# Conversational Exploratory Search of Scholarly Publications Using Knowledge Graphs

arXiv: https://arxiv.org/abs/2410.00427 · HTML: https://mlsanity.com/paper/2410.00427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 20 of its 35 references that are in the map.
- First posted on arXiv 1 Oct 2024.

## Builds on (most cited first, 20 of 20)

- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Zephyr: Direct Distillation of LM Alignment](https://mlsanity.com/paper/2310.16944/) (2023)
- [Efficient Few-Shot Learning Without Prompts](https://mlsanity.com/paper/2209.11055/) (2022)
- [SciRepEval: A Multi-Format Benchmark for Scientific Document Representations](https://mlsanity.com/paper/2211.13308/) (2022)
- [The Semantic Scholar Open Data Platform](https://mlsanity.com/paper/2301.10140/) (2023)
- [Towards Knowledge-Based Recommender Dialog System](https://mlsanity.com/paper/1908.05391/) (2019)
- [Growth rates of modern science: A latent piecewise growth curve approach to model publication numbers from established and new literature databases](https://mlsanity.com/paper/2012.07675/) (2020)
- [Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings](https://mlsanity.com/paper/2202.06671/) (2022)
- [CLAM: Selective Clarification for Ambiguous Questions with Generative Language Models](https://mlsanity.com/paper/2212.07769/) (2022)
- [Leveraging Large Language Models in Conversational Recommender Systems](https://mlsanity.com/paper/2305.07961/) (2023)
- [Look before you Hop: Conversational Question Answering over Knowledge Graphs Using Judicious Context Expansion](https://mlsanity.com/paper/1910.03262/) (2019)
- [Investigating Conversational Search Behavior For Domain Exploration](https://mlsanity.com/paper/2301.04098/) (2023)
- [Graph Meets LLM: A Novel Approach to Collaborative Filtering for Robust Conversational Understanding](https://mlsanity.com/paper/2305.14449/) (2023)
- [Exploring the Landscape of Natural Language Processing Research](https://mlsanity.com/paper/2307.10652/) (2023)
- [A Comparative Analysis of Conversational Large Language Models in Knowledge-Based Text Generation](https://mlsanity.com/paper/2402.01495/) (2024)
- [From Data to Dialogue: Leveraging the Structure of Knowledge Graphs for Conversational Exploratory Search](https://mlsanity.com/paper/2310.05150/) (2023)
- [Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search](https://mlsanity.com/paper/2403.01747/) (2024)
- [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024)

## Abstract (arXiv)

Traditional search methods primarily depend on string matches, while semantic search targets concept-based matches by recognizing underlying intents and contextual meanings of search terms. Semantic search is particularly beneficial for discovering scholarly publications where differences in vocabulary between users' search terms and document content are common, often yielding irrelevant search results. Many scholarly search engines have adopted knowledge graphs to represent semantic relations between authors, publications, and research concepts. However, users may face challenges when navigating these graphical search interfaces due to the complexity and volume of data, which impedes their ability to discover publications effectively. To address this problem, we developed a conversational search system for exploring scholarly publications using a knowledge graph. We outline the methodical approach for designing and implementing the proposed system, detailing its architecture and functional components. To assess the system's effectiveness, we employed various performance metrics and conducted a human evaluation with 40 participants, demonstrating how the conversational interface compares against a graphical interface with traditional text search. The findings from our evaluation provide practical insights for advancing the design of conversational search systems.