
Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence
Description
Alles über E-Books | Antworten auf Fragen rund um E-Books, Kopierschutz und Dateiformate finden Sie in unserem Info- & Hilfebereich.
This book constitutes the refereed proceedings of the Second China Conference on Knowledge Graph and Semantic Computing, CCKS 2017, held in Chengdu, China, in August 2017.
The 11 revised full papers and 6 revised short papers presented were carefully reviewed and selected from 85 submissions. The papers cover wide research fields including the knowledge graph, the Semantic Web, linked data, NLP, knowledge representation, graph databases.
More details
Other editions
Additional editions

Content
- Intro
- Preface
- Organization
- Contents
- Knowledge Base Completion by Learning to Rank Model
- 1 Introduction
- 2 Learning to Rank Model
- 3 Experiments
- 4 Related Work
- 5 Conclusion
- References
- Path-Based Learning for Plant Domain Knowledge Graph
- Abstract
- 1 Introduction
- 2 Approach
- 2.1 TransE Model
- 2.2 PTA Model
- 2.3 PTA (Path-Based TransE for Attributes)
- 2.4 Model Formulation
- 3 Experiments
- 3.1 Setup
- 3.2 Evaluation
- 4 Conclusion
- References
- A Graph-Based Approach to Incremental Classification in OWL 2 QL Ontology
- 1 Introduction
- 2 Preliminaries
- 2.1 OWL 2 QL
- 2.2 Graph Theory Notions
- 2.3 Digraph Representation of OWL QL 2 Ontologies
- 3 Mapping Ontologies Direct Graphs
- 4 Identifying Affected Paths and Updating Transitive Closure
- 5 Optimization
- 6 Implementation and Evaluation
- 7 Discussions
- References
- Tensor-Based Representation and Reasoning of Horn-SHOIQ Ontologies
- 1 Introduction
- 2 Ontology and Tensor Operations
- 3 A Tensor-Based Representation for Ontologies
- 4 Materialization via Tensor Operations
- 5 Conclusions and Future Work
- References
- Attention-Based Event Relevance Model for Stock Price Movement Prediction
- 1 Introduction
- 2 Related Works
- 3 Methodology
- 3.1 Problem Formulation
- 3.2 Attention-Based Event Relevance Model
- 3.3 Model Training
- 4 Experiments and Results
- 4.1 Dataset Construction
- 4.2 Implementation and Hyper-Parameter Setting
- 4.3 Stock Price Movement Prediction Experiments
- 4.4 Short-Term, Medium-Term, Long-Term Influence
- 5 Conclusion and Future Work
- References
- A Survey on Relation Extraction
- Abstract
- 1 Introduction
- 2 Datasets for Relation Extraction
- 3 Mainstream Methods
- 3.1 Rule-Based Approaches
- 3.2 Statistic-Based Approaches
- 4 Open Information Extraction (OIE)
- 5 Challenges and Directions
- 6 Conclusion
- Acknowledgment
- References
- A Sentiment and Topic Model with Timeslice, User and Hashtag for Posts on Social Media
- 1 Introduction
- 2 Sentiment Topic Model for Posts
- 3 Experiment
- 3.1 Dataset Description and Parameter Settings
- 3.2 Topic Coherence
- 3.3 Topic Visualization
- 4 Conclusion and Future Work
- References
- Collective Entity Linking Based on DBpedia
- Abstract
- 1 Introduction
- 2 Relate Work
- 3 Preliminary
- 4 Method Description
- 4.1 Candidate Entity Generation
- 4.2 Candidate Entity Selection
- 5 Evaluation
- 5.1 Dataset Description
- 5.2 Evaluation Criteria
- 5.3 Experimental Results
- 6 Conclusion and Future Work
- Acknowledgements
- References
- A CWTM Model of Topic Extraction for Short Text
- Abstract
- 1 Introduction
- 2 Related Work
- 3 A Novel Topic Modeling
- 3.1 Couple Word
- 3.2 Our Approach
- 4 Experiments and Results
- 5 Conclusions and Future Work
- References
- Micro-blog User Community Detection by Focusing on Micro-blog Content and Community Structure
- 1 Introduction
- 2 Related Work
- 3 Establish the Micro-blog Network
- 4 Detect the Objects' Clustering Directions
- 5 Detect Community
- 6 Experiments
- 6.1 Evaluate by the Interest Cohesion
- 6.2 Evaluate by the Community Structure
- 7 Conclusion and Future Work
- References
- Embedding Syntactic Tree Structures into CNN Architecture for Relation Classification
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Our Method
- 3.1 Feature Extraction
- 3.1.1 Syntactic Parsing Tree Embedding Features
- 3.1.2 Lexical Level Features
- 3.1.3 Entities Parsing Tree Embedding Features
- 3.2 Convolution Operation
- 3.3 Max-pooling Operation
- 3.4 Linear Transformation
- 3.5 Output
- 3.6 Dropout Operation
- 3.7 Training Procedure
- 4 Experiments
- 5 Conclusions
- Acknowledgements
- References
- Tracking Topic Trends for Short Texts
- 1 Introduction
- 2 Related Work
- 3 Topic Trend Detection (TTD) Model
- 3.1 Pre-process Stage
- 3.2 An Optimized Topic Model
- 3.3 Auxiliary Word Embeddings
- 4 Experiments
- 4.1 Datasets
- 4.2 Experimental Setup
- 4.3 Evaluation by Topic Coherence
- 4.4 Trend Detection in Microblog
- 5 Conclusion and Future Work
- References
- BSBM+: Extending BSBM to Evaluate Annotated RDF Features on Graph Databases
- 1 Introduction
- 2 Related Work
- 2.1 Annotated RDF
- 2.2 Benchmarks
- 2.3 Temporal and Geospatial Operators
- 3 Data Model
- 3.1 Annotation
- 3.2 Annotated RDF
- 3.3 Extended SPARQL and Extended Graph Engines
- 4 Benchmark Workflow
- 5 Dataset
- 5.1 Distribution of Annotation Type and Annotation Number
- 5.2 Distribution of the Annotation Values
- 6 Experiment
- 6.1 Metrics
- 6.2 Graph Engines Chosen and Experiment Circumstance
- 6.3 Engine Extension Module
- 6.4 SPARQL Extension Module
- 7 Results
- 7.1 Engine Extension Module
- 7.2 SPARQL Extension Module
- 8 Conclusion and Future Work
- References
- Detecting Spammers in Sina Micro-blog Based on Multiple Features
- Abstract
- 1 Introduction
- 2 Related Work
- 3 Advertising Spammers Detecting Model
- 3.1 Basic Definitions
- 3.2 Similarity Detection
- 4 Experiment and Analysis on Dataset
- 4.1 Dataset and Preprocessing
- 4.2 Evaluation Criterion
- 5 Conclusions and Future Work
- References
- A Hybrid Method to Sentiment Analysis for Chinese Microblog
- 1 Introduction
- 2 Related Work
- 2.1 Lexicon-Based
- 2.2 Machine Leaning Method
- 3 A Hybrid Method to the Sentiment Analysis for Chinese Microblog (SAFCM)
- 3.1 Construction of Sentiment Lexicon
- 3.2 Word Embedding
- 3.3 Clustering
- 4 Experiments
- 4.1 Data Sets
- 4.2 Experiment Results
- 5 Conclusion and Future Work
- References
- A User Personality-Similarity Model for Personalized Followee Recommendation in SINA Microblog
- 1 Introduction
- 2 Related Work
- 3 Our Method
- 3.1 User-Based Factor
- 3.2 Personality-Based Factors
- 4 Experiment
- 4.1 Results
- 5 Conclusions
- References
- CrowdGeoKG: Crowdsourced Geo-Knowledge Graph
- 1 Introduction
- 2 Technical Framework
- 2.1 Schema Design
- 2.2 Data Transformation and Linking
- 3 Dataset Exploitation
- 4 Conclusion and Future Work
- References
- Author Index
System requirements
File format: PDF
Copy protection: Watermark-DRM (Digital Rights Management)
System requirements:
- Computer (Windows; MacOS X; Linux): Use the free software Adobe Reader, Adobe Digital Editions, or any other PDF viewer of your choice (see eBook Help).
- Tablet/Smartphone (Android; iOS): Install the free app Adobe Digital Editions or another reading app for eBooks, e.g., PocketBook (see eBook Help).
- E-reader: Bookeen, Kobo, Pocketbook, Sony, Tolino and many more (only limited: Kindle).
The file format PDF always displays a book page identically on any hardware. This makes PDF suitable for complex layouts such as those used in textbooks and reference books (images, tables, columns, footnotes). Unfortunately, on the small screens of e-readers or smartphones, PDFs are rather annoying, requiring too much scrolling.
This eBook uses Watermark-DRM, a „soft” copy protection. This means that there are no technical restrictions to prevent illegal distribution. However, there is a personalised watermark embedded in the eBook that can be used to identify the purchaser of the eBook in the event of misuse and to provide evidence for legal purposes.
For more information, see our eBook Help page.