Knowledge Graphs and their Central Role in Big Data Processing: Past, Present, and Future


Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox [1]. 

Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques [2]. 

Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data [3, 4]. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems.  Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.  

[1] [2] [3] [4] 



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