Knowledge will propel machine understanding of content: Extrapolating from current examples

Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

Original languageEnglish
Title of host publicationWI '17: Proceedings of the International Conference on Web Intelligence
PublisherAssociation for Computing Machinery, Inc
Pages1-9
Number of pages9
ISBN (Electronic)9781450349512
DOIs
StatePublished - Aug 23 2017
Event16th IEEE/WIC/ACM International Conference on Web Intelligence - Leipzig, Germany
Duration: Aug 23 2017Aug 26 2017
Conference number: 16

Conference

Conference16th IEEE/WIC/ACM International Conference on Web Intelligence
Abbreviated titleWI 2017
Country/TerritoryGermany
CityLeipzig
Period8/23/178/26/17

ASJC Scopus Subject Areas

  • Computer Networks and Communications
  • Artificial Intelligence
  • Software

Keywords

  • Emoji sense disambiguation
  • Implicit entity recognition
  • Knowledge-driven deep content understanding
  • Knowledge-enhanced machine learning
  • Knowledgeenhanced nlp
  • Machine intelligence
  • Multimodal exploitation
  • Personalized digital health
  • Semantic-cognitive-perceptual computing
  • Understanding complex text

Disciplines

  • Bioinformatics
  • Communication
  • Communication Technology and New Media
  • Computer Sciences
  • Databases and Information Systems
  • Life Sciences
  • OS and Networks
  • Physical Sciences and Mathematics
  • Science and Technology Studies
  • Social and Behavioral Sciences

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