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- ItemUnveiling Relations in the Industry 4.0 Standards Landscape Based on Knowledge Graph Embeddings(Cham : Springer, 2020) Rivas, Ariam; Grangel-González, Irlán; Collarana, Diego; Lehmann, Jens; Vidal, Maria-Esther; Hartmann, Sven; Küng, Josef; Kotsis, Gabriele; Tjoa, A Min; Khalil, IsmailIndustry 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.
- ItemContext-Based Entity Matching for Big Data(Cham : Springer, 2020) Tasnim, Mayesha; Collarana, Diego; Graux, Damien; Vidal, Maria-Esther; Janev, Valentina; Graux, Damien; Jabeen, Hajira; Sallinger, EmanuelIn the Big Data era, where variety is the most dominant dimension, the RDF data model enables the creation and integration of actionable knowledge from heterogeneous data sources. However, the RDF data model allows for describing entities under various contexts, e.g., people can be described from its demographic context, but as well from their professional contexts. Context-aware description poses challenges during entity matching of RDF datasets—the match might not be valid in every context. To perform a contextually relevant entity matching, the specific context under which a data-driven task, e.g., data integration is performed, must be taken into account. However, existing approaches only consider inter-schema and properties mapping of different data sources and prevent users from selecting contexts and conditions during a data integration process. We devise COMET, an entity matching technique that relies on both the knowledge stated in RDF vocabularies and a context-based similarity metric to map contextually equivalent RDF graphs. COMET follows a two-fold approach to solve the problem of entity matching in RDF graphs in a context-aware manner. In the first step, COMET computes the similarity measures across RDF entities and resorts to the Formal Concept Analysis algorithm to map contextually equivalent RDF entities. Finally, COMET combines the results of the first step and executes a 1-1 perfect matching algorithm for matching RDF entities based on the combined scores. We empirically evaluate the performance of COMET on testbed from DBpedia. The experimental results suggest that COMET accurately matches equivalent RDF graphs in a context-dependent manner.
- ItemOptimizing Federated Queries Based on the Physical Design of a Data Lake(Aachen : RWTH, 2020) Rohde, Philipp D.; Vidal, Maria-EstherThe optimization of query execution plans is known to be crucial for reducing the query execution time. In particular, query optimization has been studied thoroughly for relational databases over the past decades. Recently, the Resource Description Framework (RDF) became popular for publishing data on the Web. As a consequence, federations composed of different data models like RDF and relational databases evolved. One type of these federations are Semantic Data Lakes where every data source is kept in its original data model and semantically annotated with ontologies or controlled vocabularies. However, state-of-the-art query engines for federated query processing over Semantic Data Lakes often rely on optimization techniques tailored for RDF. In this paper, we present query optimization techniques guided by heuristics that take the physical design of a Data Lake into account. The heuristics are implemented on top of Ontario, a SPARQL query engine for Semantic Data Lakes. Using sourcespecific heuristics, the query engine is able to generate more efficient query execution plans by exploiting the knowledge about indexes and normalization in relational databases. We show that heuristics which take the physical design of the Data Lake into account are able to speed up query processing.
- ItemFederated Query Processing(Cham : Springer, 2020) Endris, Kemele M.; Vidal, Maria-Esther; Graux, Damien; Janev, Valentina; Graux, Damien; Jabeen, Hajira; Sallinger, EmanuelBig data plays a relevant role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Semantic web technologies have also experienced great progress, and scientific communities and practitioners have contributed to the problem of big data management with ontological models, controlled vocabularies, linked datasets, data models, query languages, as well as tools for transforming big data into knowledge from which decisions can be made. Despite the significant impact of big data and semantic web technologies, we are entering into a new era where domains like genomics are projected to grow very rapidly in the next decade. In this next era, integrating big data demands novel and scalable tools for enabling not only big data ingestion and curation but also efficient large-scale exploration and discovery. Federated query processing techniques provide a solution to scale up to large volumes of data distributed across multiple data sources. Federated query processing techniques resort to source descriptions to identify relevant data sources for a query, as well as to find efficient execution plans that minimize the total execution time of a query and maximize the completeness of the answers. This chapter summarizes the main characteristics of a federated query engine, reviews the current state of the field, and outlines the problems that still remain open and represent grand challenges for the area.
- ItemSurvey on Big Data Applications(Cham : Springer, 2020) Janev, Valentina; Pujić, Dea; Jelić, Marko; Vidal, Maria-Esther; Janev, Valentina; Graux, Damien; Jabeen, Hajira; Sallinger, EmanuelThe goal of this chapter is to shed light on different types of big data applications needed in various industries including healthcare, transportation, energy, banking and insurance, digital media and e-commerce, environment, safety and security, telecommunications, and manufacturing. In response to the problems of analyzing large-scale data, different tools, techniques, and technologies have bee developed and are available for experimentation. In our analysis, we focused on literature (review articles) accessible via the Elsevier ScienceDirect service and the Springer Link service from more recent years, mainly from the last two decades. For the selected industries, this chapter also discusses challenges that can be addressed and overcome using the semantic processing approaches and knowledge reasoning approaches discussed in this book.
- ItemFunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation(Cham : Springer, 2020) Jozashoori, Samaneh; Chaves-Fraga, David; Iglesias, Enrique; Vidal, Maria-Esther; Corcho, Oscar; Pan, Jeff Z.; Tamma, Valentina; d'Amato, Claudia; Janowicz, Kryztof; Fu, Bo; Polleres, Axel; Seneviratne, Oshani; Kagal, LalanaData has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions – specified with function-based mapping languages like FunUL and RML+FnO – can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RML-compliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.
- ItemEncoding Knowledge Graph Entity Aliases in Attentive Neural Network for Wikidata Entity Linking(Berlin ; Heidelberg : Springer, 2020) Mulang’, Isaiah Onando; Singh, Kuldeep; Vyas, Akhilesh; Shekarpour, Saeedeh; Vidal, Maria-Esther; Lehmann, Jens; Auer, Sören; Huang, Zhisheng; Beek, Wouter; Wang, Hua; Zhou, Rui; Zhang, YanchunThe collaborative knowledge graphs such as Wikidata excessively rely on the crowd to author the information. Since the crowd is not bound to a standard protocol for assigning entity titles, the knowledge graph is populated by non-standard, noisy, long or even sometimes awkward titles. The issue of long, implicit, and nonstandard entity representations is a challenge in Entity Linking (EL) approaches for gaining high precision and recall. Underlying KG in general is the source of target entities for EL approaches, however, it often contains other relevant information, such as aliases of entities (e.g., Obama and Barack Hussein Obama are aliases for the entity Barack Obama). EL models usually ignore such readily available entity attributes. In this paper, we examine the role of knowledge graph context on an attentive neural network approach for entity linking on Wikidata. Our approach contributes by exploiting the sufficient context from a KG as a source of background knowledge, which is then fed into the neural network. This approach demonstrates merit to address challenges associated with entity titles (multi-word, long, implicit, case-sensitive). Our experimental study shows ≈8% improvements over the baseline approach, and significantly outperform an end to end approach for Wikidata entity linking.
- ItemDigital Transformation of Education Credential Processes and Life Cycles – A Structured Overview on Main Challenges and Research Questions([Wilmington, DE, USA] : IARIA, [2020], 2020) Keck, Ingo R.; Vidal, Maria-Esther; Heller, Lambert; Mikroyannidis, Alexander; Chang, Maiga; White, StephenIn this article, we look at the challenges that arise in the use and management of education credentials, and from the switch from analogue, paper-based education credentials to digital education credentials. We propose a general methodology to capture qualitative descriptions and measurable quantitative results that allow to estimate the effectiveness of a digital credential management system in solving these challenges. This methodology is applied to the EU H2020 project QualiChain use case, where five pilots have been selected to study a broad field of digital credential workflows and credential management. Copyright (c) IARIA, 2020
- ItemCreating and Capturing Artificial Emotions in Autonomous Robots and Software Agents(Cham : Springer, 2020) Hoffmann, Claus; Vidal, Maria-Esther; Bielikova, Maria; Mikkonen, Tommi; Pautasso, CesareThis 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.
- ItemA Knowledge Graph for Industry 4.0(Cham : Springer, 2020) Bader, Sebastian R.; Grangel-Gonzalez, Irlan; Nanjappa, Priyanka; Vidal, Maria-Esther; Maleshkova, Maria; Harth, Andreas; Kirrane, Sabrina; Ngonga Ngomo, Axel-Cyrille; Paulheim, Heiko; Rula, Anisa; Gentile, Anna Lisa; Haase, Peter; Cochez, MichaelOne of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality.