Relationship Extraction: Fundamentals and Applications

Β· One Billion Knowledgeable Β· αž”αžΆαž“αž’αžΆαž“αžŠαŸ„αž™ AI αžŠαŸ„αž™ Mason (αž–αžΈ Google)
αžŸαŸ€αžœαž—αŸ…β€‹αž‡αžΆβ€‹αžŸαŸ†αž‘αŸαž„
2 វិ 8 αž“
αž˜αž·αž“β€‹αžŸαž„αŸ’αžαŸαž”
αž˜αžΆαž“αžŸαž·αž‘αŸ’αž’αž·
αž’αžΆαž“αžŠαŸ„αž™ AI
αž€αžΆαžšαžœαžΆαž™αžαž˜αŸ’αž›αŸƒ αž“αž·αž„αž˜αžαž·αžœαžΆαž™αžαž˜αŸ’αž›αŸƒαž˜αž·αž“αžαŸ’αžšαžΌαžœαž”αžΆαž“αž•αŸ’αž‘αŸ€αž„αž•αŸ’αž‘αžΆαžαŸ‹αž‘αŸ αžŸαŸ’αžœαŸ‚αž„αž™αž›αŸ‹αž”αž“αŸ’αžαŸ‚αž˜
αž…αž„αŸ‹αž”αžΆαž“αž‚αŸ†αžšαžΌ 12 αž“αžΆαž‘αžΈ αž˜αŸ‚αž“αž‘αŸ? αžŸαŸ’αžŠαžΆαž”αŸ‹αž”αžΆαž“β€‹αž‚αŸ’αžšαž”αŸ‹αž–αŸαž› αž‘αŸ„αŸ‡αž”αžΈαž‡αžΆαž‚αŸ’αž˜αžΆαž“αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžαž€αŸαžŠαŸ„αž™αŸ”Β 
αž”αž“αŸ’αžαŸ‚αž˜
αž”αž‰αŸ’αž…αž»αŸ‡αžαž˜αŸ’αž›αŸƒ 30% αž“αŸ…αžαŸ’αž„αŸƒαž‘αžΈ 4 αž§αžŸαž—αžΆ

αž’αŸ†αž–αžΈαžŸαŸ€αžœαž—αŸ…β€‹αž‡αžΆαžŸαŸ†αž‘αŸαž„αž“αŸαŸ‡

What Is Relationship Extraction


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.

αž’αŸ†αž–αžΈβ€‹αž’αŸ’αž“αž€αž“αž·αž–αž“αŸ’αž’

Fouad Sabry is the former Regional Head of Business Development for Applications at HP in Southern Europe, Middle East, and Africa (SEMEA). Fouad has received his B.Sc. of Computer Systems and Automatic Control in 1996, dual masterҀ™s degrees from University of Melbourne (UoM) in Australia, Master of Business Administration (MBA) in 2008, and Master of Management in Information Technology (MMIT) in 2010.Β 

Fouad has more than 20 years of experience in Information Technology and Telecommunications fields, working in local, regional, and international companies, such as Vodafone and IBM in Middle East and Africa (MEA) region. Fouad joined HP Middle East (ME), based in Dubai, United Arab Emirates (UAE) in 2013 and helped develop the software business in tens of markets across Southern Europe, Middle East, and Africa (SEMEA) regions. Currently, Fouad is an entrepreneur, author, futurist, focused on Emerging Technologies, and Industry Solutions, and founder of One Billion Knowledgeable (1BK) Initiative.

αžœαžΆαž™αžαž˜αŸ’αž›αŸƒβ€‹αžŸαŸ€αžœαž—αŸ…αž‡αžΆαžŸαŸ†αž‘αŸαž„αž“αŸαŸ‡

αž”αŸ’αžšαžΆαž”αŸ‹αž™αžΎαž„αž’αŸ†αž–αžΈαž€αžΆαžšαž™αž›αŸ‹αžƒαžΎαž‰αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€αŸ”

αž–αŸαžαŸŒαž˜αžΆαž“αž’αŸ†αž–αžΈαž€αžΆαžšαžŸαŸ’αžŠαžΆαž”αŸ‹

αž‘αžΌαžšαžŸαž–αŸ’αž‘αž†αŸ’αž›αžΆαžαžœαŸƒ αž“αž·αž„β€‹αžαŸαž”αŸ’αž›αŸαž
αžŠαŸ†αž‘αžΎαž„αž€αž˜αŸ’αž˜αžœαž·αž’αžΈ Google Play Books αžŸαž˜αŸ’αžšαžΆαž”αŸ‹ Android αž“αž·αž„ iPad/iPhone αŸ” αžœαžΆβ€‹αž’αŸ’αžœαžΎαžŸαž˜αž€αžΆαž›αž€αž˜αŸ’αž˜β€‹αžŠαŸ„αž™αžŸαŸ’αžœαŸαž™αž”αŸ’αžšαžœαžαŸ’αžαž·αž‡αžΆαž˜αž½αž™β€‹αž‚αžŽαž“αžΈβ€‹αžšαž”αžŸαŸ‹αž’αŸ’αž“αž€β€‹ αž“αž·αž„β€‹αž’αž“αž»αž‰αŸ’αž‰αžΆαžαž±αŸ’αž™β€‹αž’αŸ’αž“αž€αž’αžΆαž“αž–αŸαž›β€‹αž˜αžΆαž“αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αž αž¬αž‚αŸ’αž˜αžΆαž“β€‹αž’αŸŠαžΈαž“αž’αžΊαžŽαž·αžβ€‹αž“αŸ…αž‚αŸ’αžšαž”αŸ‹αž‘αžΈαž€αž“αŸ’αž›αŸ‚αž„αŸ”
αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšβ€‹αž™αž½αžšαžŠαŸƒ αž“αž·αž„αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžš
αž’αŸ’αž“αž€β€‹αž’αžΆαž…β€‹αž’αžΆαž“β€‹αžŸαŸ€αžœαž—αŸ…β€‹β€‹αžŠαŸ‚αž›β€‹αž”αžΆαž“β€‹αž‘αž·αž‰β€‹β€‹αž“αŸ…β€‹αž–αŸαž›β€‹β€‹β€‹αž€αž˜αŸ’αžŸαžΆαž“αŸ’αž Google αžŠαŸ„αž™β€‹αž”αŸ’αžšαžΎβ€‹αž€αž˜αŸ’αž˜αžœαž·αž’αžΈβ€‹αžšαž»αž€αžšαž€β€‹β€‹αž”αžŽαŸ’αžŠαžΆαž‰β€‹αž€αž»αŸ†αž–αŸ’αž™αžΌαž‘αŸαžšβ€‹αžšαž”αžŸαŸ‹β€‹β€‹αž’αŸ’αž“αž€αŸ”

αž…αŸ’αžšαžΎαž“αž‘αŸ€αžαžŠαŸ„αž™ Fouad Sabry

αžŸαŸ€αžœαž—αŸ…β€‹αž‡αžΆβ€‹αžŸαŸ†αž‘αŸαž„β€‹αžŸαŸ’αžšαžŠαŸ€αž„β€‹αž‚αŸ’αž“αžΆ

αž”αžšαž·αž™αžΆαž™β€‹αžŠαŸ„αž™ Mason