Knowledge Graphs can be considered as fulfilling an early vision in Computer Science of creating intelligent systems that integrate knowledge and data at large scale. Stemming from scientific advancements in research areas of Semantic Web, Databases, Knowledge representation, NLP, Machine Learning, among others, Knowledge Graphs have rapidly gained popularity in academia and industry in the past years. The integration of such disparate disciplines and techniques give the richness to Knowledge Graphs, but also present the challenge to practitioners and theoreticians to know how current advances develop from early techniques in order, on one hand, take full advantage of them, and on the other, avoid reinventing the wheel. This talk will provide a quick historical context on the roots of Knowledge Graphs grounded in the advancements of Logic, Data, and the combination thereof.
At the moment, Knowledge Graphs are considered one of the best at organizing data sources and helping companies form better connections with entities of interest. Juan Sequeda is the principle scientist at data.world and he is here to talk about the history of Knowledge Graph's main ideas. Juan wants to provide the insight on the history of knowledge graphs in this talk today and he wants the viewers to takeaway is to be critical and that this isn't the newest and latest in technology, but rather that knowledge graphs are giants that users must acknowledge and learn about in the advancements it has created. #knowledgegraphs #knowledgegraphconference #knowledgegraphexplained