Uncovering Relations for Marketing Knowledge Representation
This work addresses the need for an automated marketing knowledge base to aid human managers, focusing on knowledge representation from dispersed corpora, but it is incremental as it builds on existing knowledge graph techniques for a specific domain.
The paper tackles the problem of creating a marketing knowledge graph from text corpora by defining a set of relations and proposing a pipeline with a rule-guided semi-supervised algorithm for relation prediction, addressing the challenge of non-factoid marketing knowledge.
Online behaviors of consumers and marketers generate massive marketing data, which ever more sophisticated models attempt to turn into insights and aid decisions by marketers. Yet, in making decisions human managers bring to bear marketing knowledge which reside outside of data and models. Thus, it behooves creation of an automated marketing knowledge base that can interact with data and models. Currently, marketing knowledge is dispersed in large corpora, but no definitive knowledge base for marketing exists. Out of the two broad aspects of marketing knowledge - representation and reasoning - this treatise focuses on the former. Specifically, we focus on creation of marketing knowledge graph from corpora, which requires identification of entities and relations. The relation identification task is particularly challenging in marketing, because of the non-factoid nature of much marketing knowledge, and the difficulty of forming rules that govern relations. Specifically, we define a set of relations to capture marketing knowledge, propose a pipeline for creating the knowledge graph from text and propose a rule-guided semi-supervised relation prediction algorithm to extract relations between marketing entities from sentences.