Semantic Filtering for Social Data

By Amit P. Sheth, Pavan Kapanipathi. 2016

239 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Statistical and Nonlinear Physics

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Citations in the corpus

Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “As the inclusion of structured knowledge (e.g., Knowledge Graphs) in deep learning, improves information retrieval (Sheth and Kapanipathi 2016), prior research has shown the signiﬁcance of knowledge in the pursuit of improving NLMs, such as in commonsense reasoning (Liu and Singh 2004).”
Gateways to the FANTOM5 promoter level mammalian expression atlas (2015, background): “Nano-publications expose individual records allowing automatic integration with any other linked data [56,57] and for citation tracking of their impact [58].”
Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples (2016, background): “For example, in [35], personal knowledge graphs are created based on the content consumed by a user, taking into account the dynamically changing vocabulary, and this is applied to improve subsequent filtering of relevant content.”
Predictive Analysis on Twitter: Techniques and Applications (2018, methodology): “ant[72] data from a dierent context into the dataset. Therefore, a suitable ltering mechanism is essential for better quality data with high recall as well as precision. A semantic ltering mechanism [8, 73] as in the Twitris platform, can be implemented that selects and employs a domain-specic knowl”
Understandable Big Data: A survey (2015, methodology): “Many authors [1,12,11,9,13,4] explicitly use the Three V’s (Volume, Variety and Velocity) to characterize Big Data.”
Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms (2014, methodology): “Regarding the various sensor types, the use of the technologies developed in the semantic web [7] such as ontologies, semantic annotation, linked data [6] and semantic web services have recently gained momentum.”
What's ur Type? Contextualized Classification of User Types in Marijuana-Related Communications Using Compositional Multiview Embedding (2018, methodology): “Data collection involved semantic filtering [13] utilizing the DAO6 on the eDrugTrends7/Twitris platform8.”