# Augmenting Scientific Creativity with Retrieval across Knowledge Domains

arXiv: https://arxiv.org/abs/2206.01328 · HTML: https://mlsanity.com/paper/2206.01328/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 75 references that are in the map.
- First posted on arXiv 2 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [Unsupervised Domain Clusters in Pretrained Language Models](https://mlsanity.com/paper/2004.02105/) (2020)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [Citation Recommendation: Approaches and Datasets](https://mlsanity.com/paper/2002.06961/) (2020)
- [Multi-Vector Models with Textual Guidance for Fine-Grained Scientific Document Similarity](https://mlsanity.com/paper/2111.08366/) (2021)
- [Accelerating Innovation Through Analogy Mining](https://mlsanity.com/paper/1706.05585/) (2017)
- [Augmenting Scientific Creativity with an Analogical Search Engine](https://mlsanity.com/paper/2205.15476/) (2022)
- [Aspect-based Document Similarity for Research Papers](https://mlsanity.com/paper/2010.06395/) (2020)
- [CSFCube -- A Test Collection of Computer Science Research Articles for Faceted Query by Example](https://mlsanity.com/paper/2103.12906/) (2021)
- [SciSight: Combining faceted navigation and research group detection for COVID-19 exploratory scientific search](https://mlsanity.com/paper/2005.12668/) (2020)
- [Neural Embeddings of Scholarly Periodicals Reveal Complex Disciplinary Organizations](https://mlsanity.com/paper/2001.08199/) (2020)
- [Bursting Scientific Filter Bubbles: Boosting Innovation via Novel Author Discovery](https://mlsanity.com/paper/2108.05669/) (2021)
- [Cross Domain Regularization for Neural Ranking Models Using Adversarial Learning](https://mlsanity.com/paper/1805.03403/) (2018)
- [Learning Disentangled Representations of Texts with Application to Biomedical Abstracts](https://mlsanity.com/paper/1804.07212/) (2018)
- [From Who You Know to What You Read: Augmenting Scientific Recommendations with Implicit Social Networks](https://mlsanity.com/paper/2204.10254/) (2022)
- [Domain Adaptation for Enterprise Email Search](https://mlsanity.com/paper/1906.07897/) (2019)

## Built on by (most cited first, 7 of 7)

- [IdeaSynth: Iterative Research Idea Development Through Evolving and Composing Idea Facets with Literature-Grounded Feedback](https://mlsanity.com/paper/2410.04025/) (2024)
- [CoQuest: Exploring Research Question Co-Creation with an LLM-based Agent](https://mlsanity.com/paper/2310.06155/) (2023)
- [PaperWeaver: Enriching Topical Paper Alerts by Contextualizing Recommended Papers with User-collected Papers](https://mlsanity.com/paper/2403.02939/) (2024)
- [DeepInnovator: Triggering the Innovative Capabilities of LLMs](https://mlsanity.com/paper/2602.18920/) (2026)
- [Abduction Without a Body? Representational Grounding and the Abduction Loop for Scientific Hypothesis Generation](https://mlsanity.com/paper/2608.02505/) (2026)
- [How Data Scientists Review the Scholarly Literature](https://mlsanity.com/paper/2301.03774/) (2023)
- [Same Problem, Different Field: Cross-Domain Solution Import via Domain-Stripped Computational Fingerprints](https://mlsanity.com/paper/2609.07595/) (2026)

## Abstract (arXiv)

Exposure to ideas in domains outside a scientist's own may benefit her in reformulating existing research problems in novel ways and discovering new application domains for existing solution ideas. While improved performance in scholarly search engines can help scientists efficiently identify relevant advances in domains they may already be familiar with, it may fall short of helping them explore diverse ideas textitoutside such domains. In this paper we explore the design of systems aimed at augmenting the end-user ability in cross-domain exploration with flexible query specification. To this end, we develop an exploratory search system in which end-users can select a portion of text core to their interest from a paper abstract and retrieve papers that have a high similarity to the user-selected core aspect but differ in terms of domains. Furthermore, end-users can `zoom in' to specific domain clusters to retrieve more papers from them and understand nuanced differences within the clusters. Our case studies with scientists uncover opportunities and design implications for systems aimed at facilitating cross-domain exploration and inspiration.