Proceedings of the International Conference on Web Intelligence 2017
DOI: 10.1145/3106426.3109448
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Knowledge will propel machine understanding of content

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 t… Show more

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Cited by 17 publications
(14 citation statements)
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“…Enhancing learning: AI techniques including ML algorithms which learn from pre-labeled examples are acknowledging that "data alone is not enough" [5]. There is a growing body of work seeking to demonstrate how and how much use of domain knowledge improves the results or effectiveness of state of the art ML and NLP techniques [6]. Learning the underlying patterns in the data goes beyond instancebased generalization to some external knowledge represented in structured graphs or networks.…”
Section: Kg Enabled Web and Enterprise Applicationsmentioning
confidence: 99%
“…Enhancing learning: AI techniques including ML algorithms which learn from pre-labeled examples are acknowledging that "data alone is not enough" [5]. There is a growing body of work seeking to demonstrate how and how much use of domain knowledge improves the results or effectiveness of state of the art ML and NLP techniques [6]. Learning the underlying patterns in the data goes beyond instancebased generalization to some external knowledge represented in structured graphs or networks.…”
Section: Kg Enabled Web and Enterprise Applicationsmentioning
confidence: 99%
“…In contrast to this literature, our work is grounded in the social science literature and incorporates domain-specific resources [30,31,52] in the model to better understand and detect extremist content using linguistic approaches. As Islamist extremism is a complex issue that involves different contexts, traditional approaches do not adequately capture important nuances in the language related to the multiple contextual dimensions of the problem.…”
Section: Related Workmentioning
confidence: 99%
“…Subsequently, background knowledge has played a key role in various tasks ranging from search and classification to personalized recommendations. In this decade, several researchers have explored the role of background knowledge to enhance natural language processing and machine learning [17].…”
mentioning
confidence: 99%