Semantic Graph Knowledge Repository Based Knowledge Discovery System for Customer Value
Jiandong Li · Institutional Repositories DataBase (IRDB) · 2017
Retail data available for consumer-oriented company is a precious asset which can deliver useful insights in decision making and marketing strategy.KID (Data-Information-Knowledge) model is a generic from data to knowledge cognitive model.It bases on how human process outside information.It can be applied to retail business for supporting retail data analytics.Knowledge repository is a key element of KID model.In this paper, a retail semantic graph knowledge repository based knowledge discovery system is proposed and developed for KID model to apply in limited retail data.The proposed knowledge repository integrates Neo4j graph database, retail ontology designed by Maryam Fazel Zarandi and Jess rule engine.It interprets streaming data into meaningful information and assimilate meaningful information into graph knowledge repository to update knowledge by pre-embedded prior objective-oriented algorithms knowledge in algorithm pool.The deductive reasoning capability is provided by Jess rule engine, so it can deduce answers to retail queries from graph knowledge repository.A customer value assessment case study by using Recency-Frenquecy-Monetary (RFM) analytic model and K-means algorithms is given to demonstrate the proposed semantic graph knowledge repository based knowledge discovery system.