Uncovering Relations for Marketing Knowledge Representation

TL;DR

This paper presents a novel pipeline and rule-guided semi-supervised algorithm for creating a marketing knowledge graph from text corpora, addressing the challenge of extracting non-factoid relations in marketing data.

Contribution

It introduces a new approach for constructing marketing knowledge graphs by defining relations and developing a semi-supervised relation prediction method.

Findings

Proposed a set of relations to represent marketing knowledge.

Developed a pipeline for extracting entities and relations from text.

Demonstrated effectiveness of the relation prediction algorithm.

Abstract

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.