Tag: knowledge extraction

Integrating Large Language Models and Knowledge Graphs for Extraction and Validation of Textual Data

by Marco Brambilla, posted in big data, data science, event, experience, Machine Learning / AI

Large manufacturing companies in mission-critical sectors like aerospace, healthcare, and defense, typically design, develop, integrate, verify, and validate products characterized by high complexity and low volume. They carefully document all phases for each product but analyses across products are challenging due to the heterogeneity and unstructured nature of the data in documents. In our research, … Continue reading Integrating Large Language Models and Knowledge Graphs for Extraction and Validation of Textual Data

Iterative knowledge extraction from social networks

by Marco Brambilla, posted in data science, social network, social network analysis

Our motivation starts from the fact that knowledge in the world continuously evolves, and thus ontologies and knowledge bases are largely incomplete. We explored iterative methods, using the results as new seeds. In this paper we address the following research questions:

How does the reconstructed domain knowledge evolve if the candidates of one extraction are recursively used as seeds?
How does the reconstructed domain knowledge spread geographically?
Can the method be used to inspect the past, present, and future of knowledge?
Can the method be used to find emerging knowledge?

Extracting Emerging Knowledge from Social Media

by Marco Brambilla, posted in big data, data science, event, experience, social network, social network analysis

Knowledge in the world continuously evolves, and ontologies are largely incomplete.
We propose a method and a tool for discovering emerging entities by extracting them from social media.
Once instrumented by experts through very simple initialization, the method is capable of finding emerging entities; we propose a mixed syntactic + semantic method.