humemai-research

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SUMMARY

Advancement at the intersection of cognitive science, temporal knowledge graphs, database systems, and AI.

README.md

HumemAI Research

HumemAI Research explores human-like memory for AI — combining episodic (experience-based) and semantic (knowledge-based) memory models.

We study how machines can store, retrieve, and reason over structured memory graphs built from text, tables, and user interactions.

Installation

pip install humemai-research

Usage

from humemai_research.rdflib import Humemai
# or
from humemai_research.janusgraph import Humemai

Research Areas

  • Episodic Memory: Representing conversations and experiences as temporal property graphs.
  • Semantic Memory: Integrating user-provided or external data (e.g. Wikidata, Wikipedia) into graph, table, and vector formats.
  • Memory Management: Learning what to remember, summarize, or forget across time.
  • Graph-Based Reasoning: Querying and updating symbolic–neural hybrid memories.
  • Reinforcement Learning & Knowledge Graphs: Using RL to induce hierarchies and explore knowledge structures.

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