關於本電子書
The identification and categorization of semantic relationship mentions within a collection of artifacts, most commonly taken from text or XML documents, is necessary for the completion of a job known as relationship extraction. The process is quite similar to that of information extraction (IE), although IE also needs the elimination of repeated relations (disambiguation) and generally refers to the extraction of a wide variety of various relationships. The goal is extremely similar.
How You Will Benefit
(I) Insights, and validations about the following topics:
Chapter 1: Relationship Extraction
Chapter 2: Semantic Network
Chapter 3: Ontology (computer science)
Chapter 4: Text Mining
Chapter 5: Information Extraction
Chapter 6: Relational Data Mining
Chapter 7: Semantic Similarity
Chapter 8: Ontology Learning
Chapter 9: Knowledge Extraction
Chapter 10: Knowledge Graph
(II) Answering the public top questions about relationship extraction.
(III) Real world examples for the usage of relationship extraction in many fields.
(IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of relationship extraction' technologies.
Who This Book Is For
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of relationship extraction.
