ConferenceCall 2024 03 27: Difference between revisions
Ontolog Forum
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* '''Markus J. Buehler''' ''Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning'' | |||
** Abstract: For centuries, researchers have sought out ways to connect disparate areas of knowledge. With the advent of Artificial Intelligence (AI), we can now rigorously explore relationships that cut across distinct areas – such as, mechanics and biology, or science and art – to deepen our understanding and to accelerate innovation. To do this we transformed a set of 1,000 scientific papers in the field of biological materials into a detailed ontological knowledge graph. We conduct a detailed analysis of the graph structure and calculate node degrees, communities and community connectivities, as well as clustering coefficients and betweenness centrality of key nodes, and find that the graph has an inherently scale-free nature. Using a large language embedding model we compute deep node representations and use combinatorial node similarity ranking to develop a path sampling strategy that allows us to link dissimilar concepts across the graph that have previously not been related. We apply this method to reveal insights into unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, propose never-before-seen material designs, and predict material behaviors. One comparison revealed detailed structural parallels between biological materials and Beethoven's 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. In another example, the algorithm proposed an innovative hierarchical mycelium-based composite based on a joint synthesis of path sampling with principles extracted from Kandinsky's `Composition VII' painting, where the resulting composite features balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a dynamic, context-dependent heterarchical interplay of entities beyond traditional hierarchical paradigms. Because our method transcends traditional disciplinary boundaries, and because it integrates diverse data modalities (graphs, images, text, numerical data, etc.) we achieve a far higher degree of novelty, explorative capacity, and technical detail, than conventional generative AI. The approach establishes a widely useful framework for innovation, drawing from diverse fields such as materials science, logic, art, and music, by revealing hidden connections that facilitate discovery. | |||
** Bio: Markus J. Buehler is the McAfee Professor of Engineering at MIT. Professor Buehler pursues new modeling, design and manufacturing approaches for advanced biomaterials that offer greater resilience and a wide range of controllable properties from the nano- to the macroscale. He received many distinguished awards, including the Feynman Prize, the ASME Drucker Medal, the J.R. Rice Medal, and many others. Buehler is a member of the National Academy of Engineering. | |||
== Conference Call Information == | == Conference Call Information == | ||
Revision as of 21:01, 25 March 2024
| Session | Foundations and Architectures |
|---|---|
| Duration | 1 hour |
| Date/Time | 27 Mar 2024 16:00 GMT |
| 9:00am PDT/12:00pm EDT | |
| 4:00pm GMT/5:00pm CST | |
| Convener | Ravi Sharma |
Ontology Summit 2024 Foundations and Architectures
Agenda
- Markus J. Buehler Accelerating Scientific Discovery with Generative Knowledge Extraction, Graph-Based Representation, and Multimodal Intelligent Graph Reasoning
- Abstract: For centuries, researchers have sought out ways to connect disparate areas of knowledge. With the advent of Artificial Intelligence (AI), we can now rigorously explore relationships that cut across distinct areas – such as, mechanics and biology, or science and art – to deepen our understanding and to accelerate innovation. To do this we transformed a set of 1,000 scientific papers in the field of biological materials into a detailed ontological knowledge graph. We conduct a detailed analysis of the graph structure and calculate node degrees, communities and community connectivities, as well as clustering coefficients and betweenness centrality of key nodes, and find that the graph has an inherently scale-free nature. Using a large language embedding model we compute deep node representations and use combinatorial node similarity ranking to develop a path sampling strategy that allows us to link dissimilar concepts across the graph that have previously not been related. We apply this method to reveal insights into unprecedented interdisciplinary relationships that can be used to answer queries, identify gaps in knowledge, propose never-before-seen material designs, and predict material behaviors. One comparison revealed detailed structural parallels between biological materials and Beethoven's 9th Symphony, highlighting shared patterns of complexity through isomorphic mapping. In another example, the algorithm proposed an innovative hierarchical mycelium-based composite based on a joint synthesis of path sampling with principles extracted from Kandinsky's `Composition VII' painting, where the resulting composite features balance of chaos and order, adjustable porosity, mechanical strength, and complex patterned chemical functionalization. We uncover other isomorphisms across science, technology and art, revealing a nuanced ontology of immanence that reveal a dynamic, context-dependent heterarchical interplay of entities beyond traditional hierarchical paradigms. Because our method transcends traditional disciplinary boundaries, and because it integrates diverse data modalities (graphs, images, text, numerical data, etc.) we achieve a far higher degree of novelty, explorative capacity, and technical detail, than conventional generative AI. The approach establishes a widely useful framework for innovation, drawing from diverse fields such as materials science, logic, art, and music, by revealing hidden connections that facilitate discovery.
- Bio: Markus J. Buehler is the McAfee Professor of Engineering at MIT. Professor Buehler pursues new modeling, design and manufacturing approaches for advanced biomaterials that offer greater resilience and a wide range of controllable properties from the nano- to the macroscale. He received many distinguished awards, including the Feynman Prize, the ASME Drucker Medal, the J.R. Rice Medal, and many others. Buehler is a member of the National Academy of Engineering.
Conference Call Information
- Date: Wednesday, 27 March 2024
- Start Time: 9:00am PDT / 12:00pm EDT / 5:00pm CET / 4:00pm GMT / 1600 UTC
- ref: World Clock
- Note: The US and Canada are on Daylight Saving Time while Europe has not yet changed.
- Expected Call Duration: 1 hour
- Video Conference URL: https://bit.ly/48lM0Ik
- Conference ID: 876 3045 3240
- Passcode: 464312
The unabbreviated URL is: https://us02web.zoom.us/j/87630453240?pwd=YVYvZHRpelVqSkM5QlJ4aGJrbmZzQT09
Participants
Discussion
Resources
Previous Meetings
| Session | |
|---|---|
| ConferenceCall 2024 03 20 | Foundations and Architectures |
| ConferenceCall 2024 03 13 | LLMs, Ontologies and KGs |
| ConferenceCall 2024 03 06 | LLMs, Ontologies and KGs |
| ... further results | |
Next Meetings
| Session | |
|---|---|
| ConferenceCall 2024 04 03 | Synthesis |
| ConferenceCall 2024 04 10 | Synthesis |
| ConferenceCall 2024 04 17 | Applications |
| ... further results | |