Graph Models for Knowledge Representation and Reasoning for Contemporary and Emerging Needs – A Survey

—Reasoning is the fundamental capability which\udrequires knowledge. Various graph models have proven\udto be very valuable in knowledge representation and\udreasoning. Recently, explosive data generation and\udaccumulation capabilities have paved way for Big Data\udand Data Intensive Systems. Knowledge Representation\udand Reasoning with large and growing data is extremely\udchallenging but crucial for businesses to predict trends\udand support decision making. Any contemporary,\udreasonably complex knowledge based system will have to\udconsider this onslaught of data, to use appropriate and\udsufficient reasoning for semantic processing of\udinformation by machines. This paper surveys graph based\udknowledge representation and reasoning, various graph\udmodels such as Conceptual Graphs, Concept Graphs,\udSemantic Networks, Inference Graphs and Causal\udBayesian Networks used for representation and reasoning,\udcommon and recent research uses of these graph models,\udtypically in Big Data environment, and the near future\udneeds and challenges for graph based KRR in computing\udsystems. Observations are presented in a table,\udhighlighting suitability of the surveyed graph models for\udcontemporary scenarios.\u