# Context-Aware Scientific Knowledge Extraction on Linked Open Data using Large Language Models

arXiv: https://arxiv.org/abs/2506.17580 · HTML: https://mlsanity.com/paper/2506.17580/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 21 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Is ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents](https://mlsanity.com/paper/2304.09542/) (2023)
- [HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction](https://mlsanity.com/paper/2408.04948/) (2024)
- [Putting GPT-4o to the Sword: A Comprehensive Evaluation of Language, Vision, Speech, and Multimodal Proficiency](https://mlsanity.com/paper/2407.09519/) (2024)
- [WorkflowLLM: Enhancing Workflow Orchestration Capability of Large Language Models](https://mlsanity.com/paper/2411.05451/) (2024)
- [Large Language Models are Built-in Autoregressive Search Engines](https://mlsanity.com/paper/2305.09612/) (2023)
- [Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning](https://mlsanity.com/paper/2403.11996/) (2024)
- [Towards a Search Engine for Machines: Unified Ranking for Multiple Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.00175/) (2024)
- [Multi-Source Knowledge Pruning for Retrieval-Augmented Generation: A Benchmark and Empirical Study](https://mlsanity.com/paper/2409.13694/) (2024)
- [Identifying Protein-Protein Interaction using Tree LSTM and Structured Attention](https://mlsanity.com/paper/1808.03227/) (2018)
- [Embedding and Extraction of Knowledge in Tree Ensemble Classifiers](https://mlsanity.com/paper/2010.08281/) (2020)
- [Large-Scale Knowledge Synthesis and Complex Information Retrieval from Biomedical Documents](https://mlsanity.com/paper/2302.06854/) (2023)
- [A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences](https://mlsanity.com/paper/2412.03531/) (2024)
- [Modelling Stopping Criteria for Search Results using Poisson Processes](https://mlsanity.com/paper/1909.06239/) (2019)
- [ByteScience: Bridging Unstructured Scientific Literature and Structured Data with Auto Fine-tuned Large Language Model in Token Granularity](https://mlsanity.com/paper/2411.12000/) (2024)

## Abstract (arXiv)

The exponential growth of scientific literature challenges researchers extracting and synthesizing knowledge. Traditional search engines return many sources without direct, detailed answers, while general-purpose LLMs may offer concise responses that lack depth or omit current information. LLMs with search capabilities are also limited by context window, yielding short, incomplete answers. This paper introduces WISE (Workflow for Intelligent Scientific Knowledge Extraction), a system addressing these limits by using a structured workflow to extract, refine, and rank query-specific knowledge. WISE uses an LLM-powered, tree-based architecture to refine data, focusing on query-aligned, context-aware, and non-redundant information. Dynamic scoring and ranking prioritize unique contributions from each source, and adaptive stopping criteria minimize processing overhead. WISE delivers detailed, organized answers by systematically exploring and synthesizing knowledge from diverse sources. Experiments on HBB gene-associated diseases demonstrate WISE reduces processed text by over 80% while achieving significantly higher recall over baselines like search engines and other LLM-based approaches. ROUGE and BLEU metrics reveal WISE's output is more unique than other systems, and a novel level-based metric shows it provides more in-depth information. We also explore how the WISE workflow can be adapted for diverse domains like drug discovery, material science, and social science, enabling efficient knowledge extraction and synthesis from unstructured scientific papers and web sources.