Knowledge Graph Induction enabling Recommending and Trend Analysis: A Corporate Research Community Use Case

TL;DR

This paper demonstrates how a corporate research community uses Semantic Web technologies to create a Knowledge Graph from diverse data sources, enabling improved recommendations and trend analysis to support research activities.

Contribution

It introduces a methodology for inducing a unified Knowledge Graph from multiple data types and patterns for API access, tailored to user needs in a corporate research setting.

Findings

Effective entity recommendation demonstrated empirically.

Identified common patterns for knowledge exploitation.

Enhanced accessibility of knowledge through APIs.

Abstract

A research division plays an important role of driving innovation in an organization. Drawing insights, following trends, keeping abreast of new research, and formulating strategies are increasingly becoming more challenging for both researchers and executives as the amount of information grows in both velocity and volume. In this paper we present a use case of how a corporate research community, IBM Research, utilizes Semantic Web technologies to induce a unified Knowledge Graph from both structured and textual data obtained by integrating various applications used by the community related to research projects, academic papers, datasets, achievements and recognition. In order to make the Knowledge Graph more accessible to application developers, we identified a set of common patterns for exploiting the induced knowledge and exposed them as APIs. Those patterns were born out of user research which identified the most valuable use cases or user pain points to be alleviated. We outline two distinct scenarios: recommendation and analytics for business use. We will discuss these scenarios in detail and provide an empirical evaluation on entity recommendation specifically. The methodology used and the lessons learned from this work can be applied to other organizations facing similar challenges.