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- ItemCompact representations for efficient storage of semantic sensor data(Dordrecht : Springer Science + Business Media B.V, 2021) Karim, Farah; Vidal, Maria-Esther; Auer, SörenNowadays, there is a rapid increase in the number of sensor data generated by a wide variety of sensors and devices. Data semantics facilitate information exchange, adaptability, and interoperability among several sensors and devices. Sensor data and their meaning can be described using ontologies, e.g., the Semantic Sensor Network (SSN) Ontology. Notwithstanding, semantically enriched, the size of semantic sensor data is substantially larger than raw sensor data. Moreover, some measurement values can be observed by sensors several times, and a huge number of repeated facts about sensor data can be produced. We propose a compact or factorized representation of semantic sensor data, where repeated measurement values are described only once. Furthermore, these compact representations are able to enhance the storage and processing of semantic sensor data. To scale up to large datasets, factorization based, tabular representations are exploited to store and manage factorized semantic sensor data using Big Data technologies. We empirically study the effectiveness of a semantic sensor’s proposed compact representations and their impact on query processing. Additionally, we evaluate the effects of storing the proposed representations on diverse RDF implementations. Results suggest that the proposed compact representations empower the storage and query processing of sensor data over diverse RDF implementations, and up to two orders of magnitude can reduce query execution time.
- ItemAudio Ontologies for Intangible Cultural Heritage(Bramhall, Stockport ; EasyChair Ltd., 2022-04-12) Tan, Mary Ann; Posthumus, Etienne; Sack, HaraldCultural heritage portals often contain intangible objects digitized as audio files. This paper presents and discusses the adaptation of existing audio ontologies intended for non-cultural heritage applications. The resulting alignment of the German Digital Library-Europeana Data Model (DDB-EDM) with Music Ontology (MO) and Audio Commons Ontology (ACO) is presented.
- 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.
- 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.
- 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.
- ItemConcept for Setting up an LTA Working Group in the NFDI Section "Common Infrastructures"(Zenodo, 2022-04-12) Bach, Felix; Degkwitz, Andreas; Horstmann, Wolfram; Leinen, Peter; Puchta, Michael; Stäcker, ThomasNFDI consortia have a variety of disparate and distributed information infrastructures, many of which are as yet only loosely or poorly connected. A major goal is to create a Research Data Commons (RDC) . The RDC concept1 includes, for example, shared cloud services, an application layer with access to high-performance computing (HPC), collaborative workspaces, terminology services, and a common authentication and authorization infrastructure (AAI). The necessary interoperability of services requires, in particular, agreement on protocols and standards, the specification of workflows and interfaces, and the definition of long-term sustainable responsibilities for overarching services and deliverables. Infrastructure components are often well-tested in NFDI on a domain-specific basis, but are quite heterogeneous and diverse between domains. LTA for digital resources has been a recurring problem for well over 30 years and has not been conclusively solved to date, getting urgency with the exponential growth of research data, whether it involves demands from funders - the DFG requires 10 years of retention - or digital artifacts that must be preserved indefinitely as digital cultural heritage. Against this background, the integration of the LTA into the RDC of the NFDI is an urgent desideratum in order to be able to guarantee the permanent usability of research data. A distinction must be2 made between the archiving of the digital objects as bitstreams (this can be numeric or textual data or complex objects such as models), which represents a first step towards long-term usability, and the archiving of the semantic and software-technical context of the digital original objects, which entails far more effort. Beyond the technical embedding of the LTA in the system environment of a multi-cloud-based infrastructure, a number of technically differentiated requirements of the NFDI's subject consortia are part of the development of a basic service for the LTA and for the re-use of research data.3 The need for funding for the development of a basic LTA service for the NFDI consortia results primarily from the additional costs associated with the technical and organizational development of a cross-NFDI, decentralized network structure for LTA and the sustainable subsequent use of research data. It is imperative that the technical actors are able to act within the network as a technology-oriented community, and that they can provide their own services as part of the support for also within a federated infrastructure. The working group "Long Term Archiving" (LTA) is to develop the requirements of the technical consortia for LTA and, on this basis, strategic approaches for the implementation of a basic service LTA. The working group consists of members of various NFDI consortia covering the humanities, natural science and engineering disciplines and experts from a variety of pertinent infrastructures with strong overall connections to the nestor long-term archiving competence network. The close linkage of NFDI consortia with experienced4 partners in the field of LTA ensures that a) the relevant technical state-of-the-art is present in the group and b) the knowledge of data producers about contexts of origin and data users interact directly. This composition enables the team to take an overarching view that spans the requirements of the disciplines and consortia, also takes into account interdisciplinary needs, and at the same time brings in the existing know-how in the infrastructure sector.
- ItemEnhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV(Amsterdam : IOS Press, 2020) Chaves-Fraga, David; Ruckhaus, Edna; Priyatna, Freddy; Vidal, Maria-Esther; Corchio, OscarOntology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets, either by materializing integrated data into RDF or by performing on-the fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented; thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with a benchmark using the GTFS dataset from the Madrid subway; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of MorphCSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.