Combining Neural Networks with Symbolic Reasoning

The Future of AI: Combining Neural Networks with Symbolic Reasoning

The artificial intelligence landscape is experiencing a paradigm shift. While we've been focused on coding and retrieval-augmented generation (RAG), groundbreaking research from Harvard, FISAR, and Hong Kong Polytechnic University is pointing toward a revolutionary approach: **neurosymbolic AI systems** that combine the pattern recognition power of large language models with the logical reasoning capabilities of symbolic AI.

 Three Groundbreaking Research Papers

Recent studies have emerged that share a common thread - the integration of neural and symbolic systems:

1. **"Empowering Domain Specific LLMs with Graph Oriented Databases"** - Explores combining neural models with graph databases to improve explainability, reduce latency, and boost system performance.

2. **"Neurosymbolic Entity Alignment"** (Hong Kong Polytechnic University) - Demonstrates how neurosymbolic approaches outperform purely symbolic or neural methodologies in both effectiveness and robustness.

3. **"Knowledge Graph Based Agents for Complex Knowledge Intensive Questions in Medicine"** (Harvard & FISAR) - Shows how combining neural systems with symbolic systems handles complex biomedical queries by verifying information against structured biomedical data.

 The Core Concept: Why Combine Neural and Symbolic AI?

Imagine you have two knowledge graphs - one biomedical and one pharmaceutical. You want to combine the insights and data from both into a unified knowledge graph to discover cross-disciplinary relationships and uncover new patterns. This is where neurosymbolic AI excels.

**The beauty lies in combining:**

- The reasoning capabilities of symbolic AI systems

- The pattern recognition strength of neural LLMs

This integration creates systems that are not only powerful but also explainable and trustworthy.

 A Three-Step Implementation Process

 Step 1: Data Processing with Neural Models

Large language models (Transformers) process unstructured text to identify:

- Entities (objects, nodes)

- Relationships (edges)

- Domain-specific components (diseases, proteins, biochemical interactions)

 Step 2: Knowledge Graph Construction

Extracted entities and their relationships are structured into a knowledge graph where:

- Nodes represent entities (proteins, diseases)

- Edges represent relationships (inhibits, associates with)

- Symbolic rules ensure logical consistency across subgraphs

 Step 3: Enhanced Insights through Neurosymbolic Integration

Using the knowledge graph and symbolic rules, the system can:

- Infer new potential drug-disease relationships

- Discover connections not explicitly mentioned in literature

- Maintain logical consistency while generating novel insights

 Learning New Logical Rules: A Practical Example

Consider this scenario: Alice (software engineer) works at a tech corporation and collaborates with Bob (data scientist) on machine learning projects.

**The system can learn new rules through five steps:**

1. **Data Analysis**: Neural components identify collaboration patterns between complementary roles

2. **Rule Induction**: If software engineers collaborate with data scientists on ML projects, they may collaborate with other related roles

3. **Relationship Inference**: The system predicts Bob might collaborate with Carol (data analyst)

4. **Knowledge Graph Update**: New relationships are added based on learned pattern.

5. **Explanation Generation**: Symbolic reasoning provides clear justification for new connections

Real-World Benefits

This neurosymbolic approach delivers:

- **Dynamic Knowledge Bases**: Systems evolve by learning from new data without constant manual updates

- **Improved Decision-Making**: Enhanced rule sets lead to more accurate and insightful inferences

- **Scalability**: Automated rule learning facilitates management of large-scale knowledge graphs

- **Interpretability**: Clear explanations for all inferences, addressing the "black box" problem of many AI systems

Hands-On Implementation

Here's what the process looks like in practice:

**Input Data**: "John is a teacher at a high school. He teaches mathematics. Mary is a student in his class."

**Step 1 - Information Extraction**:

- Entities: John (teacher), Mary (student), high school, mathematics

- Relationships: John teaches Mary, John works at high school

**Step 2 - Neural System Analysis**:

- Pattern recognition identifies potential additional relationships

- Confidence scores assigned to predictions

**Step 3 - Symbolic Rule Application**:

- Employment rules enforced

- Class relationships maintained

- Additional inferences derived (John likely has mathematics degree)

**Step 4 - Integration**:

- Combined insights create enriched knowledge graph

- System generates explanations for all inferences

 Technology Stack

**For Neural Processing**:

- SpaCy, Stanford NLP, Hugging Face Transformers

- Named entity recognition, dependency parsing

**For Symbolic Systems**:

- Drools for semantic rules

- Prolog for logic programming

- Custom rule engines

**For Integration**:

- DeepLogic, NeuroSymbolic frameworks

- Apache Spark for data pipelines

- Graph databases for visualization

Why This Matters Now

Major institutions like Harvard and FISAR are investing heavily in this technology because it addresses critical limitations of current AI systems:

- **Enhanced Comprehension**: Neural systems extract patterns while symbolic systems ensure logical consistency

- **Flexibility & Scalability**: Handle large data volumes while maintaining interpretability

- **Improved Accuracy**: Robust inferences that are both accurate and explainable

The Path Forward

We're moving beyond pure coding and RAG approaches toward **intelligent data fusion** that combines:

- Pattern recognition capabilities of modern LLMs

- Logical reasoning power of symbolic AI systems

This represents a fundamental shift in how we approach AI development - using the right tool for the right job rather than forcing everything through large language models.

 Practical Applications

**Knowledge Management**: Effectively organize and understand vast amounts of textual data

**Intelligence Systems**: Develop AI applications that reason and make informed decisions based on structured knowledge

**Domain-Specific Applications**: Particularly powerful in healthcare, biomedicine, and other knowledge-intensive fields

Conclusion

The future of AI lies not in choosing between neural networks and symbolic reasoning, but in thoughtfully combining both approaches. This neurosymbolic integration leverages the pattern recognition and adaptability of machine learning models with the logical reasoning and interpretability of rule-based systems.

As we've seen from recent research, this approach enhances data comprehension, ensures consistency, and enables sophisticated inferences that are both accurate and explainable. The technology is already accessible through tools like ChatGPT with Canvas, making it possible for developers to start experimenting with these concepts today.

The paradigm shift is clear: we're moving toward AI systems that don't just recognize patterns or follow rules, but intelligently combine both capabilities to create truly understanding machines.

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*Want to explore this further? The three research papers mentioned provide deep technical insights into implementation strategies and real-world applications of neurosymbolic AI systems.*

Bloggers Note: The 3 research papers mention weren't listed. Sorry..