Adaptive Knowledge Networks: A Time Capsule

Swati Padhee; Anurag Illendula; Amit Sheth; Krishnaprasad Thirunarayan; Valerie L Shalin

Adaptive Knowledge Networks: A Time Capsule

Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie L Shalin

Research output: Contribution to conference › Poster

Abstract

❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai

❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events.

❖ Current state-of-the-art curates dynamic knowledge graph from structured text.

❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

Original language	American English
State	Published - Jan 1 2019

Disciplines

Bioinformatics
Communication Technology and New Media
Databases and Information Systems
Life Sciences
OS and Networks
Science and Technology Studies

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Adaptive Knowldge Networks A Time CapsuleFinal published version, 348 KBLicense: CC BY

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@conference{e630117e72344d749812104a3aaf645b,

title = "Adaptive Knowledge Networks: A Time Capsule",

abstract = " ❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai ❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events. ❖ Current state-of-the-art curates dynamic knowledge graph from structured text. ❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.",

author = "Swati Padhee and Anurag Illendula and Amit Sheth and Krishnaprasad Thirunarayan and Shalin, {Valerie L}",

year = "2019",

month = jan,

day = "1",

language = "American English",

}

TY - CONF

T1 - Adaptive Knowledge Networks

T2 - A Time Capsule

AU - Padhee, Swati

AU - Illendula, Anurag

AU - Sheth, Amit

AU - Thirunarayan, Krishnaprasad

AU - Shalin, Valerie L

PY - 2019/1/1

Y1 - 2019/1/1

N2 - ❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai ❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events. ❖ Current state-of-the-art curates dynamic knowledge graph from structured text. ❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

AB - ❖ Real world events are dynamic in nature Periodic events e.g. US Presidential Election Non-periodic events e.g. Cyclone Idai ❖ Need for real-time predictive analysis, trend analysis, spatio-temporal decision making, public opinion analysis for events. ❖ Current state-of-the-art curates dynamic knowledge graph from structured text. ❖ We propose creating an Adaptive Knowledge Network from incoming real-time multimodal spatio-temporally evolving data.

UR - https://corescholar.libraries.wright.edu/knoesis/1162

M3 - Poster

ER -

Adaptive Knowledge Networks: A Time Capsule

Abstract

Disciplines

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