. Based on these insights we propose Git4Voc which comprises guidelines how Git can be adopted to vocabulary development. Finally, we demonstrate how Git hooks can be implemented to go beyond the plain functionality of Git by realizing vocabulary-specific features like syntactic validation and semantic diffs

Procedimiento para la obtención de un modelo ontológico para representar la información contenida en bases de datos

El presente trabajo propone un procedimiento a través del cual un razonador evalúa la información de una base de datos y la clasifica de forma automática en conceptos, relaciones, roles y atributos. Esta clasificación se desarrolla mediante un procedimiento dividido en dos métodos: primero, un Algoritmo de migración el cual genera una ontología con los elementos del esquema relacional de la base de datos. El segundo método es la Estrategia de clasificación de la información, esta consiste en una serie de consultas SPARQL mediante las que se clasifica la información de la base de datos.---ABSTRACT---This paper proposes a method by which a reasoner evaluates information from a database and automatically classifies in concepts, relationships, roles and attributes. This classification is developed through a procedure divided into two methods: first, a migration algorithm which generates an ontology with elements of relational schema database. The second method is the strategy classification of information, this is a series of SPARQL queries through that classified using the information the database

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

DemoEffTE: A demonstrator of dependency-aware evaluation of test cases over ontology

Traditional approaches, which follow a test-driven development technique, allow a set of test cases to be exhaustively evaluated ensuring that each modification of an ontology does not violate predefined requirements. However, the time required for the evaluation of test cases is high and usually represents a bottleneck in an ontology development process. The EffTE framework tackles this problem; it relies on a graph-based model of the dependencies between test cases to support users during an ontology development process. Traversing the dependency graph is realized using breadth-first search along with a mechanism that tracks tabu test cases, i.e., test cases that will be ignored for further evaluation due to faulty parent test cases. As a result, the number of test cases that are evaluated is minimized, thus reducing the time required for validating an ontology after each modification. We demonstrate the benefits of prioritization and selection of the test cases to be evaluated with DemoEffTE . Attendees will observe the behavior of both a naive approach and the EffTE framework on different configuration settings such as different: (1) ontology size; (2) topology of the dependency graph of the test cases; and (3) number of test cases. The demo is available at: http://vocol.iais.fraunhofer.de/DemoEffTE

SCORVoc: Vocabulary-Based Information Integration and Exchange in Supply Networks

International audienceAdvanced, highly specialized economies require instant, robust and efficient information flows within its value-added and Supply Chain networks. Especially also in the context of the recent Industry 4.0, smart manufacturing or cyber-physical systems initiatives more efficient and effective information exchange in supply networks is of paramount importance. The Supply Chain Operation Reference (SCOR) is a cross-industry approach to lay the groundwork for this goal by defining a conceptual model for Supply Chain related information. Semantics-based approaches could facilitate information flows in supply networks, and enable to analyze, monitor and optimize Supply Chains (in particular for robustness). This paper first reviews existing formalizations of the Supply Chain Council's SCOR standard. It then introduces the SCORVoc RDFS vocabulary which fully formalizes the latest SCOR standard, while over-coming the identified limitations of existing work. SCORVoc is operationalized by a set of SPARQL queries, that enable to evaluate metrics and key performance indicator (KPIs) defined by SCOR, on-the-fly, in an information systems that adheres to the vocabulary. Finally, we define concrete test scenarios and implement a synthetic benchmark to demonstrate the practicality of SCORVoc