Summary of Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models, by Frank Wawrzik et al.
Customized Information and Domain-centric Knowledge Graph Construction with Large Language Models
by Frank Wawrzik, Matthias Plaue, Savan Vekariya, Christoph Grimm
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework utilizes knowledge graphs to provide timely access to structured information, enabling actionable technology intelligence and improving cyber-physical systems planning. The framework includes text mining processes such as information retrieval, keyphrase extraction, semantic network creation, and topic map visualization. A selective knowledge graph construction approach is employed, backed by an electronics and innovation ontology, for multi-objective decision-making focused on cyber-physical systems. The methodology is demonstrated in the automotive electrical systems domain, showcasing scalability. Results indicate that the constructed process outperforms GraphGPT, bi-LSTM REBEL, and transformer REBEL with a pre-defined dataset in terms of class recognition, relationship construction, and correct “subclass of” categorization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to quickly access important information is proposed. This method uses special networks called knowledge graphs to help people make good decisions about complex systems that combine physical and digital parts. The approach involves several steps: finding the most important words in a text, creating a network of related ideas, and using this network to visualize and understand the information better. This method was tested on automotive electrical systems and showed it can be used effectively and efficiently. It even outperformed other methods in certain tasks. |
Keywords
» Artificial intelligence » Knowledge graph » Lstm » Transformer
