
The Semantic Web
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This book constitutes the refereed proceedings of the 16th International Semantic Web Conference, ESWC 2019, held in Portoroz, Slovenia.
The 39 revised full papers presented were carefully reviewed and selected from 134 submissions. The papers are organized in three tracks: research track, resources track, and in-use track and deal with the following topical areas: distribution and decentralisation, velocity on the Web, research of research, ontologies and reasoning, linked data, natural language processing and information retrieval, semantic data management and data infrastructures, social and human aspects of the Semantic Web, and, machine learning.More details
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Persons
Content
- Intro
- Preface
- Organization
- Sponsors
- Contents
- Research Track
- A Decentralized Architecture for Sharing and Querying Semantic Data
- 1 Introduction
- 2 Related Work
- 3 PIQNIC
- 3.1 Data Fragmentation
- 3.2 Network Architecture
- 3.3 Replication of Datasets
- 4 Query Processing
- 5 Evaluation
- 6 Conclusions
- References
- Reformulation-Based Query Answering for RDF Graphs with RDFS Ontologies
- 1 Introduction
- 2 Preliminaries
- 2.1 RDF Graph
- 2.2 RDF Entailment Rules
- 2.3 Basic Graph Pattern Queries
- 3 Prior Related Work
- 4 Extending Query Reformulation to Queries over the Ontology
- 4.1 Overview of Our Query Reformulation Technique
- 4.2 Reformulation Rules Associated with Rc
- 4.3 Reformulation Algorithm Associated with Rc
- 5 Experimental Evaluation
- 6 Conclusion
- References
- A Hybrid Graph Model for Distant Supervision Relation Extraction
- 1 Introduction
- 2 Background
- 2.1 Various Types of Background Information
- 2.2 Graph Convolutional Network
- 3 Methodology
- 3.1 Problem Definition
- 3.2 Overview
- 3.3 Encoders
- 3.4 Graph Convolutional Network with Attention
- 3.5 Optimization
- 4 Experiments
- 4.1 Dataset and Metrics
- 4.2 Experimental Settings
- 4.3 Precision-Recall Curve Comparison
- 4.4 Model Robustness
- 4.5 Case Study
- 5 Related Work
- 5.1 Distant Supervision
- 5.2 Neural Network Methods
- 5.3 Methods with Background Information
- 6 Conclusion and Future Work
- References
- Retrieving Textual Evidence for Knowledge Graph Facts
- 1 Introduction
- 2 Related Work
- 2.1 Passage Retrieval for Knowledge Graph Facts
- 2.2 Word Embeddings for Information Retrieval
- 3 Retrieving Textual Evidence
- 3.1 Exact Matching
- 3.2 Semantic Matching
- 3.3 FacTify Model
- 4 Evaluation
- 4.1 Baselines
- 4.2 Evaluation Setup
- 4.3 Benchmark
- 4.4 Results
- 4.5 Discussion
- 5 Conclusion
- References
- Boosting DL Concept Learners
- 1 Introduction and Motivation
- 2 Related Work
- 3 The Learning Problem
- 4 Boosting a Concept Learner
- 4.1 Basics of Boosting
- 4.2 Learning Confidence-Rated Hypotheses in DL
- 5 Designing a Simple and Fast Weak Learner
- 5.1 wDLF: A Weak Learner Derived from DL-Foil
- 5.2 Heuristics
- 6 Empirical Evaluation
- 6.1 Design and Setup of the Experiments
- 6.2 Aggregate Results and Discussion
- 6.3 Examples of Induced Ensembles
- 7 Conclusions and Outlook
- References
- Link Prediction in Knowledge Graphs with Concepts of Nearest Neighbours
- 1 Introduction
- 2 Related Work
- 3 Preliminaries
- 4 Concepts of Nearest Neighbours (CNN)
- 4.1 Theoretical Definitions
- 4.2 Algorithmic and Practical Aspects
- 5 Link Prediction
- 6 Experiments
- 6.1 Methodology
- 6.2 Results
- 6.3 In-Depth Analysis
- 6.4 Example Inferences and Explanations
- 7 Conclusion
- References
- Disclosing Citation Meanings for Augmented Research Retrieval and Exploration
- 1 Introduction
- 2 Related Work
- 3 Data and Preprocessing Tools
- 3.1 Data
- 3.2 Snippet Extraction
- 3.3 Snippet Linking
- 4 Semantic Analysis
- 4.1 Text Transformation
- 4.2 Syntax-Based Snippet Tokenization
- 4.3 Dimensionality Reduction
- 5 Citation Meanings Extraction
- 5.1 Citation Clustering
- 5.2 Cluster Labeling
- 6 Model and Method Complexity
- 7 Evaluation
- 7.1 Application: Citation Explorer
- 7.2 Reliability of the Extracted Citation Meanings
- 8 Conclusions and Future Work
- References
- Injecting Domain Knowledge in Electronic Medical Records to Improve Hospitalization Prediction
- 1 Introduction
- 2 Related Work
- 3 Enriching Vector Representations of EMRs with Ontological Knowledge
- 3.1 Extraction of Ontological Knowledge from EMRs
- 3.2 Integrating Ontological Knowledge in a Vector Representation
- 4 Experiments and Results
- 4.1 Dataset and Protocol
- 4.2 Selected Machine Learning Algorithms
- 4.3 Results
- 4.4 Discussion
- 5 Conclusion and Future Work
- References
- Explore and Exploit. Dictionary Expansion with Human-in-the-Loop
- 1 Introduction
- 2 Related Work
- 3 Approach
- 3.1 Explore
- 3.2 Exploit
- 4 Evaluation
- 4.1 Dictionary Growth
- 4.2 Impact of the Human-in-the-loop on the Dictionary Growth
- 4.3 Generating ``Future Proof'' Dictionaries
- 5 Conclusions
- References
- Aligning Biomedical Metadata with Ontologies Using Clustering and Embeddings
- 1 Introduction
- 2 Related Work
- 3 Methods
- 4 Results
- 4.1 Clustering Metadata Field Names
- 4.2 Alignments Between Field Names and Ontology Terms
- 5 Evaluation by Expert Panel
- 5.1 Semi-structured Interview Design
- 5.2 Expert Panel Results
- 6 Conclusions
- References
- Generating Semantic Aspects for Queries
- 1 Introduction
- 2 Preliminaries
- 3 Generating Factors
- 4 The xFactor Algorithm
- 5 Properties of the xFactor Algorithm
- 6 Evaluation
- 6.1 Annotated Document Collections
- 6.2 Ground Truth Semantic Aspects and Queries
- 6.3 Measures
- 6.4 Evaluation Setup
- 6.5 Results for Quality
- 6.6 Results for Ranking
- 7 Related Work
- 8 Conclusions
- References
- A Recommender System for Complex Real-World Applications with Nonlinear Dependencies and Knowledge Graph Context
- 1 Introduction
- 2 Background and Mathematical Notation
- 2.1 Notation
- 2.2 Automation Solutions
- 2.3 Knowledge Graphs
- 3 Related Methods
- 3.1 Recommender Systems
- 3.2 Autoencoders
- 4 Our Method - NECTR
- 4.1 Problem Setup
- 4.2 Architecture
- 5 Real-World Experimental Study
- 5.1 Data
- 5.2 Evaluation
- 5.3 Results
- 5.4 Additional Experiments
- 6 Conclusion
- References
- Learning URI Selection Criteria to Improve the Crawling of Linked Open Data
- 1 Introduction
- 2 Related Work
- 3 Preliminary Knowledge
- 4 Prediction Model for Crawling Criteria
- 4.1 Task Description
- 4.2 Feature Extraction
- 4.3 Feature Hashing
- 4.4 Online Prediction
- 5 Implementation of Crawler
- 6 Evaluation
- 6.1 Feature Set Evaluation
- 6.2 Online Versus Offline
- 6.3 Processing Time of Per Selection
- 7 Conclusion
- References
- Deontic Reasoning for Legal Ontologies
- 1 Introduction
- 2 Related Work
- 3 Scope of the Modeling
- 4 Rule Modeling
- 5 Answering Deontic Questions
- 5.1 Drawing a Deontic Conclusion
- 5.2 Building of Pros(Q), Cons(Q), Pros(Q) and Cons(Q)
- 5.3 Exceptions to a Conclusion
- 6 Detailed Examples
- 6.1 Qa Is it Allowed to Navigate Near the PNC for Military Vessels?
- 6.2 Qb Is Any Vessel Allowed to Navigate Near the PNC?
- 6.3 Qc Is it Prohibited to Any Vessel to Navigate Near the PNC?
- 7 Future Work
- 8 Conclusion
- References
- Incorporating Joint Embeddings into Goal-Oriented Dialogues with Multi-task Learning
- 1 Introduction
- 2 Related Work
- 3 Model Description
- 3.1 Attention Based Seq-to-Seq Model
- 3.2 Predicting Intent
- 3.3 Training Joint Text and Knowledge Graph Embeddings
- 3.4 Regularizing Using Additional Entity Loss
- 3.5 Final Objective Function
- 3.6 Key-Value Entity Look-Up
- 4 Experiments
- 4.1 Dataset
- 4.2 Pre-processing and Model Hyperparameters
- 4.3 Results
- 5 Ablation Study
- 6 Qualitative Analysis
- 7 Error Analysis
- 8 Conclusions and Future Work
- References
- Link Prediction Using Multi Part Embeddings
- 1 Introduction
- 2 Background and Related Works
- 2.1 Scoring Functions
- 2.2 Loss Functions
- 2.3 Ranking Evaluation Metrics
- 3 The TriModel Model
- 3.1 Motivation
- 3.2 TriModel Embeddings Interactions
- 3.3 Training the TriModel Model
- 4 Experiments
- 4.1 Data
- 4.2 Implementation
- 4.3 Experiments Setup
- 5 Results and Discussion
- 5.1 Results of the Ranking Loss Configuration
- 5.2 Results of the Multi-class Loss Configuration
- 5.3 Ranking and Multi-class Approaches
- 6 Conclusions and Future Work
- References
- Modelling the Compatibility of Licenses
- 1 Introduction
- 2 Related Work
- 3 CaLi: A Lattice-Based License Model
- 3.1 Formal Model Description
- 3.2 Example 1
- 4 A CaLi Ordering for Creative Commons
- 4.1 Description of a CC Ordering Based on CaLi
- 4.2 Analysis of CC_CaLi
- 5 Implementation of CaLi Orderings
- 5.1 Experimental Validation
- 5.2 A Search Engine Based on an ODRL CaLi Ordering
- 6 Conclusions and Perspectives
- References
- GConsent - A Consent Ontology Based on the GDPR
- 1 Introduction
- 2 Related Work
- 3 Ontology Creation
- 3.1 Methodology
- 3.2 Information Collection and Analysis
- 3.3 Use-Cases and Scenarios
- 3.4 Evaluation
- 4 GConsent Ontology
- 4.1 Ontology Overview
- 4.2 Example Use-Case
- 4.3 Limitations
- 5 Conclusion and Future Work
- References
- Latent Relational Model for Relation Extraction
- 1 Introduction
- 2 Related Work
- 2.1 Relation Extraction
- 2.2 Word Analogy
- 3 Methodology
- 3.1 Latent Relational Model
- 3.2 Geometric Interpretation of Analogy
- 3.3 Relation Extraction as Analogy Problem
- 4 Experiment
- 4.1 Experimental Setting
- 4.2 Results and Discussion
- 4.3 Unsupervised Relational Analysis
- 5 Conclusion and Future Work
- References
- Mini-ME Swift: The First Mobile OWL Reasoner for iOS
- 1 Introduction and Motivation
- 2 Background
- 3 Description Logics Reasoning for iOS Devices
- 4 Case-Study: Semantic-Based POI Discovery in Augmented Reality
- 5 Experiments
- 5.1 Standard Inference Services
- 5.2 Non-standard Inference Services
- 6 Conclusion and Future Work
- References
- Validation of SHACL Constraints over KGs with OWL 2 QL Ontologies via Rewriting
- 1 Introduction
- 2 Preliminaries and Running Example
- 2.1 Knowledge Graph
- 2.2 SHACL Syntax
- 2.3 SHACL Semantics
- 2.4 OWL 2 QL
- 2.5 Constraint Validation over KGs Enhanced with Ontologies
- 3 The Problem of Constraint Rewriting
- 3.1 SHACL-Rewriting
- 3.2 Non-existence of SHACL-Rewritings
- 4 Rewriting of SHACL+ Constraints over OWL 2 QL
- 4.1 Satisfiable Knowledge Graphs
- 4.2 Validity over Canonical Models
- 4.3 Rewriting of Shape Targets
- 4.4 Rewriting of the Ontology
- 5 Related Work
- 6 Conclusion
- References
- An Ontology-Based Interactive System for Understanding User Queries
- 1 Introduction
- 2 Preliminaries
- 2.1 Knowledge Bases
- 2.2 Problem Statement
- 3 An Interactive Search Approach
- 4 Question Generation
- 5 Knowledge Base Enrichment
- 6 Evaluation
- 6.1 Concept Enrichment
- 6.2 Evaluating the Effect of k in Selecting the Top-k Candidates
- 6.3 Evaluating Candidate Selection and Depth of Correct Answer
- 6.4 Further Evaluation of Grouping Algorithm
- 7 Conclusions
- References
- Knowledge-Based Short Text Categorization Using Entity and Category Embedding
- 1 Introduction
- 2 Related Work
- 3 Knowledge-Based Short Text Categorization (KBSTC)
- 3.1 Probabilistic Approach
- 3.2 Parameter Estimation
- 4 Entity and Category Embedding
- 4.1 Network Construction
- 4.2 Embedding Model
- 5 Experimental Results
- 5.1 Datasets
- 5.2 Baselines
- 5.3 Evaluation of KBSTC
- 5.4 Evaluation of Entity and Category Embedding
- 5.5 Evaluation of Entity Linking
- 5.6 Using Wikipedia as a Training Set
- 6 Conclusion and Future Work
- References
- A Hybrid Approach for Aspect-Based Sentiment Analysis Using a Lexicalized Domain Ontology and Attentional Neural Models
- 1 Introduction
- 2 Related Work
- 3 Specification of Data and Tasks
- 4 Method
- 4.1 Ontology-Based Approach
- 4.2 Left-Center-Right Separated Neural Network with Rotatory Attention
- 4.3 Two-Step Approach
- 4.4 Inversed LCR-Rot
- 4.5 Multi-hop LCR-Rot
- 5 Evaluation
- 6 Conclusion
- References
- Predicting Entity Mentions in Scientific Literature
- 1 Introduction
- 2 Related Work
- 3 Entity Prediction in Scientific Literature
- 3.1 Failed Attempts
- 4 Using a Neural Network with BOW
- 4.1 Including Author's Co-authorship
- 5 Evaluation
- 5.1 Entity Prediction Using Abstract Entities
- 5.2 Entity Prediction with Different Hyperparameters
- 5.3 Entity Prediction Including Co-Authorship
- 5.4 Limitations
- 6 Conclusion
- References
- Resources Track
- AYNEC: All You Need for Evaluating Completion Techniques in Knowledge Graphs
- 1 Introduction
- 2 Workflow
- 2.1 Preprocessing
- 2.2 Splitting
- 2.3 Negatives Generation
- 2.4 Triple Classification
- 2.5 Statistical Analysis
- 3 Related Datasets
- 4 AYNEC-DataGen
- 4.1 Preprocessing
- 4.2 Splitting
- 4.3 Negatives Generation
- 4.4 Output
- 5 AYNEC-ResTest
- 6 AYNEC-Datasets
- 7 Conclusions
- References
- RVO - The Research Variable Ontology
- 1 Introduction
- 2 Relevance
- 3 RVO - The Research Variable Ontology
- 3.1 Ontology Development Process
- 3.2 Ontology
- 3.3 Integrating Existing Ontologies
- 3.4 How RVO Can Assist in the Analytics Process
- 3.5 Reusability of RVO
- 3.6 Availability
- 4 Case Study
- 4.1 Introduction
- 4.2 Visualizing RVO
- 5 Conclusion
- References
- EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities
- 1 Introduction
- 2 Related Work
- 3 Scientific Events Ontology
- 4 EVENTSKG Characteristics
- 5 Data Curation
- 6 Use Case
- 7 Metadata Analysis
- 8 Conclusions and Future Work
- References
- CORAL: A Corpus of Ontological Requirements Annotated with Lexico-Syntactic Patterns
- 1 Introduction
- 2 Building the Corpus
- 2.1 Steps for Generating the Corpus
- 2.2 Availability, Extensibility and Maintenance of the Annotated Corpus
- 3 Corpus Description
- 3.1 Dictionary of Lexico-Syntactic Patterns
- 3.2 Annotated Corpus of Ontological Requirements
- 3.3 Example of Use
- 3.4 Publishing the Corpus as Linked Data
- 4 Corpus Statistics
- 5 Applications of the Corpus
- 6 Related Work
- 7 Conclusions
- References
- MMKG: Multi-modal Knowledge Graphs
- 1 Introduction
- 2 Relevance
- 2.1 Relevance for Multi-relational Link Prediction Research
- 2.2 Relevance for Entity Matching Research
- 3 Mmkg: Dataset Generation
- 3.1 Availability and Sustainability
- 4 Technical Quality of Mmkg
- 4.1 Task: sameAs Link Prediction
- 4.2 Model: Products of Experts
- 4.3 Additional Baseline Approaches
- 5 Experiments
- 5.1 Evaluation
- 6 Conclusion
- References
- BeSEPPI: Semantic-Based Benchmarking of Property Path Implementations
- 1 Introduction
- 2 Preliminaries
- 2.1 Graph
- 2.2 SPARQL 1.1 Property Paths
- 3 Property Path Benchmark BeSEPPI
- 3.1 Dataset
- 3.2 Queries
- 3.3 Metrics
- 3.4 Execution Strategy
- 4 Benchmark Results
- 4.1 Experimental Setting
- 4.2 Completeness and Correctness
- 4.3 Summary of Results
- 5 Related Work
- 6 Conclusion
- References
- QED: Out-of-the-Box Datasets for SPARQL Query Evaluation
- 1 Introduction
- 2 Motivation
- 3 SPARQL QED
- 4 Dataset Examples
- 5 Related Work
- 6 Conclusions
- References
- ToCo: An Ontology for Representing Hybrid Telecommunication Networks
- 1 Introduction
- 2 Background and Requirements
- 2.1 Background
- 2.2 Research Gap
- 3 TOUCAN Ontology
- 3.1 Ontology Perspectives
- 4 Examples and Use Cases of ToCo
- 4.1 Examples on Network Resource Description
- 4.2 Examples on Network Management Task Execution with ToCo
- 4.3 Use Cases
- 5 Conclusions
- References
- A Software Framework and Datasets for the Analysis of Graph Measures on RDF Graphs
- 1 Introduction
- 2 Related Work
- 3 A Framework for Graph-Based Analysis on RDF Data
- 3.1 Functionality
- 3.2 Graph Measures
- 3.3 Availability, Sustainability and Maintenance
- 4 RDF Datasets for the Analysis of Graph Measures
- 4.1 Data Acquisition
- 4.2 Execution Environment
- 4.3 Availability, Sustainability and Maintenance
- 5 Preliminary Analysis and Discussion
- 5.1 Observations About Graph Topologies in the LOD Cloud
- 5.2 Effective Measures for RDF Graph Analysis
- 6 Conclusions and Future Work
- References
- In-Use Track
- The Location Index: A Semantic Web Spatial Data Infrastructure
- 1 Introduction
- 2 Requirements
- 2.1 Initial Datasets
- 2.2 Future Datasets
- 2.3 Data Governance
- 2.4 Data Delivery
- 3 Identifier Governance
- 4 Architecture
- 4.1 Ontologies
- 4.2 Dataset Publication
- 4.3 Linkset Publication
- 4.4 Data Validation
- 4.5 Graph Expansion
- 4.6 Clients
- 4.7 Graph Caches
- 5 Tools
- 6 Conclusions
- References
- Legislative Document Content Extraction Based on Semantic Web Technologies
- 1 Introduction
- 2 History of the Law and Parliamentary Labor Projects
- 2.1 Production Workflow
- 3 Software Environment
- 3.1 Linked Open Data
- 3.2 Automatic XML Marker
- 3.3 Publishing
- 3.4 Content Delivery
- 4 Results
- 5 Discussion
- 6 Conclusions
- References
- BiographySampo - Publishing and Enriching Biographies on the Semantic Web for Digital Humanities Research
- 1 National Biographical Dictionaries on the Web
- 2 Creating the Knowledge Graph
- 3 Data Model, Datasets, and Data Service
- 4 New Ways for Studying Biographies
- 5 Discussion
- References
- Tinderbook: Fall in Love with Culture
- 1 Introduction
- 2 Recommendation Algorithm
- 2.1 Definitions
- 2.2 Approach
- 2.3 Offline Evaluation
- 3 Application
- 3.1 Session
- 3.2 Architecture
- 4 Online Evaluation
- 5 Competing Systems
- 6 Conclusions and Lessons Learned
- References
- Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation*-5mm
- 1 Introduction
- 2 Shape Expressions
- 2.1 ShEx Implementations
- 2.2 Interoperability
- 3 Use Cases
- 3.1 Domain-Specific ShEx Validation in Medical Informatics
- 3.2 Domain-Generic ShEx Validation in Wikidata
- 4 ShEx Validation of Domain-Specific Wikidata Subgraphs
- 4.1 Molecular Biology
- 4.2 Software and File Formats
- 4.3 Bibliographic Metadata
- 5 Discussion
- 5.1 Novelty of Validation of RDF Data Using ShEx
- 5.2 Uptake of ShEx Tooling
- 5.3 Soundness and Quality
- 5.4 Availability
- 6 Conclusion
- References
- Correction to: Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation
- Correction to: Chapter "Using Shape Expressions (ShEx) to Share RDF Data Models and to Guide Curation with Rigorous Validation" in: P. Hitzler et al. (Eds.): The Semantic Web, LNCS 11503, https://doi.org/10.1007/978-3-030-21348-0_39
- Author Index
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