arxiv191113229v2 csai 23 mar 2020

Описание: Get Free GPT4.1 from https://codegive.com/b2f4fd7
Okay, let's dive into the paper "Knowledge Representation with Neural Network Embeddings: A Review" (arXiv:1911.13229v2 [cs.AI] 23 Mar 2020). This paper provides a comprehensive survey of techniques for integrating knowledge representation (KR) formalisms with neural network embeddings. It's a valuable resource for anyone interested in combining the strengths of symbolic AI (KR) with the learning power of connectionist models (neural networks).

*Tutorial: Knowledge Representation with Neural Network Embeddings*

*1. Introduction & Motivation*

*Bridging the Gap:* The core idea is to address the limitations of both traditional symbolic AI and neural networks in isolation.
*Symbolic AI (KR):* Offers strong explainability, logical reasoning capabilities, and structured knowledge organization. However, it often struggles with noisy data, scalability, and learning complex patterns directly from raw data.
*Neural Networks:* Excel at pattern recognition, learning from data, and handling noisy inputs. However, they are often considered "black boxes," lack explicit reasoning abilities, and require large amounts of training data.

*The Goal:* To create systems that are:
Explainable: The system can provide reasons for its decisions.
Robust: Handles noise and uncertainty gracefully.
Scalable: Works effectively with large knowledge bases and datasets.
Reasoning-capable: Can perform logical inference and deduction.
Adaptive: Learns and updates its knowledge from new data.

*Key Concepts:*
*Knowledge Representation (KR):* A way to represent facts, rules, concepts, and relationships in a machine-readable format. Examples include:
*Knowledge Graphs:* Represent entities and relationships between them (e.g., Wikidata, DBpedia).
*Logic Programming (e.g., Prolog):* Uses rules and facts to perform reasoning.
*Ontologies (e.g., OWL):* Formal repr ...

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