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  • Champin, Pierre-Antoine  (2)
  • Borg, Erik
  • Ławrynowicz, Agnieszka
  • 1
    UID:
    b3kat_BV044660371
    Format: 1 Online-Ressource (XIX, 387 Seiten) , Illustrationen, Diagramme
    ISBN: 9783319704074
    Series Statement: Lecture Notes in Computer Science 10577
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-319-70406-7
    Language: English
    Subjects: Computer Science
    RVK:
    RVK:
    Keywords: Semantic Web ; Ontologie ; Maschinelles Lernen ; World Wide Web 2.0 ; Information Retrieval ; Empfehlungssystem ; Linked Data ; Konferenzschrift
    URL: Volltext  (URL des Erstveröffentlichers)
    URL: Volltext  (lizenzpflichtig)
    URL: Cover
    Author information: Hose, Katja
    Author information: Ławrynowicz, Agnieszka
    Author information: Hartig, Olaf
    Author information: Paulheim, Heiko 1977-
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    b3kat_BV047316193
    Format: 1 Online-Ressource , Illustrationen, Diagramme
    ISBN: 9783030773854
    Series Statement: Lecture notes in computer science 12731
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-77384-7
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-3-030-77386-1
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Semantic Web ; Ontologie ; Maschinelles Lernen ; World Wide Web 2.0 ; Information Retrieval ; Empfehlungssystem ; Linked Data ; Konferenzschrift
    URL: Volltext  (URL des Erstveröffentlichers)
    Author information: Hose, Katja
    Author information: Ristoski, Petar 1988-
    Author information: Paulheim, Heiko 1977-
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  • 3
    UID:
    gbv_1759347590
    Format: 1 Online-Ressource (xxi, 738 Seiten) , Illustrationen
    ISBN: 9783030773854
    Series Statement: Lecture notes in computer science 12731
    Content: Ontologies and Reasoning -- Streaming Partitioning of RDF Graphs for Datalog Reasoning -- Parallelised ABox Reasoning and Query Answering with Expressive Description Logics -- Analysing Large Inconsistent Knowledge Graphs using Anti-Patterns -- Processing SPARQL Property Path Queries Online with Web Preemption -- Ontology-Based Map Data Quality Assurance -- Knowledge Graphs (Understanding, Creating, and Exploiting) -- Applying Grammar-based Compression to RDF -- HDT Bitmap Triple Indices for Efficient RDF Data Exploration -- Programming and Debugging with Semantically Lifted States -- Do Embeddings Actually Capture Knowledge Graph Semantics? -- A Semantic Framework to Support AI System Accountability and Audit -- Semantic Data Management, Querying and Distributed Data -- Comparison Table Generation from Knowledge Bases -- Incremental Schema Discovery for RDF Data at Scale -- HTTP Extensions for the Management of Highly Dynamic Data Resources -- Expressibility of OWL Axioms with Patterns -- Data Dynamics, Quality, and Trust -- Refining Transitive and pseudo-Transitive Relations at Web Scale -- Data Reliability and Trustworthiness through Digital Transmission Contracts -- Matching, Integration, and Fusion -- Neural Knowledge Base Repairs -- Natural Language Inference over Tables: Enabling Explainable Data Exploration on Data Lakes -- NLP and Information Retrieval -- Grounding Dialogue Systems via Knowledge Graph Aware Decoding with Pre-trained Transformers -- WEB-SOBA: Word Embeddings-Based Semi-automatic Ontology Building for Aspect-Based Sentiment Classification -- Context Transformer with Stacked Pointer Networks for Conversational Question Answering over Knowledge Graphs -- Machine Learning -- Neural Multi-Hop Reasoning With Logical Rules on Biomedical Knowledge Graphs -- Augmenting Ontology Alignment by Semantic Embedding and Distant Supervision -- Convolutional Complex Knowledge Graph Embeddings -- RETRA: Recurrent Transformers for Learning Temporally Contextualized Knowledge Graph Embeddings -- Injecting Background Knowledge into Embedding Models for Predictive Tasks on Knowledge Graphs -- Science Data and Scholarly Communication -- Structured Semantic Modeling of Scientific Citation Intents -- Discovering Research Hypotheses in Social Science using Knowledge Graph Embeddings -- Problems to Solve Before You Die -- Towards a Linked Open Code -- A Polyvocal and Contextualised Semantic Web -- Resources -- The WASABI dataset: cultural -- RuBQ 2.0: An Innovated Russian Question Answering Dataset -- A Knowledge Organization System for the United Nations Sustainable Development Goals -- RSP4J: An API for RDF Stream Processing -- WasmTree: Web Assembly for the Semantic Web -- ParaQA: A Question Answering Dataset with Paraphrase Responses for Single-Turn Conversation -- kgbench: A Collection of Knowledge Graph Datasets for Evaluating Relational and Multimodal Machine Learning -- The SLOGERT Framework for Automated Log Knowledge Graph Construction -- P2P-O: A Purchase-To-Pay Ontology for Enabling Semantic Invoices -- KOBE: Cloud-native Open Benchmarking Engine for Federated Query Processors -- CSKG: The CommonSense Knowledge Graph -- In-Use Track -- A Knowledge Graph-based Approach for Situation Comprehension in Driving Scenarios -- Pay-as-you-go Population of an Automotive Signal Knowledge Graph.
    Content: This book constitutes the refereed proceedings of the 18th International Semantic Web Conference, ESWC 2021, held virtually in June 2021. The 41 full papers and 2 short papers presented were carefully reviewed and selected from 167 submissions. The papers were submitted to three tracks: the research track, the resource track and the in-use track. These tracks showcase research and development activities, services and applications, and innovative research outcomes making their way into industry. The research track caters to both long-standing and emerging research topics in the form of the following subtracks: ontologies and reasoning; knowledge graphs (understanding, creating, and exploiting); semantic data management, querying and distributed data; data dynamics, quality, and trust; matching, integration, and fusion; NLP and information retrieval; machine learning; science data and scholarly communication; and problems to solve before you die.
    Additional Edition: ISBN 9783030773847
    Additional Edition: ISBN 9783030773861
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030773847
    Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 9783030773861
    Additional Edition: Erscheint auch als Druck-Ausgabe ESWC (18. : 2021 : Online) The semantic web Cham : Springer, 2021 ISBN 9783030773847
    Language: English
    Subjects: Computer Science
    RVK:
    Keywords: Semantic Web ; Ontologie ; Natürliche Sprache ; Information Retrieval ; Konferenzschrift
    Author information: Verborgh, Ruben
    Author information: Hose, Katja
    Author information: Paulheim, Heiko 1977-
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  • 4
    UID:
    edochu_18452_21210
    Format: 1 Online-Ressource (52 Seiten)
    Content: Forest ecosystems fulfill a whole host of ecosystem functions that are essential for life on our planet. However, an unprecedented level of anthropogenic influences is reducing the resilience and stability of our forest ecosystems as well as their ecosystem functions. The relationships between drivers, stress, and ecosystem functions in forest ecosystems are complex, multi-faceted, and often non-linear, and yet forest managers, decision makers, and politicians need to be able to make rapid decisions that are data-driven and based on short and long-term monitoring information, complex modeling, and analysis approaches. A huge number of long-standing and standardized forest health inventory approaches already exist, and are increasingly integrating remote-sensing based monitoring approaches. Unfortunately, these approaches in monitoring, data storage, analysis, prognosis, and assessment still do not satisfy the future requirements of information and digital knowledge processing of the 21st century. Therefore, this paper discusses and presents in detail five sets of requirements, including their relevance, necessity, and the possible solutions that would be necessary for establishing a feasible multi-source forest health monitoring network for the 21st century. Namely, these requirements are: (1) understanding the effects of multiple stressors on forest health; (2) using remote sensing (RS) approaches to monitor forest health; (3) coupling different monitoring approaches; (4) using data science as a bridge between complex and multidimensional big forest health (FH) data; and (5) a future multi-source forest health monitoring network. It became apparent that no existing monitoring approach, technique, model, or platform is sufficient on its own to monitor, model, forecast, or assess forest health and its resilience. In order to advance the development of a multi-source forest health monitoring network, we argue that in order to gain a better understanding of forest health in our complex world, it would be conducive to implement the concepts of data science with the components: (i) digitalization; (ii) standardization with metadata management after the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles; (iii) Semantic Web; (iv) proof, trust, and uncertainties; (v) tools for data science analysis; and (vi) easy tools for scientists, data managers, and stakeholders for decision-making support.
    Content: Peer Reviewed
    In: Basel : MDPI, 10,7
    Language: English
    URL: Volltext  (kostenfrei)
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