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End date: 2024 × Subject: Semantic Web ×

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Now showing 1 - 5 of 5
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    Semantic Representation of Physics Research Data
    (Setúbal, Portugal : Science and Technology Publications, Lda, 2020) Say, Aysegul; Fathalla, Said; Vahdati, Sahar; Lehmann, Jens; Auer, Sören; Aveiro, David; Dietz, Jan; Filipe, Joaquim
    Improvements in web technologies and artificial intelligence enable novel, more data-driven research practices for scientists. However, scientific knowledge generated from data-intensive research practices is disseminated with unstructured formats, thus hindering the scholarly communication in various respects. The traditional document-based representation of scholarly information hampers the reusability of research contributions. To address this concern, we developed the Physics Ontology (PhySci) to represent physics-related scholarly data in a machine-interpretable format. PhySci facilitates knowledge exploration, comparison, and organization of such data by representing it as knowledge graphs. It establishes a unique conceptualization to increase the visibility and accessibility to the digital content of physics publications. We present the iterative design principles by outlining a methodology for its development and applying three different evaluation approaches: data-driven and criteria-based evaluation, as well as ontology testing.
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    Compacting frequent star patterns in RDF graphs
    (Dordrecht : Springer Science + Business Media B.V, 2020) Karim, Farah; Vidal, Maria-Esther; Auer, Sören
    Knowledge graphs have become a popular formalism for representing entities and their properties using a graph data model, e.g., the Resource Description Framework (RDF). An RDF graph comprises entities of the same type connected to objects or other entities using labeled edges annotated with properties. RDF graphs usually contain entities that share the same objects in a certain group of properties, i.e., they match star patterns composed of these properties and objects. In case the number of these entities or properties in these star patterns is large, the size of the RDF graph and query processing are negatively impacted; we refer these star patterns as frequent star patterns. We address the problem of identifying frequent star patterns in RDF graphs and devise the concept of factorized RDF graphs, which denote compact representations of RDF graphs where the number of frequent star patterns is minimized. We also develop computational methods to identify frequent star patterns and generate a factorized RDF graph, where compact RDF molecules replace frequent star patterns. A compact RDF molecule of a frequent star pattern denotes an RDF subgraph that instantiates the corresponding star pattern. Instead of having all the entities matching the original frequent star pattern, a surrogate entity is added and related to the properties of the frequent star pattern; it is linked to the entities that originally match the frequent star pattern. Since the edges between the entities and the objects in the frequent star pattern are replaced by edges between these entities and the surrogate entity of the compact RDF molecule, the size of the RDF graph is reduced. We evaluate the performance of our factorization techniques on several RDF graph benchmarks and compare with a baseline built on top gSpan, a state-of-the-art algorithm to detect frequent patterns. The outcomes evidence the efficiency of proposed approach and show that our techniques are able to reduce execution time of the baseline approach in at least three orders of magnitude. Additionally, RDF graph size can be reduced by up to 66.56% while data represented in the original RDF graph is preserved.
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    Further with Knowledge Graphs. Proceedings of the 17th International Conference on Semantic Systems
    (Berlin : AKA ; Amsterdam : IOS Press, 2021) Alam, Mehwish; Groth, Paul; de Boer, Victor; Pellegrini, Tassilo; Pandit, Harshvardhan J.; Montiel, Elena; Rodríguez-Doncel, Victor; McGillivray, Barbara; Meroño-Peñuela, Albert
    The field of semantic computing is highly diverse, linking areas such as artificial intelligence, data science, knowledge discovery and management, big data analytics, e-commerce, enterprise search, technical documentation, document management, business intelligence, and enterprise vocabulary management. As such it forms an essential part of the computing technology that underpins all our lives today. This volume presents the proceedings of SEMANTiCS 2021, the 17th International Conference on Semantic Systems. As a result of the continuing Coronavirus restrictions, SEMANTiCS 2021 was held in a hybrid form in Amsterdam, the Netherlands, from 6 to 9 September 2021. The annual SEMANTiCS conference provides an important platform for semantic computing professionals and researchers, and attracts information managers, IT­architects, software engineers, and researchers from a wide range of organizations, such as research facilities, NPOs, public administrations and the largest companies in the world. The subtitle of the 2021 conference’s was “In the Era of Knowledge Graphs”, and 66 submissions were received, from which the 19 papers included here were selected following a rigorous single-blind reviewing process; an acceptance rate of 29%. Topics covered include data science, machine learning, logic programming, content engineering, social computing, and the Semantic Web, as well as the additional sub-topics of digital humanities and cultural heritage, legal tech, and distributed and decentralized knowledge graphs. Providing an overview of current research and development, the book will be of interest to all those working in the field of semantic systems.
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    Voraussetzungen und Anwendungspotentiale einer präzisen Sacherschließung aus Sicht der Wissenschaft
    (Zenodo, 2018) Kasprzik, Anna
    Thesen: Intellektuelle und automatisierte Sacherschließung müssen ineinandergreifen – tief erschlossene Kerne, die für eine Skalierung durch automatisierte Methoden optimiert sind. Die Qualität der Sacherschließung in Titeldaten lässt sich durch hochqualitative Normdaten verbessern. Automatisierung: Nachhaltige Mischung von statistischen/heuristischen und semantischen/logischen Methoden. Eine Transformation hin zu interoperablen Semantic-Web-Formaten öffnet Möglichkeiten zur Qualitätssteigerung durch erleichterte Nachnutzung in den Fachcommunities.