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Knowledge-infused Neurosymbolic AI:

Knowledge Graphs for Enhanced Semantics

Guest Lecturer. Kaushik Roy

Prof. Manas Gaur

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https://www.youtube.com/watch?v=cudkPdgWfoY

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Outline

  • What is Semantics?
  • Symbolic AI
  • Neural AI
  • Knowledge-infused Neurosymbolic AI
  • Process Knowledge-infused Neurosymbolic AI
  • Examples from our Works

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What is Semantics?

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What is Semantics?

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Example Semantic Interpretations constructed either from manual effort (A, B, C), automatically (D, E), or semi-automatically (F).

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(A) is empathi ontology designed to identify concepts in disaster scenarios (Gaur et al. 2019).

(B) Chem2Bio2RDF (Chen et al. 2010).

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(C) ATOMIC (Sap et al. 2019).

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(D) Education Knowledge Graph by Embibe (Faldu et al. 2020).

(E) Event Cascade Graph in WildFire (Jiang et al. 2019).

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(F) Opioid Drug Knowledge Graph (Kamdar et al. 2019)

What is Semantics?

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The DARPA Perspective on Artificial Intelligence

Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

Downstream

Perform downstream task

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Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

Target: Return search results

Inference rule: If keyword match exists return page

Symbolic AI

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Keywords Obtained Using TF-IDF Vectorization, for example.

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Search Results

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The DARPA Perspective on Artificial Intelligence

Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

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Raw Big data + Minimal Target Demonstrations

Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

Target Prediction

with probability score

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Visual Information processing

- object classification, object segmentation

Natural language processing

- part-of-speech tagging, constituency parsing

Long List of Successes!!

Neural AI

Downstream

Perform downstream task

Search Results

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The DARPA Perspective on Artificial Intelligence

Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

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Semantic Interpreter

Map raw data to useful features

Heterogeneous

Entities (nodes) and Relationships (directed edges)

Reasoner

Use rules of inference to infer targets from features

Target: Return search results

Inference rule: If path exists between nodes through certain intermediate nodes

Search Graph

Knowledge-infused Neurosymbolic AI

Downstream

Perform downstream task

Search Results

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The DARPA Perspective on Artificial Intelligence

Semantic Interpreter

Map raw data to useful features

Reasoner

Use rules of inference to infer targets from features

—-----

—-----

—------

Semantic Interpreter

Map raw data to useful features

Heterogeneous

Entities (nodes) and Relationships (directed edges)

Reasoner

Use rules of inference to infer targets from features

Target: Return search results

Inference rule: If path exists between nodes through certain intermediate nodes

Process Knowledge-infused Neurosymbolic AI

Downstream

Perform downstream task

Search Results

Search Graph

Process Trigger

If search non-toxic

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Examples from our Works

Playlist Link

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  • Sheth, Amit, Manas Gaur, Kaushik Roy, and Keyur Faldu. "Knowledge-intensive Language Understanding for Explainable AI." IEEE Internet Computing 25, no. 5, 2021.

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  • Gaur, Manas, et al. "" Let Me Tell You About Your Mental Health!" Contextualized Classification of Reddit Posts to DSM-5 for Web-based Intervention." In Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018.

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  • Kursuncu, Ugur, Manas Gaur, and Amit Sheth. "Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning." (2020), In AAAI Fall Symposium.

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  • Gaur, Manas, Keyur Faldu, and Amit Sheth. "Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?." IEEE Internet Computing 25, no. 1, 2021.

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  • Roy, K., Khandelwal, V., Goswami, R., Dolbir, N., Malekar, J., & Sheth, A. (2023, September). Demo alleviate: Demonstrating artificial intelligence enabled virtual assistance for telehealth: The mental health case. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 13, pp. 16479-16481).

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