
Semantic Web Evaluation Challenges
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This book constitutes the thoroughly refereed post conference proceedings of the second edition of the Semantic Web Evaluation Challenge, SemWebEval 2015, co-located with the 12th European Semantic Web conference, held in Portoroz, Slovenia, in May/June 2015.
This book includes the
descriptions of all methods and tools that competed at SemWebEval 2015,
together with a detailed description of the tasks, evaluation procedures and
datasets. The contributions are grouped in the areas: open knowledge extraction
challenge (OKE 2015); semantic publishing challenge (SemPub 2015);
schema-agnostic queries over large-schema databases challenge (SAQ 2015);
concept-level sentiment analysis challenge (CLSA 2015).
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Content
- Intro
- Preface
- Organization
- Contents
- Open Knowledge Extraction Challenge (OKE-2015)
- Open Knowledge Extraction Challenge
- 1 Introduction
- 2 Tasks
- 2.1 Task 1: Entity Recognition, Linking and Typing for Knowledge Base population
- 2.2 Task 2: Class Induction and Entity Typing for Vocabulary and Knowledge Base Enrichment
- 3 Training and Evaluation Datasets
- 3.1 Task 1
- 3.2 Task 2
- 4 Results
- 5 Conclusions
- References
- CETUS -- A Baseline Approach to Type Extraction
- 1 Introduction
- 2 Related Work
- 3 Pattern Extraction
- 3.1 Sentence Part Extraction
- 3.2 Grammar Construction
- 4 Type Extraction
- 4.1 Type String Extraction
- 4.2 Local Type Hierarchy
- 5 Entity Type Linking Using YAGO
- 6 Entity Type Linking Using FOX
- 7 Evaluation
- 7.1 OKE Challenge 2015 Task 1
- 7.2 OKE Challenge 2015 Task 2
- 8 Conclusion
- References
- A Hybrid Approach for Entity Recognition and Linking
- 1 Introduction
- 2 Related Work
- 2.1 Entity Recognition
- 2.2 Entity Linking
- 3 A Hybrid Approach for Entity Recognition and Linking
- 3.1 Named Entity Recognition
- 3.2 Named Entity Linking
- 3.3 Named Entity Pruning
- 4 System Implementation
- 4.1 Pipeline_1
- 4.2 Pipeline_2
- 4.3 Pipeline_3
- 5 Experimental Settings and Results
- 5.1 Statistics of the Oracle
- 5.2 Experimental Settings
- 5.3 Results on the Training Set
- 5.4 Comparison with Other Tools on the Training Set
- 5.5 Results on the Test Set
- 5.6 Comparison with Other Tools on the Test Set
- 6 Conclusion and Future Work
- References
- Using FRED for Named Entity Resolution, Linking and Typing for Knowledge Base Population
- 1 Introduction
- 2 FRED at Work
- 3 Addressing the Open Knowledge Extraction Challenge
- 3.1 Task 1: Named Entity Resolution, Linking and Typing for Knowledge Base Population
- 3.2 Task 2: Class Induction and Entity Typing for Vocabulary and Knowledge Base Enrichment
- 4 Results
- 5 Conclusions
- References
- Exploiting Linked Open Data to Uncover Entity Types
- 1 Introduction
- 2 Related Work
- 3 Methodology
- 3.1 Type Induction
- 3.2 Type Alignment
- 4 Evaluation
- 4.1 Type Induction
- 4.2 Type Alignment
- 4.3 Competition Result
- 5 Conclusion
- References
- Semantic Publishing Challenge (SemPub2015)
- Semantic Publishing Challenge -- Assessing the Quality of Scientific Output by Information Extraction and Interlinking
- 1 Introduction: Semantic Publishing Today
- 2 Definition of the Challenge
- 3 Common Evaluation Procedures
- 4 Task 1: Extraction and Assessment of Workshop Proceedings Information
- 4.1 Motivation and Objectives
- 4.2 Data Source
- 4.3 Queries
- 4.4 Accepted Submissions and Winners
- 4.5 Lessons Learnt
- 5 Task 2: Extracting Contextual Information from the PDF Full Text of the Papers
- 5.1 Motivation and Objectives
- 5.2 Data Source
- 5.3 Queries
- 5.4 Accepted Submissions and Winner
- 5.5 Lessons Learnt
- 6 Task 3: Interlinking
- 6.1 Motivation and Objectives
- 6.2 Data Source
- 6.3 Queries
- 6.4 Lessons Learnt
- 7 Overall Lessons Learnt for Future Challenges
- References
- Information Extraction from Web Sources Based on Multi-aspect Content Analysis
- 1 Introduction
- 2 System Architecture
- 2.1 Page Rendering
- 2.2 Model Building
- 2.3 Output Dataset Generation
- 3 Ontological Model
- 3.1 Rendered Page Level
- 3.2 Segmentation Level
- 3.3 Tagging Level
- 3.4 Logical Tree Level
- 3.5 Domain Level
- 4 System Implementation
- 4.1 Layout Analysis
- 4.2 Generic Text Tagging
- 4.3 CEUR Entity Recognition
- 4.4 Logical Structure Construction
- 4.5 CEUR Index Page Processing
- 5 Conclusions
- References
- Extracting Contextual Information from Scientific Literature Using CERMINE System
- 1 Introduction
- 2 System Overview
- 2.1 Models and Formats
- 2.2 System Architecture
- 3 Metadata Extraction Algorithms
- 3.1 Geometric Structure Extraction
- 3.2 Content Classification
- 3.3 Author and Affiliation Extraction
- 3.4 Affiliation Parsing
- 3.5 References Extraction
- 3.6 Reference Parsing
- 4 Semantic Publishing Challenge of ESWC 2015
- 5 Conclusions and Future Work
- References
- Machine Learning Techniques for Automatically Extracting Contextual Information from Scientific Publications
- 1 Introduction
- 2 Unsupervised Extraction of Contiguous Text Blocks as Basic Units of a PDF
- 3 Supervised Classification of Author and Affiliation Meta-Data
- 3.1 Classification of Text Blocks
- 3.2 Classification of Tokens
- 4 Detection, Segmentation, and Tokenisation of References
- 4.1 Reference Line Extraction
- 4.2 Reference Segmentation
- 4.3 Reference Preprocessing
- 4.4 Reference Token Classification
- 5 Extracting Funding Information Using Named Entity Recognition
- 6 Conclusion and Discussion
- References
- MACJa: Metadata and Citations Jailbreaker
- 1 Introduction
- 2 Related Work
- 3 Metadata and Citations Jailbreaker
- 3.1 Materials and Ontologies
- 3.2 Methods
- 4 Implementation Details
- 4.1 Queries Q2.1 and Q2.2: Affiliations
- 4.2 Queries Q2.3, Q2.4 and Q2.5: Citations
- 4.3 Queries Q2.6, Q2.7 and Q2.8: Research Grants, Funding Agencies and EU Projects
- 4.4 Queries Q2.9 and Q2.10: Related and New Ontologies
- 5 Conclusions
- References
- Automatic Construction of a Semantic Knowledge Base from CEUR Workshop Proceedings
- 1 Introduction
- 2 Design
- 2.1 Syntactic Processing
- 2.2 Semantic Processing
- 2.3 Knowledge Base Construction
- 3 Implementation
- 3.1 Text Pre-processing
- 3.2 Rule-Based Extraction of Contextual Entities
- 3.3 Knowledge Base Population
- 4 Results and Discussion
- 5 Conclusions
- References
- CEUR-WS-LOD:
- 1 Introduction
- 2 System Description
- 3 Ontology Model
- 3.1 Mapping to Well-Know Ontologies
- 4 Overview of Dataset
- 4.1 Example Queries
- 5 Conclusion
- References
- Metadata Extraction from Conference Proceedings Using Template-Based Approach
- 1 Introduction
- 2 Data Model
- 3 Our Approach
- 4 Implementation
- 4.1 Overall Architecture
- 4.2 Library for Context Information Extraction from the PDF Full Text of the Papers
- 5 Results and Discussions
- 6 Conclusion
- References
- Semantically Annotating CEUR-WS Workshop Proceedings with RML
- 1 Introduction
- 2 Problem Statement
- 3 Overview of Our Approach
- 4 RML
- 4.1 Structure of an RML Mapping Document
- 4.2 Leveraging HTML with RML
- 5 Data Modeling
- 6 Mapping CEUR-WS from HTML to RDF
- 6.1 Defining the Mappings
- 6.2 Executing the Mappings
- 7 Query Evaluation
- 8 Tools
- 8.1 RML Processor
- 8.2 RDFUnit
- 8.3 The DataTank
- 9 Discussion and Conclusion
- References
- On the Automated Generation of Scholarly Publishing Linked Datasets: The Case of CEUR-WS Proceedings
- 1 Extract and Semantically Model Scholarly Publishing Contents
- 2 Turning On-line Workshop Proceedings into RDF Graphs: Overall Approach
- 3 Data Analysis Pipelines
- 3.1 Task 1: Processing CEUR-WS HTML Contents
- 3.2 Task 2: Mining PDF Papers
- 4 Modeling Workshop Data as an RDF Graph
- 5 Evaluating Workshop Linked Datasets by SPARQL Queries
- 6 Conclusions and Future Work
- References
- Schema-Agnostic Queries over Large-Schema Databases Challenge (SAQ-2015)
- The Schema-Agnostic Queries (SAQ-2015) Semantic Web Challenge: Task Description
- 1 Introduction
- 2 Schema-Agnostic Queries
- 3 Challenge Description
- 4 Evaluation Description
- 4.1 Schema-Agnostic SPARQL Query
- 4.2 Schema-Agnostic Keyword Query
- 4.3 Returned Result
- 5 Schema-Agnostic Mappings
- 6 Results
- 7 Summary
- References
- UMBC_Ebiquity-SFQ: Schema Free Querying System
- Abstract
- 1 Introduction
- 2 Semantic Similarity Component
- 3 Concept Level Association Knowledge Model (CAK Model)
- 4 Query Interpretation
- 5 Type Inference and Property Mapping
- 6 SPARQL Query Generation and Selection
- 7 System II
- 8 Evaluation and Discussion
- 9 Conclusions
- References
- Concept-Level Sentiment Analysis Challenge (CLSA2015)
- ESWC 15 Challenge on Concept-Level Sentiment Analysis
- 1 Introduction
- 2 Related Work
- 3 Tasks, Datasets and Evaluation Measures
- 3.1 Task 1: Polarity Detection
- 3.2 Task 2: Aspect-Based Sentiment Analysis
- 3.3 Task 3: Frame Entities Identification
- 3.4 Task 4: The Most Innovative Approach
- 4 Submitted Systems
- 5 Results
- 5.1 Task 1
- 5.2 Task 3
- 5.3 The Most Innovative Approach Task
- 6 Conclusions
- References
- The Benefit of Concept-Based Features for Sentiment Analysis
- 1 Introduction
- 2 Related Work
- 3 Sentence Polarity Classification
- 3.1 Data
- 3.2 Evaluation
- 4 Aspect Detection
- 4.1 Data
- 4.2 Evaluation
- 5 Aspect Polarity Classification
- 5.1 Evaluation
- 6 Conclusion
- References
- An Information Retrieval-Based System for Multi-domain Sentiment Analysis
- 1 Introduction
- 2 Related Work
- 3 The SHELLFBK System
- 3.1 Indexes Construction
- 3.2 Domain and Polarity Computation
- 4 In-Vitro Evaluation and Challenge Results
- 4.1 In-Vitro Evaluation
- 4.2 Participation at ESWC 2015 Challenge on Semantic Sentiment Analysis
- 5 Conclusion
- References
- Detecting Sentiment Polarities with Sentilo
- 1 Introduction
- 2 Description of Sentilo
- 3 Addressing the Concept-Level Sentiment Analysis Challenge
- 4 Results
- 5 Conclusions
- References
- Supervised Opinion Frames Detection with RAID
- 1 Introduction
- 2 Related Work
- 2.1 Approaches for Opinion Frames Extraction
- 2.2 Datasets Annotated with Opinion Frames
- 3 The RAID Pipeline
- 3.1 Pre-processing
- 3.2 Extraction of Opinion Expressions
- 3.3 Extraction of Opinion Holders/Targets
- 3.4 Polarity Classification
- 4 Evaluation
- 5 Conclusions and Future Work
- References
- Author Index
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