Freebase: a massive, collaboratively edited database of cross-linked data. Touted as “an openly shared database of the world’s knowledge”. It was bought by Google and used to power its own KG. In 2015, it was finally discontinued. Vrandečić, D., Krötzsch, M.: Wikidata: a free collaborative knowledgebase. Commun. ACM 57(10), 78–85 (2014)

How to use machine learning to enrich your knowledge graph and mine features from a knowledge graph to create accurate predictive models. The types of entities and relationships in a knowledge graph are not limited, and new ones will be added over their lifetime. Your initial knowledge graph may contain information about locations and restaurants, but then you decide to extend it with details on the types of cuisine and ingredients served at the restaurants or maybe with other types of local businesses like hair salons, bookstores, or dry cleaners.G. Qi, J. Tang, J. Du, J.Z. Pan, Y. Yu (eds.), Linked Data and Knowledge Graph—7th Chinese Semantic Web Symposium and 2nd Chinese Web Science Conference (CSWS2013): Revised Selected Papers, Shanghai, China, 12–16 August 2013. Springer CCIS, vol. 406

In spite of having several open-source KGs, we may have a requirement to create domain-specific KG for our use case. There, our base data (from which we want to create the KG), could be of multiple types — tabular, graphical, or text blob. We will cover some steps on how to create KG from unstructured data like text, as it’s relatively easier to convert structured data into KG using minimal domain knowledge and scripting. The complete process can be divided into two steps, If you’re a data scientist, you will see a knowledge graph as an augmented feature store for enriched (connected) data, where you will be able to compute and access (and operationalize and govern) structural features for ML. Think of centrality metrics for a given data point, the different data clusters it belongs to, or the distance to a given point in the graph. All these features completely escape table-based datasets and significantly improve the accuracy of your predictive models. Example of knowledge graph-based knowledge panel used by Google. [Right] the actual panel shown by google when you search for Einstein. [left] recreation of how we might store similar information in a KG. Source: by Author + Google. Finally, once we have prepared the script (with ttl extension — for scripts in Turtle language), that script contains the complete schema and definition of our KG. In itself, this may not be interesting, hence the file can be imported into any KG database for beautiful visualization and efficient querying.F.M. Suchanek, G. Kasneci, G. Weikum, Yago: a core of semantic knowledge, in Proceedings of the 16th International World Wide Web Conference (WWW2007), 8–12 May 2007 (ACM, Banff, Canada) To better under Knowledge graphs, let's start by understanding its basic unit i.e. a “fact”. A fact is the most basic piece of information that can be stored in a KG. Facts can be represented in form of triplets in either of the ways, Heterogenous data: supports different types of entities (person, date, painting, etc) and relations (likes, born on, etc). Classical algorithms considered user-item interactions to generate recommendations. Over time, newly created algorithms started considering additional information about the user as well as items to improve the recommendations. World Travel & Tourism Council, Travel & Tourism Economic Impact 2018 World (2018). https://www.wttc.org/-/media/files/reports/economic-impact-research/regions-2018/world2018.pdf