|Title||Adaptive Knowledge Networks: A Time Capsule|
|Year of Publication||2019|
|Authors||Swati Padhee, Anurag Illendula, Amit Sheth, Krishnaprasad Thirunarayan, Valerie Shalin|
|Venue||CRA-W Grad Cohort, Chicago|
|Keywords||Adaptive Knowledge Networks, Dynamic Knowledge Graphs, Temporal Information Retrieval|
Change is a law of nature, and static KGs like DBpedia fail to capture this dynamic flow of information. Diverse applications of AI are increasingly relying on the knowledge which is not necessarily static e.g., President_of_the_USA, champion_of_FIFA_World_Cup are temporally-sensitive facts, unlike birth_date or death_date. The need for accurate temporal query responses by dominant search engines requires extracting, maintaining, and updating the temporal facts in KGs. Analyzing real-world dynamic events (e.g., elections, natural disasters, etc.) requires real-time predictive analysis, trend analysis, spatiotemporal decision making, and public opinion analysis. In this project, we propose to curate Adaptive Knowledge Networks from incoming real-time multimodal spatiotemporally evolving data which change with time.
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