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

arXiv: https://arxiv.org/abs/2207.05188 · HTML: https://mlsanity.com/paper/2207.05188/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 48 references that are in the map.
- First posted on arXiv 11 Jul 2022.

## Builds on (most cited first, 15 of 15)

- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [A Survey on Knowledge Graph-Based Recommender Systems](https://mlsanity.com/paper/2003.00911/) (2020)
- [Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references](https://mlsanity.com/paper/1402.4578/) (2014)
- [GenIE: Generative Information Extraction](https://mlsanity.com/paper/2112.08340/) (2021)
- [Robust Retrieval Augmented Generation for Zero-shot Slot Filling](https://mlsanity.com/paper/2108.13934/) (2021)
- [Generative Relation Linking for Question Answering over Knowledge Bases](https://mlsanity.com/paper/2108.07337/) (2021)
- [Edge: Enriching Knowledge Graph Embeddings with External Text](https://mlsanity.com/paper/2104.04909/) (2021)
- [Question Answering on Scholarly Knowledge Graphs](https://mlsanity.com/paper/2006.01527/) (2020)
- [Generate FAIR Literature Surveys with Scholarly Knowledge Graphs](https://mlsanity.com/paper/2006.01747/) (2020)
- [KGI: An Integrated Framework for Knowledge Intensive Language Tasks](https://mlsanity.com/paper/2204.03985/) (2022)
- [Detection, Analysis, and Prediction of Research Topics with Scientific Knowledge Graphs](https://mlsanity.com/paper/2106.12875/) (2021)
- [Leveraging a Federation of Knowledge Graphs to Improve Faceted Search in Digital Libraries](https://mlsanity.com/paper/2107.05447/) (2021)
- [Demonstration of Faceted Search on Scholarly Knowledge Graphs](https://mlsanity.com/paper/2107.05738/) (2021)

## Built on by (most cited first, 2 of 2)

- [KnowGL: Knowledge Generation and Linking from Text](https://mlsanity.com/paper/2210.13952/) (2022)
- [Scaling Knowledge Graphs for Automating AI of Digital Twins](https://mlsanity.com/paper/2210.14596/) (2022)

## Abstract (arXiv)

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.