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• Title Page; Contents; Preface; Prologue; Part I. Introduction and Scientific Background; Chapter 1. Introduction; Motivation
• Why this book?; Who should read this book?; SmartWeb System; Roadmap; Chapter 2. Scientific Background; Natural Language Processing; Dialogue Systems; Information Retrieval; Question Answering; Ontologies; Metacognition and Introspection; Machine Learning; Standards; Related Systems; Summary; Part II. Ontologies and Dialogue-Based QA; Chapter 3. Ontology-Based Dialogue Processing; Integration Framework; Semantic Web Knowledge Structures

• A Pattern Language for Interaction Design Related Research; Summary;
• Chapter 4. Dialogue Management; Dialogue Constraints; Reaction and Presentation Manager; Summary; Part III. Introspection and Dialogue Adaptation;
• Chapter 5. Introspective Mechanism; Metadata Management; Introspective Control; Learner Pool; Verification of Introspective Methods; Summary;
• Chapter 6. Adaptable Dialogue Management; Dimensions of Adaptivity; Complex Decision Making Process; Dialogue Information State; Related Research; Summary;
• Chapter 7. Evaluation; Evaluation Methodology; Evaluation Types; Data Characteristics

• Evaluation Measures Data Sets and the Groundwork hypothesis; Introspection and the Mining hypothesis; Introspection and the Adaptation hypothesis; Introspection and the Feedback hypothesis; Dialogue Level Integration; Summary;
• Chapter 8. Further Applications; Application Potentials; Semantic Mediation in THESEUS; Prototype Integration; Semantic Navigation; Summary; Epilogue; Bibliography

Question answering (QA) has become one of the fastest growing topics in computational linguistics and information access. To advance research in the area of dialogue-based question answering, we propose a combination of methods from different scientific fields (i.e., Information Retrieval, Dialogue Systems, Semantic Web, and Machine Learning). This book sheds light on adaptable dialogue-based question answering. We demonstrate the technical and computational feasibility of the proposed ideas, the introspective methods in particular, by beginning with an extensive introduction to the dialogical.

• Title Page
• Contents
• Prologue
• Introduction
• Problem Statement
• Incomplete Agreement on Assertional Identifiers
• Use of Analogous Terminologies
• Hypothesis
• Contribution and Thesis Structure
• Impact
• Background
• The World Wide Web
• The Semantic Web
• Resource Description Framework
• RDF Schema
• Web Ontology Language
• RDF Web Publishing and Linked Data
• RDF Search Engines
• Notation and Core Concepts
• RDF
• Turtle Syntax
• Linked Data Principles and Provenance
• Atoms and Rules

• Terminological Data: RDFS/OWLDistribution Framework
• Crawling, Corpus and Ranking
• Crawler
• Breadth-first Crawling
• Incorporating Politeness
• On-disk Queue
• Multi-threading
• Crawling RDF/XML
• Distributed Approach
• Related Work
• Critical Discussion and Future Directions
• Evaluation Corpus
• Crawl Statistics
• Corpus Statistics
• Related Work
• Critical Discussion and Future Directions
• Ranking
• Rationale and High-level Approach
• Creating and Ranking the Source Graph
• Distributed Ranking Implementation

• Ranking Evaluation and ResultsRelated Work
• Critical Discussion and Future Directions
• Core
• Web Reasoning
• Linked Data Reasoning: Overview
• Incomplete Reasoning: Rationale
• Rule-based Reasoning
• Forward Chaining
• OWL 2 RL/RDF Scalability
• Distinguishing Terminological Data
• Implementing T-split Inferencing
• Optimising the Assertional Program
• Merging Equivalent T-ground Rules
• Rule Index
• Rule Saturation
• Preliminary Performance Evaluation
• Towards Linked Data Reasoning
• ``A-linear'' OWL 2 RL/RDF

• Authoritative ReasoningDistributed Reasoning
• Linked Data Reasoning Evaluation
• Related Work
• Scalable/Distributed Reasoning
• Web Reasoning
• Critical Discussion and Future Directions
• Annotated Reasoning
• Generalised Annotated Programs
• Use-case Annotations
• Blacklisting
• Authoritative Analysis
• Triple Ranks
• Formal Annotation Framework
• Annotation Domains
• (Specialised) Annotated Programs
• Least Fixpoint and Decidability
• Seeding Annotations
• T-split Annotated Programs
• Annotated Reasoning Tasks
• Constraints

• Annotated Linked Data ReasoningRanking Triples: Implementation/Evaluation
• Reasoning: Implementation/Evaluation
• Repair: Implementation/Evaluation
• Related Work
• Annotated Reasoning
• Inconsistency Repair
• Critical Discussion and Future Directions
• Consolidation
• OWL Equality Semantics
• Corpus: Naming Across Sources
• Base-line Consolidation
• High-level approach
• Distributed approach
• Evaluation
• Extended Reasoning Consolidation
• High-level approach
• Distributed approach
• Evaluation

Linked Data publishing has brought about a novel "Web of Data": a wealth of diverse, interlinked, structured data published on the Web. These Linked Datasets are described using the Semantic Web standards and are openly available to all, produced by governments, businesses, communities and academia alike. However, the heterogeneity of such data - in terms of how resources are described and identified - poses major challenges to potential consumers. Herein, we examine use cases for pragmatic, lightweight reasoning techniques that leverage Web vocabularies (described in RDFS and OWL) to better i

• Web collaboration on mathematical knowledge
• Representing mathematical knowledge
• Ontologies for structures of mathematical knowledge
• Using mathematical markup for implementing and documenting expressive ontologies
• Multi-dimensional metadata markup
• Primitive services for managing mathematical knowledge
• Integrating assistive services into interactive documents
• Transparent translations in knowledge bases
• The semantic Wiki SWiM
• an integrated collaboration environment
• Usability evaluation of an integrated environment for maintaining semiformal collections
• Namesake prefixes
• Ontologies
• Algorithm and implementation details.

• Title Page; Abstract; Acknowledgements; Contents; Part I. Prelude;
• Chapter 1. Introduction; Problem statement; Research questions; Hypothesis; Contributions; Metadata Lifecycle; Thesis structure; Part II. Foundations;
• Chapter 2. Research background; Documents as interlinked knowledge elements; Rhetorical Structure Theory; The Evolution of the Web; The Social Semantic Desktop; Part III. Core;
• Chapter 3. Layered ontological framework; The SALT Layers; The KonneX Vocabulary; Related work; Conclusion;
• Chapter 4. Manual Semantic Authoring and Publishing; The manual creation process; Evaluation.

• Related workConclusion; Chapter 5. Towards (semi)-automatic Semantic Authoring and Publishing; Automatic extraction of shallow metadata; Automatic extraction of discourse knowledge items; Using Semantic Publishing for information expansion and visualisation; Evaluation; Related work; Conclusion; Chapter 6. KonneXSALT
• A Semantic Publishing Platform; KonneXSALT as a semantic claim federation infrastructure; KonneXSALT as an integrated component of CORAAL; Related work; Conclusion; Part IV. Conclusions; Chapter 7. Conclusion and Future Work; Objectives and contributions; Insights.

• Open challenges and future researchSummary; Bibliography.

Dissemination can be seen as a communication process between scientists. Over the course of several publications, they expose and support their findings, while discussing stated claims. Such discourse structures are trapped within the content of the publications, thus making the semantics discoverable only by humans. In addition, the lack of advances in scientific publishing, where electronic publications are still used as simple projections of paper documents, combined with the current growth in the amount of scientific research being published, transforms the process of finding relevant lite.

• Intro
• Title Page
• Abstract
• Contents
• Foundations
• Introduction
• Motivation
• Problem Statement
• Challenges
• Hypotheses
• Contributions
• Outline
• Preliminaries
• The Semantic Web, Linked Data, and Knowledge Graphs
• The Resource Description Framework
• The SPARQL Protocol and RDF Query Language
• Linked Data Fragments
• Foundations of Query Processing
• Sample-based Knowledge Graph Profiling
• Related Work: Knowledge Graph Profiling and Sampling
• Performance Profiling
• Statistical Profiling
• Sampling from Graphs

• Performance Profiling of Triple Pattern Fragments
• Introduction
• Motivating Example
• Triple Pattern Fragments Profiler
• Evaluation
• Conclusion
• Statistical Profiling for Federated Query Processing
• Introduction
• Problem Statement
• Characteristic Sets Profile Feature Estimation
• Estimation-based Federated Query Planning
• Evaluation
• Conclusion
• Decentralized Query Processing over Knowledge Graphs
• Related Work: Decentralized Query Processing
• Single Knowledge Graphs
• Multiple Knowledge Graphs
• Robust Query Processing in Relational Databases

• Robust Query Processing over Linked Data Fragments
• Introduction
• Motivating Example
• Robust Query Planning
• A New Class of Adaptive Join Operators
• Evaluation
• Conclusion
• Federated Query Processing over Heterogeneous Linked Data Fragments
• Introduction
• Motivating Example
• Federations of Linked Data Fragment Services
• Conceptual Framework
• Illustration of the Framework
• Evaluation
• Conclusion
• Conclusion
• Summary
• Outlook
• Bibliography
• Notation
• Acronyms
• Prefixes
• Additional Material
• Chapter 4
• Chapter 5
• Efficiency of CSPF Computation

• Intro
• Title Page
• Preface
• Program Committee of SEMANTiCS 2022
• Contents
• Semantics in Data Quality, Standards, and Protection
• Evaluating Web Content Using the W3C Credibility Signals
• Evaluating Quality Improvement Techniques Within the Linked Data Generation Process
• A Semantic Specification for Data Protection Impact Assessments (DPIA)
• AIRO: An Ontology for Representing AI Risks Based on the Proposed EU AI Act and ISO Risk Management Standards
• Representation Learning and Reasoning for Downstream AI Tasks
• On a Generalized Framework for Time-Aware Knowledge Graphs

• Assigning Systems to Test Environments Through Ontological Reasoning
• Adding Domain Knowledge to Improve Entity Resolution in 17th and 18th Century Amsterdam Archival Records
• Ontology Development
• Automatically Drafting Ontologies from Competency Questions with FrODO
• Plow: A Novel Approach to Interlinking Modular Ontologies Based on Software Package Management
• Modelling Business Agreements in the Multimodal Transportation Domain Through Ontological Smart Contracts
• Learning over Complementary Knowledge

• Learning Ontology Classes from Text by Clustering Lexical Substitutes Derived from Language Models
• Metadata Based Contextual Summarizer for Technical Conversations in Public Forums
• esT5s: A Spanish Model for Text Summarization
• A Joint Model for Detecting Causal Sentences and Cause-Effect Relations from Text
• Distinguishing the Types of Coordinated Verbs with a Shared Argument by Means of New ZeugBERT Language Model and ZeugmaDataset
• Subject Index
• Author Index

• Intro
• Title Page
• Preface
• Contents
• TODO: A Core Ontology for Task-Oriented Dialogue Systems in Industry 4.0
• Interlinking Valency Frames and WordNet Synsets in the LiLa Knowledge Base of Linguistic Resources for Latin
• NEST: Neural Soft Type Constraints to Improve Entity Linking in Tables
• Raising Awareness of Data Sharing Consent Through Knowledge Graph Visualisation
• Facade-X: An Opinionated Approach to SPARQL Anything
• Literal2Feature: An Automatic Scalable RDF Graph Feature Extractor

• LLOD-Driven Bilingual Word Embeddings Rivaling Cross-Lingual Transformers in Quality of Life Concept Detection from French Online Health Communities
• Knowledge Graph Question Answering Using Graph-Pattern Isomorphism
• Optimizing RDF Stream Processing for Uncertainty Management
• Personalizing Type-Based Facet Ranking Using BERT Embeddings
• Annotating Entities with Fine-Grained Types in Austrian Court Decisions
• When is the Peak Performance Reached? An Analysis of RDF Triple Stores

• Building a Data Processing Activities Catalog: Representing Heterogeneous Compliance-Related Information for GDPR Using DCAT-AP and DPV
• A Fully Decentralized Triplestore Managed via the Ethereum Blockchain
• BESOCIAL: A Sustainable Knowledge Graph-Based Workflow for Social Media Archiving
• Embedding Taxonomical, Situational or Sequential Knowledge Graph Context for Recommendation Tasks
• Adding Structure and Removing Duplicates in SPARQL Results with Nested Tables
• Object-Action Association Extraction from Knowledge Graphs

• Members of Parliament in Finland Knowledge Graph and Its Linked Open Data Service
• Subject Index
• Author Index

• Intro
• Title Page
• Abstract
• Acknowledgments
• Contents
• List of Figures
• List of Tables
• Chapter 1. Introduction
• Chapter 2. Large Scale Semantic Integration of Linked Data: A survey
• Chapter 3. Research Gaps & Motivating Scenarios
• Chapter 4. Cross-dataset Identity Reasoning & Semantics-aware Indexes at Global Scale
• Chapter 5. Content-based Intersection, Union and Complement Metrics Among Several Linked Datasets
• Chapter 6. The LODsyndesis Suite of Services
• Chapter 7. Conclusion
• Bibliography
• Appendix A. Publications, Systems & Useful Links

• Intro
• Title Page
• Abstract
• Acknowledgments
• Contents
• List of Figures
• List of Tables
• Chapter 1. Introduction
• Chapter 2. Large Scale Semantic Integration of Linked Data: A survey
• Chapter 3. Research Gaps & Motivating Scenarios
• Chapter 4. Cross-dataset Identity Reasoning & Semantics-aware Indexes at Global Scale
• Chapter 5. Content-based Intersection, Union and Complement Metrics Among Several Linked Datasets
• Chapter 6. The LODsyndesis Suite of Services
• Chapter 7. Conclusion
• Bibliography
• Appendix A. Publications, Systems & Useful Links

• Intro
• Title Page
• Abstract
• Contents
• Part I. Introduction and Preliminaries
• Chapter 1. Introduction
• Chapter 2. Preliminaries
• Part II. Type-systems for the Semantic Web
• Chapter 3. A Basic Programming Language (lambda-calculus)
• Chapter 4. Type Checking with Description Logics
• Chapter 5. Type Checking with SHACL
• Chapter 6. Shape Containment
• Part III. Related Work and Conclusion
• Chapter 7. Related Work
• Chapter 8. Conclusion
• Bibliography
• Appendix A. Soundness of the lambda calculus

• Intro
• Title Page
• Preface
• Contents
• Part I. Ontology Design, Extraction and Applications
• Modular Ontology Modeling: A Tutorial
• The Landscape of Ontology Reuse Approaches
• Best Practices for Implementing FAIR Vocabularies and Ontologies on the Web
• High-Quality Knowledge Graphs Generation: R2RML and RML Comparison, Rules Validation and Inconsistency Resolution
• CLARIAH: Enabling Interoperability Between Humanities Disciplines with Ontologies
• Ontology Extraction and Usage in the Scholarly Knowledge Domain

• Representing Complex Knowledge for Exploration and Recommendation: The Case of Classical Music Information
• Part II. Ontology Reasoning
• A Framework for Reasoning on Probabilistic Description Logics
• Reasoning About Typicality and Probabilities in Preferential Description Logics
• Axiom Pinpointing
• Defeasible Reasoning in Description Logics: An Overview on DL^N
• Querying the Semantic Web via Rules
• Large-Scale Ontological Reasoning via Datalog
• Subject Index
• Author Index

• Intro
• Title Page
• Preface
• Contents
• Part 1. Foundations of Knowledge-Based eXplainable Systems
• Knowledge Graphs on the Web
• An Overview
• Foundations of Explainable Knowledge-Enabled Systems
• Knowledge Graph Embeddings and Explainable AI
• Benchmarking the Lifecycle of Knowledge Graphs
• Part 2. Applications
• Knowledge-Aware Interpretable Recommender Systems
• Differentiable Reasoning on Large Knowledge Bases and Natural Language
• Neuro-Symbolic Architectures for Context Understanding

• Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning
• Knowledge-Based Explanations for Transfer Learning
• Explanations in Predictive Analytics: Case Studies
• Generating Explanations in Natural Language from Knowledge Graphs
• Part 3. Challenges for Knowledge-Based eXplainable Systems
• Directions for Explainable Knowledge-Enabled Systems
• The Data Ethics Challenges of Explainable AI and Their Knowledge-Based Solutions
• Who Is This Explanation for? Human Intelligence and Knowledge Graphs for eXplainable AI

• Managing Identity in Knowledge-Based Explainable Systems
• Subject Index
• Author Index

• Intro
• Title Page
• Acknowledgements
• Abstract
• Notation
• Contents
• Introduction
• Aims and Objectives
• Contributions
• Thesis Structure
• Background
• Natural Language Generation
• Neural Networks in Natural Language Processing
• Language Modelling with Neural Networks
• Neural Networks as Generative Models
• Encoder-Decoder Framework
• Summary
• Evaluation Methodology
• Evaluation Methods
• Automatic Evaluation
• Human Evaluation
• Evaluating Multilingual Summaries from the Perspective of Wikipedia Readers and Editors
• Baselines
• Random

• Kneser-Ney (KN) Language Model
• Information Retrieval (IR)
• Machine Translation (MT)
• Summary
• Building Corpora of Natural Language Texts Aligned with Knowledge Base Triples
• Automatically Aligning Texts and Triples
• Wikipedia Summaries
• Knowledge Base Triples
• Aligned Corpora
• Biographies
• The D3 Corpus
• Building Multilingual Corpora
• Discussion
• Summary
• Neural Wikipedian: Generating Biographies from Knowledge Base Triples
• The Model
• Triple Encoder
• Decoder
• Property-Type Placeholders
• Model Training
• Generating Summaries
• Dataset Preparation

• Modelling the Textual Summaries
• Modelling the Input Triples
• Experiments
• Training Details
• Automatic Evaluation
• Human Evaluation
• Discussion
• Conclusion
• Learning to Generate Wikipedia Summaries for Underserved Languages
• Model
• Property Placeholders
• Dataset Preparation
• Experiments
• Training Details
• Automatic Evaluation
• Community Study
• Recruitment
• Readers' Evaluation
• Editors' Evaluation
• Conclusion
• Point at the Triple: Improving Neural Wikipedian with a Pointer Mechanism
• The Model
• Decoder
• Triple Encoder

• Dynamically Expanding the Vocabulary
• Summarising By Pointing and Generating
• Dataset Preparation
• Experiments
• Training Details
• Automatic Evaluation
• Human Evaluation
• Summary and Discussion
• Conclusion and Future Work
• Summary and Conclusions
• Current Limitations and Future Work
• Generation of Multi-Sentence Summaries
• The Main Entity of Interest
• Using the S3 Corpus
• Bibliography

• Foundations
• Related work
• Methodology for benchmarking graph cover strategies
• Evaluation of common graph cover strategies
• Combining the benefits of graph clustering and hash partitioning.

• Intro; Title Page; Abstract; Table of Contents; 1 Introduction; 1.1 Research Questions; 1.2 Contributions; 1.3 Structure; 2 Fundamentals; 2.1 Semantic Web Knowledge Graphs; 2.1.1 Linked Open Data; 2.2 Data Mining and The Knowledge Discovery Process; 2.3 Semantic Web Knowledge Graphs in Data Mining; 3 Related Work; 3.1 Selection; 3.1.1 Using LOD to interpret relational databases; 3.1.2 Using LOD to interpret semi-structured data; 3.1.3 Using LOD to interpret unstructured data; 3.2 Preprocessing; 3.2.1 Domain-independent Approaches; 3.2.2 Domain-specific Approaches; 3.3 Transformation

• 3.3.1 Feature Generation3.3.2 Feature Selection; 3.3.3 Other; 3.4 Data Mining; 3.4.1 Domain-independent Approaches; 3.4.2 Domain-specific Approaches; 3.5 Interpretation; 3.6 Discussion; 3.7 Conclusion and Outlook; I Mining Semantic Web Knowledge Graphs; 4 A Collection of Benchmark Datasets for Systematic Evaluations of Machine Learning on the Semantic Web; 4.1 Datasets; 4.2 Experiments; 4.2.1 Feature Generation Strategies; 4.2.2 Experiment Setup; 4.2.3 Results; 4.2.4 Number of Generated Features; 4.2.5 Features Increase Rate; 4.3 Conclusion and Outlook

• 5 Propositionalization Strategies for Creating Features from Linked Open Data5.1 Strategies; 5.1.1 Strategies for Features Derived from Specific Relations; 5.1.2 Strategies for Features Derived from Relations as Such; 5.2 Evaluation; 5.2.1 Tasks and Datasets; 5.2.2 Results; 5.3 Conclusion and Outlook; 6 Feature Selection in Hierarchical Feature Spaces; 6.1 Problem Statement; 6.2 Approach; 6.3 Evaluation; 6.3.1 Datasets; 6.3.2 Experiment Setup; 6.3.3 Results; 6.4 Conclusion and Outlook; 7 Mining the Web of Linked Data with RapidMiner; 7.1 Description; 7.1.1 Data Import; 7.1.2 Data Linking

• 7.1.3 Feature Generation7.1.4 Feature Subset Selection; 7.1.5 Exploring Links; 7.1.6 Data Integration; 7.2 Example Use Case; 7.3 Evaluation; 7.3.1 Feature Generation; 7.3.2 Propositionalization Strategies; 7.3.3 Feature Selection; 7.3.4 Data Integration; 7.3.5 Time Performances; 7.4 Related Work; 7.5 Conclusion and Outlook; II Semantic Web Knowledge Graphs Embeddings; 8 RDF2Vec: RDF Graph Embeddings for Data Mining; 8.1 Approach; 8.1.1 RDF Graph Sub-Structures Extraction; 8.1.2 Neural Language Models
• word2vec; 8.2 Evaluation; 8.3 Experimental Setup; 8.4 Results

• 8.5 Semantics of Vector Representations8.6 Features Increase Rate; 8.7 Conclusion and Outlook; 9 Biased Graph Walks for RDF Graph Embeddings; 9.1 Approach; 9.2 Evaluation; 9.2.1 Datasets; 9.2.2 Experimental Setup; 9.2.3 Results; 9.3 Conclusion and Outlook; III Applications of Semantic Web Knowledge Graphs; 10 Analyzing Statistics with Background Knowledge from Semantic Web Knowledge Graphs; 10.1 The ViCoMap Tool; 10.1.1 Data Import; 10.1.2 Correlation Analysis; 10.2 Use Case: Number of Universities per State in Germany; 10.3 Conclusion and Outlook; 11 Semantic Web enabled Recommender Systems