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The ever-growing datasets published on Linked Open Data mainly containencyclopedic information. However, there is a lack of quality structured andsemantically annotated datasets extracted from unstructured real-time sources.In this paper, we present principles for developing a knowledge graph ofinterlinked events using the case study of news headlines published on Twitterwhich is a real-time and eventful source of fresh information. We represent theessential pipeline containing the required tasks ranging from choosingbackground data model, event annotation (i.e., event recognition andclassification), entity annotation and eventually interlinking events. Thestate-of-the-art is limited to domain-specific scenarios for recognizing andclassifying events, whereas this paper plays the role of a domain-agnosticroad-map for developing a knowledge graph of interlinked events.

Principles for Developing a Knowledge Graph of Interlinked Events from News Headlines on Twitter