This lecture starts by addressing common myths about data and Artificial Intelligence that give reasons why knowledge acquisition and knowledge modeling should be a technology consideration. The common myths are 1) a database is enough to store any type of information 2) As a database grows to more than 10TB size, one must migrate to Big Data 3) Data Science is Artificial Intelligence 4) Adopting data-driven and Artificial Intelligence is enough. The difference between data, information, and knowledge brings the audience to a common definition of those three. Later, the lecture delves into taxonomy, ontology, relations, and knowledge graph. The difference between normal search engines and semantic search engines is also highlighted. Finally, the lecture explains the connection between Natural Language Processing and Knowledge Acquisition.