Agentic Agents: The Synergy of Graph-Aware Transformers and Ontological Reasoning

In the rapidly evolving world of artificial intelligence, new terms and concepts seem to emerge almost daily, requiring us to learn at an unprecedented pace just to keep up. One such term gaining significant attention is agentic agents. As we navigate this fast-moving landscape, it's easy to feel overwhelmed by the sheer volume of information and terminology. This article is the result of my own efforts to grasp what agentic agents truly are and to connect this new concept with what I already know. My hope is that by sharing these insights, I can help you enter this topic more swiftly and with a clearer understanding.

An agentic agent refers to an AI system that exhibits autonomous decision-making, goal-directed behavior, and reasoning capabilities. These systems can perceive their environment, learn from interactions, and act based on structured reasoning mechanisms.

Graph-Aware Transformers and ontological reasoning are two pillars for developing advanced agentic systems. Here’s how they contribute:

Understanding the Core Concepts

1. Graph-Aware Transformers
2. Ontological Reasoning

Why is this Important for Agentic Agents?

• More Robust Reasoning: Merging learned patterns (via GIN) with structured ontological knowledge enables better decision-making.
• Explainability: AI can justify its reasoning based on both learned correlations and formal knowledge structures.
• Continuous Learning with Stability: New insights can be integrated without breaking logical consistency.

Where to Start?

1. Graph-Based Learning
2. Transformers & Graph Integration
3. Ontologies & Reasoning
4. Applications & Frameworks

1. Are Graph-Aware Transformers and Ontological Reasoning Solutions Available Today?

As of now, Graph-Aware Transformers and ontological reasoning are still primarily areas of active research, but there are commercially available solutions and frameworks that leverage some of these techniques, though often not in their full, cutting-edge research form. Here's a breakdown:

Graph-Aware Transformers

• Research Focus: Graph-Aware Transformers, especially those combining Graph Isomorphism Networks (GIN) with transformers, are still largely experimental. The idea is that transformers, traditionally used for sequence data (like text), are enhanced to work with graph-structured data by incorporating specialized graph networks that can process relationships more effectively.
• Available Solutions:

Ontological Reasoning

• Research Focus: Ontologies and reasoning are a bit more established, with ontologies being widely used for knowledge representation, semantic web, and linked data. Ontological reasoning involves formal systems that can reason about concepts like "is-a," property inheritance, and transitive relationships (e.g., if "A is a type of B," and "B is a type of C," then "A is a type of C").
• Available Solutions:

Summary:

• On the shelves: There are tools like PyTorch Geometric, DGL, Protégé, Jena, and Wolfram’s tools for building and reasoning with ontologies. Graph-Aware Transformers are still in research stages, but graph-based transformers are starting to find applications.
• Still research: Full integration of GIN-enhanced transformers for general agentic systems is not yet widespread, but progress is being made.

2. What is the Current Accuracy of Knowledge Models and Learning Systems?

The accuracy of knowledge models (ontologies) and learning systems (like Graph-Aware Transformers) can vary widely depending on the specific task, domain, and how well the system is trained. Here's a general view:

Knowledge Models (Ontologies)

• Accuracy: The accuracy of ontological reasoning largely depends on the completeness and correctness of the ontology. Since ontologies are manually created and curated, their accuracy is usually very high when the ontology is well-constructed and verified.Example: Human Phenotype Ontology (HPO) is highly accurate for medical and genetic research, but any errors in the ontology (e.g., incorrect classifications) will directly affect reasoning outcomes.
• Limitations:Ontologies are static by nature and require manual updates to stay current with new knowledge.They rely on domain-specific knowledge, so their accuracy is only as good as the domain experts who build them.They don't inherently "learn" from data or experience (i.e., they don't generalize based on new patterns).

Learning Systems (Graph-Aware Transformers)

• Accuracy: The accuracy of systems using Graph-Aware Transformers can be hard to quantify, as these systems are still evolving. The key here is that these models are trying to improve the way transformers handle structured data like graphs.In natural language tasks, standard transformers (like BERT) have achieved SOTA (state-of-the-art) accuracy in tasks like question-answering and text classification.For graph-based tasks, accuracy depends on the graph structure, features, and task type (e.g., node classification, link prediction). For instance, GraphSAGE and GAT (Graph Attention Networks) achieve good accuracy in tasks like link prediction and node classification in graph-based datasets.
• Limitations:Generalization: Graph-based transformers may struggle to generalize to new or unseen types of graph structures (i.e., out-of-distribution graphs).Training Data: The performance heavily depends on the quality and quantity of training data, which can be sparse for niche domains like biological or industrial graphs.Explainability: Even with the enhanced attention mechanisms in GIN, these systems can still be opaque, making it hard to explain why certain relationships or predictions were made.

Combined Systems (Ontological + Learning)

• Combining ontological reasoning with learning systems can lead to more robust and explainable models, but the accuracy of such systems depends on balancing the flexibility of learned patterns (via machine learning) with the rigidity and consistency of ontological knowledge.If new knowledge contradicts existing ontology, you need a system that can integrate these new insights without breaking coherence. This is where the integration of ontologies and learning systems is still being fine-tuned.

Summary:

• Ontologies: High accuracy when well-defined but limited by manual curation and lack of adaptability.
• Learning Systems (Graph-Aware Transformers): Achieving good accuracy on graph-based tasks, but still evolving in terms of generalization and explainability.
• Combined Systems: Have great potential but require careful balancing to ensure that both knowledge and learning contribute effectively.

Conclusion

• Where to Start: You can experiment with existing tools like PyTorch Geometric (for graph-based learning) and Protégé (for ontologies), both of which are already widely used in industry.
• Accuracy: Ontological reasoning is highly accurate for well-curated data, while learning systems still face challenges in generalization and explainability, especially when integrating graphs with transformers.

About performance and scalability of traditional reasoners

Traditional reasoners (especially ontological reasoning systems) often struggle with performance and scalability when handling large datasets. As ontologies grow in size or complexity, performance bottlenecks can quickly emerge, especially when reasoning needs to process vast amounts of interconnected data in real time. This is why it's critical to explore tools and solutions that are optimized for both large-scale reasoning and flexible integration with other technologies.

RDFox: A Scalable Reasoning Solution

RDFox is one of the more promising solutions for improving the scalability and performance of reasoning with large datasets. Here’s why it might be a good fit for your needs:

What is RDFox?

• RDFox is a high-performance RDF store that supports ontological reasoning on large-scale data. It is designed to handle reasoning tasks over RDF (Resource Description Framework) datasets, which are commonly used in knowledge graphs.
• RDFox implements a combination of reasoning techniques, including materialization-based reasoning and incremental reasoning, which makes it particularly suited for large-scale reasoning tasks.

Key Features of RDFox:

1. High Performance: RDFox is engineered to be one of the fastest reasoning systems, handling large-scale graphs and complex queries efficiently. It supports deductive reasoning using ontologies and can answer queries over large knowledge graphs in real-time.
2. Scalability: It is optimized for reasoning over massive RDF datasets, making it capable of handling the vast amounts of interconnected data often encountered in fields like biomedicine, finance, and enterprise systems.
3. Parallelization: RDFox can take advantage of multi-core processing and supports distributed reasoning, allowing it to scale across large clusters of machines when needed.
4. Incremental Reasoning: As data evolves, RDFox can incrementally update the reasoning without needing to recompute everything from scratch. This is important for systems that require continuous learning or are streaming data.
5. Optimized for OWL: RDFox supports reasoning based on OWL ontologies, which is the standard for defining formal knowledge models on the web.

Why RDFox Could Be a Good Fit:

• Performance and Scalability: If you are working with large knowledge graphs and need real-time reasoning, RDFox's optimizations for scalability and speed could be a significant advantage.
• Integration with Machine Learning: Although RDFox itself is focused on reasoning over static knowledge graphs, you can combine it with learning-based systems (such as Graph Neural Networks (GNNs)) for a hybrid approach. This could enable you to leverage ontological reasoning alongside machine learning models for more flexible, context-dependent decision-making.

Limitations:

• Data Size: While RDFox is scalable, performance can still degrade with very huge datasets that exceed system memory or when the reasoning process becomes too complex. It's also important to note that the reasoning is still based on predefined ontologies, so it may not handle highly dynamic or noisy data as well as machine learning systems.
• Complex Reasoning Tasks: For highly complex, multi-hop reasoning or tasks requiring semantic reasoning over ambiguous data, RDFox may still have limitations, though its incremental reasoning and optimization strategies help mitigate these issues.

Alternatives and Hybrid Approaches

If RDFox doesn't meet all your needs or if you're interested in exploring more alternatives, here are a few other solutions and strategies that might be suitable:

1. GraphDB:

• GraphDB is another popular RDF store that supports SPARQL querying and reasoning. It is designed for performance and scalability, making it suitable for large datasets. It’s often used for enterprise-level applications in sectors like healthcare and finance.

2. Apache Jena:

• Jena is an open-source framework for building semantic web and linked data applications. It provides reasoning engines like Pellet and HermiT, which can be used for RDF and OWL reasoning.
• While it is more flexible and widely used, its performance can be slower compared to RDFox when dealing with extremely large datasets.

3. Hybrid Models with Machine Learning:

• Combine ontological reasoning (like RDFox) with Graph Neural Networks (GNNs), which are well-suited for learning from graph structures. This can improve performance in areas like link prediction, entity classification, and graph generation.
• GraphSAGE, GAT (Graph Attention Networks), and Graph Neural Networks (GNNs) can learn patterns that complement ontological reasoning, enhancing both flexibility and performance when dealing with data that may not strictly follow rigid ontological rules.

Conclusion

• RDFox is a strong choice for improving the performance of reasoning over large datasets, particularly for use cases where ontological reasoning and RDF graphs are central. Its high scalability, parallelization, and incremental reasoning make it a good fit for environments with dynamic and large-scale knowledge graphs.
• While traditional reasoners like RDFox are already commercially available, they still face challenges when dealing with extremely large or complex data. A hybrid approach, combining ontological reasoning with machine learning models like Graph Neural Networks, can offer even more flexibility and performance, especially in dynamic environments.

Links with NLP?

Integrating ontological reasoning systems like RDFox with Natural Language Processing (NLP) solutions can provide powerful capabilities for semantic understanding, knowledge extraction, and decision support systems. Here are a few ways NLP and ontological reasoning can work together, as well as some existing solutions and frameworks that facilitate this integration:

1. NLP and Ontological Reasoning Integration

• Semantic Text Representation: NLP techniques can convert unstructured text (such as medical reports, research papers, or customer reviews) into structured data that can be mapped to an ontology (e.g., OWL). This allows ontological reasoning systems to reason over the relationships between concepts identified in the text and the established knowledge base.
• Named Entity Recognition (NER): NLP systems can be used for NER to identify entities in text (e.g., disease names, medications, or geographic locations). These entities can then be matched against an ontology (e.g., Human Phenotype Ontology (HPO)) to provide a richer, context-aware understanding.
• Relationship Extraction: NLP can help extract relationships between entities in text, which can then be mapped into an ontological structure. For example, extracting that "John has diabetes" and using an ontology to infer that diabetes is a disease.
• Text to Knowledge Graph: NLP can help convert text data into a graph structure, allowing reasoning and graph-based analysis using a graph-aware transformer or other reasoning engines like RDFox. For example, by analyzing text, NLP systems can build knowledge graphs that represent entities and relationships, which can be processed further using reasoning engines.

2. NLP Solutions with RDFox and Similar Tools

Here are some NLP tools that can be combined with RDFox or other ontological reasoning systems for enhanced semantic analysis and automated reasoning:

A. Textual Knowledge Graph Generation

• Spacy: A popular NLP library for Python, SpaCy can be used for entity recognition and dependency parsing. You can extract entities, concepts, and relationships from text and map them into an RDF graph, which can then be queried and reasoned over using RDFox.
• Stanford NLP: This toolkit includes dependency parsing and entity recognition, which can help generate semantic graphs from unstructured text. The relationships identified can be converted into RDF triples for further processing by a reasoner like RDFox.
• OpenIE: A solution for extracting relationships from text in the form of triples (subject-predicate-object). These triples can then be added to an RDF store for reasoning.

B. Question Answering (QA) with Ontological Reasoning

Combining QA systems with ontological reasoning can improve performance by ensuring answers are derived from well-structured, verified knowledge.

• Haystack by deepset: An open-source NLP framework designed for question-answering (QA), which can be enhanced with ontological reasoning. You can integrate knowledge graphs or ontologies into the search process to improve the relevance and accuracy of answers.
• Rasa: An NLP-based chatbot framework that uses intent recognition and entity extraction. By incorporating ontologies, you can build more accurate models for automated responses. For instance, when a user asks a question related to a specific domain, the system can reason over the ontology to provide a more precise answer.

C. Hybrid Approaches for Reasoning and NLP

• Graph-Aware Transformers: As mentioned earlier, integrating Graph-Aware Transformers (GIN) with NLP allows for a more sophisticated context-dependent reasoning based on the relationships identified by NLP. This combination can help improve NLP tasks like text classification and entity linking by learning patterns from graphs while maintaining semantic integrity using ontologies.
• Ontology-Aware Neural Networks: There are emerging approaches that combine neural networks and ontologies for tasks like semantic search or document classification. These models combine the flexibility of neural learning with the rigorous reasoning of ontologies. For example, an ontology can provide prior knowledge to guide the learning process, leading to more robust and explainable predictions.

3. Example Use Cases

• Healthcare: By combining NLP techniques (such as medical text extraction from clinical records) with ontological reasoning (e.g., using SNOMED CT or Human Phenotype Ontology (HPO)), you can create advanced clinical decision support systems that can infer relationships between diseases, treatments, and patient histories.
• Legal: Using NLP to extract legal entities, actions, and relationships from contracts and case law documents, then reasoning over these relationships using ontologies like Legal Knowledge Graphs (e.g., LexML or LegalTextML).
• Customer Support: Enhancing chatbots with knowledge from structured ontologies like Schema.org or custom-built knowledge graphs to provide more accurate and contextually relevant responses in customer support scenarios.

Conclusion

NLP and ontological reasoning can be combined to improve semantic understanding of text, enhance knowledge extraction, and enable intelligent decision-making based on structured knowledge. There are several existing solutions that enable the integration of NLP and ontologies, such as SpaCy, OpenIE, Haystack, and Rasa, which can be used together with reasoning engines like RDFox for real-time, scalable reasoning. These integrations are increasingly important in domains like healthcare, legal analysis, and enterprise-level knowledge systems.

Some mathematics for Graph Neural Networks and literal explanation

This equation appears when presenting graph neural network (GNN) or message-passing framework. Here's a breakdown of the components and their meanings:

Components:

Context:

This kind of update rule is commonly used in Graph Neural Networks (GNNs), particularly in Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and other message-passing models. These models are designed to capture the graph structure by updating node representations based on both local features and their relationships with neighbors.

• GNNs are effective in domains where graph-structured data is involved, such as social networks, knowledge graphs, biological networks, etc.

The goal of this equation is to iteratively update node representations by passing messages through the graph, eventually capturing higher-level graph features that can be used for tasks like node classification, graph classification, link prediction, and more.

Let's explain it in order it can be understood by a child

Final result:

At the end of this process, you (node u) have a new name tag that tells you a little more about who you are and how you're connected to the people around you. This new name tag is not just about you anymore but includes all the cool stuff you've learned from your friends (your neighbors).

To sum it up:

This process is about how you learn from your friends and update your own information based on what you know about them and what they know about you. The more you talk to your neighbors (friends), the smarter you become about who you are in the whole neighborhood!

The particular case of Agentic Agents: usage of Graph Isomorphism Networks (GIN).

Here the equation is not exactly the same.

The equation is related to Graph Isomorphism Networks (GIN), and it's a form of updating node features using a graph neural network (GNN) approach. Let me break this down and explain it in simpler terms.

Here’s what’s happening in the context of Graph Isomorphism Networks (GIN):

Breaking Down the Equation:

What Makes This Special in GIN?

Use Cases in Practice

This approach is widely used in tasks like:

• Node classification: Predicting labels for nodes in a graph (e.g., identifying types of people in a social network).
• Graph classification: Predicting properties of the entire graph based on node information.
• Link prediction: Predicting missing edges in a graph (e.g., suggesting friends in social networks).
• Recommendation systems: Recommending items to users by modeling the relationships between users and items as a graph.

Summary:

The equation you're looking at is a Graph Isomorphism Network (GIN) update rule that combines a node's own features and the features of its neighbors to create a new, updated feature for that node. The term (1+?)(1 + \epsilon)(1+?) gives it some flexibility to balance the importance of the node’s own features versus its neighbors. This method allows GINs to effectively handle graph-structured data and is a powerful tool for many machine learning tasks involving graphs.

Why the equation appropriate for Agentic agents

The equation is particularly appropriate for agentic systems (systems that have the ability to act autonomously and make decisions based on reasoning or learned experiences) because of several key aspects that align with the characteristics of agentic agents.

In Summary: Why is it appropriate for agentic agents?

• Autonomy: The agent’s own state influences its decision, giving it autonomy while still considering the environment (neighbors).
• Adaptability: The agent can adjust how much it relies on its own experience versus others’ through the parameter ?\epsilon?.
• Collaboration and Social Learning: The agent incorporates information from its neighbors, allowing for interaction and learning in a multi-agent environment.
• Continuous Learning: The agent can evolve and adapt over time as it continuously updates its knowledge.
• Scalability: It scales well in multi-agent systems where many agents are learning and interacting together.

This makes the equation highly suitable for agentic systems, where agents need to act independently but also consider the context provided by others in a flexible, learning-based way.

What about needed processing power?

1. Processing Power Requirements:

Yes, the tools related to Graph Isomorphism Networks (GINs), Graph-Aware Transformers, and ontological reasoning can be quite computationally intensive, especially when dealing with large datasets or complex environments. Here's a breakdown of why this might be:

• Graph-Based Learning (GINs and Transformers): These models require processing not only individual node features but also the interactions and dependencies between nodes in the graph (i.e., the relationships or edges). For large graphs, this can be computationally expensive, as the system needs to aggregate information from many neighbors in each update step.
• Ontological Reasoning: Reasoning over ontologies, especially if you're working with large and complex ontologies, can be expensive because it often involves graph traversal, checking relationships like "is-a," "part-of," or more complex inferences. Ontological reasoners also may need to perform complex reasoning tasks (like transitive closure, consistency checking, or satisfiability checking) that can be slow depending on the size of the ontology and the complexity of the reasoning rules applied.
• Integration: When combining both graph-based models (like GINs) and ontological reasoning (like using RDFox or OWL-based reasoning), there’s added complexity. The need to reconcile different forms of knowledge (statistical learned patterns vs. structured formal knowledge) and handle reasoning both in a learned and structured domain makes this a dual-layered computational task.

Optimizations to Consider:

• Preprocessing: You can optimize by preprocessing the graph data (e.g., feature engineering, pruning irrelevant nodes/edges) and using sparse data structures.
• Graph Sampling: Techniques like graph sampling or neighborhood aggregation can help reduce the graph’s size for more efficient processing in large-scale systems.
• Distributed Computing: For large-scale systems, distributing computation (e.g., using distributed graph databases or GPUs) can be a solution to handle the processing load effectively.

2. Ontology for Evolving Reasoning Models:

Yes, ontologies are indeed powerful in the context of evolving reasoning models. Ontologies typically provide a formalized structure of knowledge, which allows reasoning systems to adapt progressively to new information as it becomes available. Here’s how ontologies support evolving reasoning models:

Key Advantages of Using Ontologies for Evolving Reasoning:

• Structured Knowledge Representation: Ontologies provide a framework for formally defining concepts, relationships, and rules, which gives the reasoning system a foundation for interpreting new data. This structure ensures that the reasoning process can scale and evolve as new knowledge is integrated, without needing to discard prior understanding.
• Hierarchical and Relational Reasoning: Ontologies often define hierarchical relationships (e.g., "is-a," "part-of") between entities. This allows for reasoning to be progressive because new instances can be classified into existing categories or adapted to evolving categories. For example, a new disease symptom might be added to an existing classification in a medical ontology.
• Dynamic Adaptation: Ontological reasoning models can evolve over time by incorporating new facts (e.g., through reasoning over new observations or incoming data). If new information is added that maps with an existing class in the ontology (or leads to the creation of new classes or relationships), the reasoning model adapts without needing a complete redesign.
• Statistical Mapping with Available Data: Ontologies allow for the integration of new data into the existing model. For example, statistical data (such as new research findings or real-world observations) can be mapped into an ontology to update the reasoning system’s understanding. This could be achieved by connecting new observations with existing concepts in the ontology, thus allowing the model to progressively adapt its reasoning over time.
• Incorporating Probabilistic Reasoning: With some ontologies (like those using OWL or RDF), you can integrate probabilistic reasoning. For instance, a system can be set up to reason about the likelihood of various hypotheses or concepts, adapting over time as more data accumulates.

Example Scenario:

Let’s take the healthcare domain as an example. Suppose you’re working on an agentic system that helps diagnose diseases based on symptoms:

• Initially, the system may have a limited ontology of diseases and symptoms, but as new cases emerge, the system gathers more data and integrates new symptoms into the ontology.
• As the system encounters new combinations of symptoms, it could use both the hierarchical knowledge (e.g., “fever” is a symptom of multiple diseases) and statistical data (e.g., from medical journals or patient records) to draw new conclusions about potential diagnoses.
• Over time, as new relationships (or diseases) emerge, the ontology can be updated to reflect these changes, and the system's reasoning model will adapt to the new data.

Benefits for Agentic Systems:

• Adaptive Decision-Making: The system’s decisions (e.g., diagnosis or prediction) evolve as the ontology evolves, making it more accurate and aligned with real-world conditions.
• Consistency and Reliability: Ontologies provide a consistent framework for knowledge that ensures new reasoning doesn’t contradict prior knowledge, helping to maintain reliable decision-making.
• Context-Aware: As the system encounters new data that fits within an existing ontology, the system can contextually refine its reasoning without overhauling its entire understanding.

Conclusion:

• Processing Load: Tools that combine graph-based learning and ontological reasoning can require substantial computational resources, especially when dealing with large, dynamic environments. Techniques like data preprocessing, graph sampling, and distributed computing can help mitigate some of these costs.
• Evolving Reasoning: Ontologies provide an excellent foundation for evolving reasoning models because they allow for a formalized, flexible structure that can adapt over time as new data becomes available. The system can progressively adjust its reasoning by integrating new facts while maintaining logical consistency, making it suitable for dynamic environments where the agent must adapt its decision-making.

Data quality issue

Knowledge acquisition results from a scientific approach while a model is just a representation. Capturing it from set of unchecked data can lead to not accurate ontology models.

Ontologies, while powerful and structured, are indeed based on a scientific approach to knowledge representation. They typically aim to formalize human knowledge in a way that machines can reason over it. However, when they are derived from unchecked or noisy data, the accuracy of the ontology models can be compromised. Here's why:

1. Quality of Data:

• Unchecked or Noisy Data: If the data used to build or update an ontology isn't validated or curated, the ontology may incorporate incorrect, inconsistent, or incomplete information. For example, if the ontology is built from crowd-sourced data or data that hasn't been cross-checked for accuracy, it might contain false relationships, incorrect classifications, or even conflicting concepts.
• Consequences: An inaccurate ontology can lead to faulty reasoning or incorrect conclusions. For example, in a medical ontology, inaccurate relationships between symptoms and diseases could result in incorrect diagnoses or missed conditions.

2. Challenges of Deriving Ontologies from Raw Data:

• Data Quality and Provenance: Raw data often lacks proper context and provenance (information about where it came from, who created it, or how it was collected). This means the reliability of that data can be questionable. If an ontology is derived directly from such data without rigorous checks, it could reflect biases or errors in the underlying sources.
• Data Inconsistencies: Data may contain conflicting information. For example, one dataset might say "a symptom is linked to a disease" while another dataset might suggest otherwise. Without careful reasoning and validation, an ontology built on these conflicting sources could be inaccurate or incomplete.
• Lack of Granularity: Raw data may lack the level of detail or specificity required for accurate ontological representation. An ontology needs to define clear relationships and distinctions between concepts, but raw data might be ambiguous or overly simplified.

3. How Can This Be Addressed?

Here are some strategies to mitigate the risks associated with deriving ontologies from unchecked data:

A. Data Validation and Cleaning:

• Curated Data Sources: One approach is to rely on well-established, curated datasets or scientific knowledge as a foundation for ontologies. For example, in the medical domain, ontologies like SNOMED CT and HPO (Human Phenotype Ontology) are built on expert-reviewed and scientifically validated data, making them much more reliable.
• Data Preprocessing: Before using data to build or update an ontology, it can be cleaned and validated to ensure consistency and accuracy. This includes removing duplicates, resolving conflicts, and correcting errors.

B. Combining Data with Expert Knowledge:

• Hybrid Approaches: Instead of building ontologies purely from raw data, they can be built using a combination of data mining techniques and expert input. Domain experts can help validate and refine the ontology as it evolves, ensuring that it reflects scientific consensus and domain-specific knowledge.
• Crowdsourcing with Oversight: In cases where crowdsourced data is involved, it can be valuable to use a supervised crowdsourcing approach where the contributions of the crowd are validated and reviewed by experts. This helps reduce the impact of errors and biases.

C. Reasoning and Consistency Checking:

• Reasoning Engines: After creating an ontology, you can use reasoners to check for logical consistency. If the ontology has conflicting relationships or rules, a reasoner can flag them, ensuring that the ontology doesn't produce contradictions or errors in the reasoning process.
• Consistency Validation: Reasoning tools like RDFox or Pellet allow you to check for satisfiability and consistency of ontological models, ensuring that the ontology's logical structure holds up as more data is added.

D. Continuous Updates and Monitoring:

• Iterative Updates: Ontologies can evolve and be updated over time. Regularly incorporating new, validated data while keeping track of changes allows the ontology to stay relevant and accurate.
• Feedback Loops: If the ontology is being used in a real-world application, feedback from its use (e.g., user feedback, results of reasoning) can help identify errors or gaps in the model. This feedback can be used to refine the ontology and improve its accuracy.

E. Hybrid Ontologies (Statistical + Logical):

• Combining Statistical Learning with Ontology: While ontologies are structured and logical, they can also integrate statistical learning models to adapt to evolving data. For instance, a system might use machine learning to identify new relationships from large datasets, and these learned patterns can then be formalized within the ontology. This hybrid approach allows the ontology to be both rigorous and adaptive.

Conclusion:

• Ontologies derived from unchecked data can lead to significant issues such as inaccurate classifications, false relationships, and logical inconsistencies. These errors can negatively impact reasoning and decision-making in agentic systems.
• However, careful curation, expert validation, and reasoning can significantly improve the quality and reliability of ontologies. Incorporating statistical learning models can also help ontologies adapt to new data while maintaining their logical structure, ensuring they stay relevant and accurate as the system evolves.

To summarize, while ontologies offer a structured framework for evolving reasoning, their accuracy and effectiveness heavily depend on the quality of the underlying data and validation processes that ensure they remain scientifically sound.

Statistical aspect

Let's note statistical approaches can only invalidate some formal model, but not really make a valid formal model, as it is the case for all what is related to correlations. So what can be the consequences with Agentic Agents?

statistical approaches can help invalidate or refine formal models, but they cannot inherently create a valid formal model on their own. This is a fundamental distinction between statistical models and formal ontologies or logical models.

Key Differences Between Statistical Approaches and Formal Models:

1. Statistical Approaches (Correlations, Patterns, and Inference):
2. Formal Models (Ontologies, Logic, and Rules):

The Role of Statistical Models in Formal Knowledge Representation:

While statistical approaches can't build valid formal models on their own, they can play a complementary role in enhancing or refining formal models:

• Identifying Gaps: Statistical models can help identify new patterns or relationships that haven't been previously discovered, which can be useful for expanding an ontology or updating formal models.
• Hypothesis Generation: Statistical analysis can generate hypotheses about potential relationships or structures that can later be formally tested and validated in a logical framework.
• Validating Predictions: Once a formal model (ontology) is constructed, statistical models can help test its predictive power by seeing how well the model performs on new data.

However, as you've pointed out, statistical methods cannot replace the need for formal, logical structures. They can assist in refining the model, but the rigor and validity of formal models depend on the logical foundations and verification processes that are separate from statistical inference.

Why Statistical Models Can't Create Valid Formal Models:

1. Lack of Logical Foundations: Statistical models are built on patterns in data, but they don't have a logical framework that guarantees their findings are consistent or meaningful in a formal sense. They can't reason about the causal structure of relationships, which is essential for constructing valid models.
2. Context-Dependency: Statistical models often work in a context-dependent manner. For example, a model that works well for a specific population or dataset may not generalize to other contexts. In contrast, formal ontologies aim to be generalizable across contexts by defining clear, structured relationships that hold consistently regardless of the data source.
3. Inability to Capture Complex Relationships: Some complex relationships, especially those involving hierarchical structures or specific types of logical reasoning (e.g., transitivity or equivalence), are hard to capture purely with statistical approaches. Formal logic, on the other hand, can describe these relationships in precise terms.

Conclusion:

• Statistical models can be extremely useful for discovering patterns, correlations, and making predictions, but they lack the rigor to establish valid formal models for reasoning. They are great at identifying relationships but don’t guarantee that those relationships are logically sound or causally valid.
• Formal models, such as ontologies, provide the logical structure and consistency needed for reasoning and decision-making, ensuring that relationships and classifications are precise and scientifically valid. They are the foundation for explainability and interpretability in complex systems.

In essence, while statistical models can guide the evolution and refinement of formal models by identifying new patterns and insights, formal models provide the rigorous foundation necessary for understanding, reasoning, and validating knowledge in a structured, logical way.

Impact of redundancies?

In multivariate statistical inductive methods, redundancy in variables (often referred to as multicollinearity) can be a significant issue. Redundant variables can negatively affect the performance of models by making them unstable, hard to interpret, and prone to overfitting. This is a key challenge, particularly in statistical AI and machine learning approaches, where noisy or inaccurate data is prevalent. Let’s break down why this is a concern and how it's typically addressed:

The Issue with Redundant Variables in Statistical AI:

1. Multicollinearity and Redundancy:
2. Impact of Noisy Data:
3. Overfitting:
4. Generalization Problem:

How Statistical AI Deals with These Issues:

1. Feature Selection:
2. Dimensionality Reduction:
3. Regularization:
4. Robust Algorithms:
5. Data Preprocessing:
6. Transfer Learning:

How Statistical AI Models Can Evolve Based on New Information:

While statistical AI techniques are often criticized for working with noisy or imperfect data, they can adapt and improve over time as more data is collected or as the models are refined. Some ways in which AI models can evolve based on new information include:

1. Online Learning: This allows models to update incrementally as new data arrives, enabling the system to evolve progressively over time. It helps prevent models from becoming outdated and ensures that they reflect new patterns or changes in the data.
2. Continuous Improvement: Models can be periodically retrained on new data, allowing them to refine their patterns and improve predictions. This ensures that the model adapts to changes and can better handle noisy or evolving data over time.
3. Adaptive Reasoning: In the context of agentic systems, combining AI with ontologies and formal reasoning allows for adaptive models that evolve as new knowledge becomes available. As the agent encounters new situations, it can update its reasoning process by incorporating the new data, while still maintaining the formal structure and validity of the ontology.

Conclusion:

• Redundant and noisy data is indeed a significant challenge in statistical AI and machine learning. It's essential to use techniques like feature selection, dimensionality reduction, and regularization to mitigate the impact of these issues.
• While statistical approaches are great at discovering patterns and adapting based on new data, they can't guarantee that the data is logically coherent or causally valid. Formal models like ontologies provide a way to structure and validate knowledge, which can be crucial for ensuring robust reasoning and decision-making in systems that need to evolve based on both external information and logical consistency.

Does it explain why LLMs ignore true and false?

Yes, in a way, the points we discussed regarding statistical AI and the challenges with noisy data, redundancy, and formal reasoning can help explain why large language models (LLMs) like GPT often don't explicitly differentiate between true and false, or good and bad, in the same way humans might expect. Here's a breakdown of why that happens:

1. Statistical Nature of LLMs:

• LLMs, like GPT, are primarily statistical models. They are trained on large datasets and learn to predict the next word or token based on the statistical likelihood of what comes next in a sequence.
• Truth and falsehood are not directly encoded in the training data. Instead, LLMs learn patterns of language and associations based on their training data, but they don’t inherently possess a formal understanding of truth or falsehood.
• The model doesn't "know" truth in a human or logical sense—it only produces outputs that are statistically likely based on patterns in the training data. This can lead to the model generating text that may sound plausible but may not necessarily align with truth.

2. Training on Noisy and Imperfect Data:

• The training data for LLMs often comes from a wide variety of sources, including web pages, books, forums, and other texts. Much of this data can contain falsehoods, biases, or incorrect information, which makes it difficult for the model to distinguish true from false information on its own.
• Since the model is essentially a statistical approximation of human language, it’s not equipped with a framework to validate whether statements are true or false (as formal ontologies or logic systems would). It simply predicts what is likely to come next, based on patterns it has learned.

3. Absence of a Formal Reasoning Layer:

• While some LLMs are incorporating reasoning components, they typically don’t have an inherent reasoning structure like an ontology or a logic-based system. Ontologies and formal reasoning systems allow for a more structured understanding of concepts, relations, and the validation of truth (e.g., using rules like "A is true if and only if B happens").
• LLMs generally lack this capability. So, for example, if you ask an LLM "Is 2+2=5?", it might not always respond with a direct logical refutation, but rather continue based on patterns in language, such as offering contextually similar statements or responding with uncertainty.

4. Focus on Language Fluency, Not Logic:

• LLMs are designed to generate fluent and contextually appropriate language. The goal is to match patterns in the input text, not necessarily to assert truth or falsehood in a rigorous, logical way. When asked a question or given a prompt, the LLM tries to fill in the blanks based on its learned patterns, even if those blanks contain inaccuracies.
• This is why LLMs often don’t explicitly offer moral judgments or truth values unless specifically prompted to do so. Without formal reasoning or ethical grounding in the training process, it defaults to generating text that is linguistically appropriate rather than factually accurate.

5. Lack of Causality and Logical Structures:

• Traditional logic and reasoning rely on causal relationships (e.g., "If X happens, Y will follow") and formal systems that allow for proofs or verification. However, LLMs do not inherently model causal relationships in a formal, logical sense. Instead, they learn to generate likely continuations based on previous text.
• This statistical approach doesn't inherently incorporate causal reasoning or truth tables (like in formal logic or ontology-driven systems), which are necessary for making definitive statements about what is true or what is false.

6. Ambiguity and Contextualization:

• In many cases, what is true or false, or good and bad, can be context-dependent and subjective. LLMs don’t have intrinsic understanding of these values—they can only generate outputs based on patterns that they’ve seen in the data. For example, the model might not provide a definitive judgment on whether an action is good or bad because it understands those concepts differently based on the data it was trained on.
• In ethical or moral contexts, LLMs are often trained with a wide variety of perspectives, leading to ambiguity in their responses. They generate text that reflects statistical trends in how different viewpoints are discussed but don’t necessarily offer a definitive answer rooted in formal logic or ethics.

7. Why LLMs Can't Be Trusted for Truth:

• Because LLMs operate based on patterns and not formal validation of facts, their outputs should be interpreted as linguistic predictions, not factual truths. The accuracy of an LLM’s response can be evaluated based on the data it was trained on, but it’s not inherently equipped to separate truth from falsehood without additional tools like external fact-checking, ontological reasoning, or external knowledge databases.
• This is why tools that combine LLMs with formal reasoning systems or knowledge graphs are being developed to improve the reliability and truthfulness of the responses.

In Summary:

LLMs like GPT are pattern-based models that generate text based on learned associations, not based on formal logical reasoning. They do not inherently distinguish between true and false, or good and bad because they lack an internal formal system of reasoning. They simply predict what is likely to come next in a given context, which is why truth and accuracy are not guaranteed. For an LLM to provide reliable outputs, it must be paired with systems that validate the information, such as ontologies or fact-checking systems, or used in contexts where linguistic fluency is the primary goal rather than logical consistency or truth.

Are agentic agents a way to combine two approaches with a needed accurate process performed by people in a one's hand, with accurate scientific approach and data quality process in the other's hand?

Yes. An agentic agent is generally a system that combines autonomous decision-making with purposeful, goal-driven behavior. It can involve blending different approaches, such as machine learning, ontological reasoning, and human oversight, to address specific tasks or challenges. In this context, the need for accuracy—especially in domains where data quality and scientific rigor are critical—becomes paramount.

How an Agentic Agent Combines Approaches:

1. Autonomous Decision-Making with Human Oversight:
2. Machine Learning + Ontological Reasoning:
3. Data Quality and Process:

Combining Approaches for Accuracy:

To achieve accuracy in scientific applications, an agentic agent might combine the following elements:

1. Machine Learning:
2. Ontological Reasoning:
3. Human-in-the-Loop:
4. Feedback Loops:

Use Cases:

• Medical Diagnosis: An agentic agent might use machine learning to suggest possible diagnoses based on patient data, while ontological reasoning ensures that the suggestions align with the latest medical knowledge. Humans validate the final diagnosis based on their expertise.
• Research: In scientific research, an agentic agent could automate the process of data collection and initial analysis, but human researchers would oversee the interpretations to ensure scientific rigor and the use of valid methodologies.
• Industrial Systems: In complex manufacturing or engineering systems, the agentic agent could monitor processes, identify anomalies, and optimize operations, while humans intervene when precision and data accuracy are crucial.

Conclusion:

An agentic agent combines autonomy and machine learning with ontological reasoning and human oversight to create systems that can perform complex tasks with accuracy and adaptability. The scientific approach and data quality processes ensure that the system remains grounded in reliable, validated knowledge while also being able to evolve and adapt based on new data.

So let's imagine we have to explain all this, agentic agent with therole for GIN and ontology, the required process. Then let's consider we've to explain how it can be studied and assessed for creating value for enterprise. What should it be?

Part 1: Explaining Agentic Agents with GIN and Ontology

1. What is an Agentic Agent?

An agentic agent is a system that has the ability to act autonomously toward achieving a specific goal or set of objectives. It combines the ability to make independent decisions based on its programming or learned experiences with a sense of purpose, guided by the objective it’s trying to achieve. This could involve solving problems, optimizing processes, or making predictions.

In simpler terms, it’s like a robot or software that can think and act on its own, but it can also be influenced by humans when necessary (e.g., giving instructions or adjusting its behavior).

2. Why is GIN Important for Agentic Agents?

Graph Isomorphism Networks (GIN) are a type of neural network that helps understand relationships between data points that are structured like a graph. Think of graphs like social networks or supply chains where everything is connected—each node (person, product, etc.) has relationships with other nodes (friends, suppliers, etc.).

For agentic agents, GIN allows the system to understand these complex relationships and make decisions based on them. For instance, in a recommendation system, GIN could help the agent understand which users are similar to each other, how products are related, and make personalized recommendations.

How does GIN work? GIN works by considering the relationships between all the neighboring nodes in the graph and learning how to update the state of each node (data point) based on the state of its neighbors. This is done through a process called message passing, where each node sends information to its neighbors, learns from the data, and then updates itself accordingly. This helps the agent understand complex relationships and make informed decisions.

3. Why is Ontology Important for Agentic Agents?

An ontology is a formal representation of knowledge that defines concepts and their relationships. It provides a framework to understand how different things are related to each other.

For example, in healthcare, an ontology might define that "fever" is a symptom of "infection", and "infection" is a type of "disease". These relationships are structured hierarchically, and they help an agent know the connections between different types of knowledge.

Why use ontologies with agentic agents? Ontologies help the agent reason about knowledge in a structured and logical way. They provide a rigid framework where the agent can check if its conclusions are consistent and aligned with what’s already known. This prevents the agent from making decisions based on inaccurate or incomplete information.

When an agent uses ontological reasoning, it doesn’t just act on raw data; it can follow rules and relationships that help it draw valid conclusions. For instance, in a medical system, if the agent encounters a combination of symptoms, it can use the ontology to ensure that its diagnosis is consistent with known medical classifications.

4. Combining GIN and Ontology for Agentic Behavior

When GIN is used alongside ontologies, we get a powerful combination. GIN helps the agent learn patterns and relationships from raw data (e.g., identifying which symptoms often occur together), while the ontology ensures that those patterns are consistent with scientifically valid knowledge.

This is especially important for tasks that require both learning from data and rigorously structured knowledge. The GIN can adapt to new patterns, while the ontology ensures that the system’s conclusions stay grounded in verified knowledge.

5. The Required Process

For an agentic agent to be effective, it requires:

• Data Gathering: Collecting data from reliable sources (e.g., sensors, databases, human input).
• GIN Processing: Using Graph Isomorphism Networks to learn relationships between data points and understand the connections in the data.
• Ontology Integration: Integrating the structured knowledge from ontologies to ensure that reasoning is based on validated and accurate knowledge.
• Human Oversight: Ensuring that experts can review, validate, and adjust the agent’s decisions when necessary.
• Continuous Learning: Allowing the agent to adapt and evolve its behavior based on new information, while still respecting the ontological framework.

Part 2: How Agentic Agents Can Be Studied and Assessed for Creating Value in Enterprise

1. Assessing the Impact of Agentic Agents in Enterprise

To understand how agentic agents can create value in an enterprise, we need to assess their effectiveness, efficiency, and alignment with business goals. This includes:

• Problem-Solving: Is the agentic agent solving the problems it was designed for? Are the solutions it provides more efficient or accurate than traditional methods?
• Decision-Making: How well does the agent’s decision-making align with the company’s goals? Does it make decisions that improve productivity, reduce costs, or enhance outcomes?
• Human Collaboration: How effectively does the agent work alongside human experts? Does it provide insights that are actionable, or does it require too much intervention?

2. Studying the Performance of Agentic Agents

To study an agentic agent, you would look at:

• Data Quality and Integration: How well does the system handle and process the data it’s given? Is the data clean, and is the ontological reasoning applied properly?
• Learning Capabilities: How well does the agent learn over time? Can it adapt to new patterns, or is it limited by its initial training data?
• Accuracy and Consistency: How accurate are the decisions made by the agent? Does it stay consistent with established knowledge, as ensured by the ontology?

3. Metrics for Enterprise Value Creation

To measure the value created by agentic agents in an enterprise, we could use metrics such as:

• Cost Reduction: Does the agent help reduce operational costs by automating tasks or improving decision-making?
• Productivity Gains: Does the agent increase the efficiency of processes, allowing employees to focus on higher-value tasks?
• Risk Mitigation: Does the agent help mitigate risks by making more accurate predictions or ensuring decisions align with industry standards and regulations?
• Innovation: Does the agent help generate new ideas, identify emerging trends, or uncover insights that weren’t previously possible?

4. Examples of Agentic Agents Creating Value in Enterprises

• Healthcare: In a hospital setting, an agentic agent could help doctors make better diagnoses by combining data from patient records, medical literature, and ontologies of diseases. By doing so, it could reduce misdiagnosis and improve treatment outcomes.
• Supply Chain: In supply chain management, an agentic agent could optimize inventory by analyzing relationships between supply chain nodes (using GIN) and ensuring decisions align with industry standards (using ontologies). This could reduce costs and improve efficiency.
• Customer Service: An agentic agent in customer service could handle routine queries, freeing up human agents to focus on more complex issues. By learning from interactions (GIN) and using ontologies to classify issues, it can provide better customer experiences.

Conclusion:

An agentic agent combines the learning power of Graph Isomorphism Networks (GIN) with the structured reasoning of ontologies to autonomously solve complex problems while ensuring that decisions are grounded in accurate and validated knowledge. The required process for an agentic agent involves collecting reliable data, applying advanced reasoning (both learned and formal), and ensuring human oversight.

When studying and assessing its impact in an enterprise, we focus on measuring the agent’s effectiveness, its ability to adapt and learn, and the value it generates, such as through cost reduction, productivity gains, risk mitigation, and innovation. Ultimately, agentic agents can significantly enhance decision-making and operational efficiency within businesses.

Is it a silver bullet? Is it still research?

No, an agentic agent is not a silver bullet, and it is still very much a research area with a lot of development ahead. While the combination of Graph Isomorphism Networks (GIN) and ontology-based reasoning presents a promising approach, several challenges remain before this technology can be widely deployed and trusted in production environments.

Why it's not a silver bullet:

1. Complexity of Integration: Combining GIN with ontology-based reasoning requires careful integration. Ontologies need to be well-defined and curated for them to be useful. In many cases, these ontologies are incomplete, out-of-date, or not precise enough to guide accurate decision-making. GIN, while powerful for identifying relationships, can still struggle with noisy data and incomplete information.
2. Data Quality: The system is heavily dependent on the quality of data. If the data fed into the system is noisy, biased, or unstructured, the decisions or predictions made by the agent may be flawed. In business, a lot of data is messy and unstructured, which makes this a challenge for deploying these systems at scale.
3. Explainability and Trust: One of the biggest concerns for real-world applications is explainability. While combining GIN and ontologies can improve explainability in some cases, it’s still not easy for humans to understand why certain decisions are made. In fields like healthcare, finance, and law, this lack of explainability can be a major barrier to adoption.
4. Adaptability and Generalization: These systems can be quite narrow in scope. They may work well within the contexts they are trained on but struggle to adapt to new, unforeseen situations. Businesses often require systems that can generalize well to various domains or edge cases, which these models currently struggle with.
5. Computational Complexity: While Graph Neural Networks are powerful, they also require significant computational resources. For real-time decision-making in large-scale systems, these models can become computationally expensive, making them impractical for many use cases unless optimized properly.

Is this still research?

Yes, this technology is still in research. Although there have been significant advances in areas like Graph Neural Networks, ontology integration, and multi-modal reasoning, many challenges remain in terms of:

• Scalability: Ensuring that these models can work effectively on large, diverse datasets that businesses typically work with.
• Generalization: Improving the models' ability to apply knowledge across different domains without explicit retraining.
• Performance and Efficiency: Optimizing computational requirements to ensure that these systems can be used in real-time applications without overwhelming system resources.
• Validation and Evaluation: A lack of standardized benchmarks for evaluating these systems in various contexts means that it can be hard to assess their true value and effectiveness.

In Summary:

While the combination of GIN and ontology-based reasoning is highly promising and has the potential to significantly improve decision-making in enterprises, it is not a silver bullet. It still faces multiple challenges, including data quality, computational complexity, and explainability, and is largely in the realm of research and experimental deployment at this stage. Many of these challenges need to be addressed before they can become reliable and scalable solutions for real-world applications. So, although the approach is exciting, it’s not a ready-made solution for every problem just yet.

For curiosity, link and similarity with Principal Component Analysis and clustering (we stay here in the statistical world)

Principal Component Analysis (PCA) and clustering are two fundamental techniques in data analysis that share some similarities with agentic agents, especially when combining Graph Isomorphism Networks (GIN) and ontology-based reasoning. Let's break it down:

Principal Component Analysis (PCA)

PCA is a statistical technique used to reduce the dimensionality of data while retaining as much variability (information) as possible. It does this by transforming the original variables into a new set of variables, called principal components, which are linear combinations of the original variables. These new components are ordered by the amount of variance they capture.

Clustering

Clustering is a technique used to group similar data points together, often in an unsupervised manner. Popular methods like k-means clustering or hierarchical clustering aim to identify patterns or structures in data by grouping points that are more similar to each other than to points in other clusters.

Link and Similarities with Agentic Agents (GIN + Ontologies)

1. Dimensionality Reduction and Structure Discovery:
2. Clustering and Grouping:
3. Pattern Recognition and Representation:
4. Data Simplification:

Key Differences:

While PCA and clustering aim at simplifying data and finding meaningful patterns, agentic agents (GIN + ontology) take this further by:

• Adding a layer of semantic understanding (ontology) to the process of learning patterns, making it context-aware.
• Leveraging graph-based structures, which is more flexible for modeling complex relationships compared to the fixed geometric structure of PCA.
• Incorporating dynamic learning through GINs, allowing them to adapt to new data in a more sophisticated manner than traditional clustering or PCA methods.

In Summary:

• PCA and clustering help simplify complex data by either reducing dimensions or grouping data points. In agentic agents, GIN and ontology-based reasoning share a similar goal of simplifying and extracting meaningful relationships from data, but they do so in a more dynamic and context-driven way. The incorporation of ontology ensures that the results are not just based on statistical similarity, but also on a semantic understanding of the data's structure, while GINs improve the model’s ability to recognize and adapt to complex patterns in graph-structured data.

Links with emerging RAGs?

RAG (Retrieval-Augmented Generation) is a powerful approach in natural language processing (NLP) that combines retrieval-based techniques with generative models like transformers. This hybrid approach allows the model to retrieve relevant information from external sources (such as a knowledge base or database) and use it to augment the generative process, producing more accurate, context-aware outputs.

Let's explore how RAG links with Agentic Agents (combining Graph Isomorphism Networks (GIN) and ontology-based reasoning), and how these concepts can intersect:

1. RAG and GIN (Graph Isomorphism Networks)

In RAG, the generative model (such as GPT or BERT) retrieves information from external sources (usually through a retriever component) to inform the generation of a response. This is similar in concept to GIN models, where the goal is to capture and utilize graph-based relationships in a more structured way.

• Graph Isomorphism Networks excel in learning relationships and patterns within graph structures. These relationships could represent connections between entities, such as knowledge or concepts in an ontology.
• In RAG, the retrieval step can be seen as pulling information from a graph-based knowledge base (e.g., a graph of concepts, facts, or relationships), similar to how GINs would learn from graph data.

2. Ontology-Based Reasoning in RAG

Ontologies provide a structured framework of knowledge where concepts are classified, and semantic relationships like "is-a", "part-of", and "related-to" define the structure of the knowledge. In RAG, the retrieved information could be enhanced with ontology-based reasoning to ensure that the response generated is not only contextually appropriate but also semantically correct.

• Ontology Reasoning allows for structured, hierarchical reasoning and semantic validation, ensuring that relationships and concepts are used in a logically sound way. This process would augment RAG by ensuring that the retrieved data respects the semantic rules of the knowledge domain.

3. RAG and Dynamic Learning / Adaptation

One of the challenges with RAG is ensuring that the model adapts to new or changing data, especially when the data used for retrieval is dynamic or comes from multiple sources.

• GINs and ontology reasoning bring adaptability to agentic systems, allowing the system to update its understanding of the relationships in the graph or the ontology as new data is introduced.
• GINs enable the model to learn patterns dynamically through message-passing and graph updates, while ontology-based reasoning ensures that new relationships or concepts introduced into the system are logically consistent with the existing framework.

4. RAG and Explainability

RAG models can sometimes produce results that are difficult to explain, especially when the retrieval and generation steps are complex and involve large amounts of data. The integration of ontology reasoning and GIN can help improve explainability by providing a clear, structured framework for understanding the relationships between different pieces of information.

• Ontology reasoning can act as a semantic guide that makes it easier to understand why certain information was retrieved and how it fits within the larger knowledge base.
• GINs, with their ability to focus on graph relationships, can help the model track the path of information from the retrieval step to the final generated output, making it clearer how relationships between nodes in the graph influenced the result.

Conclusion: RAG and Agentic Agents

Both RAG and Agentic Agents (with GIN and ontology reasoning) share a common goal of improving the quality and context-awareness of the outputs produced by the model, especially in the context of complex, real-world data. By combining retrieval with generation, RAG systems aim to enhance the generative model with relevant, accurate information. Adding GIN and ontology reasoning can improve this process by:

1. Enhancing retrieval: By considering the semantic relationships between pieces of data, ensuring that the retrieved information is relevant and context-aware.
2. Ensuring consistency: Ontology-based reasoning can ensure that the data used is consistent with the logical structure of the domain.
3. Improving adaptability: Both GIN and ontology reasoning help the system adapt and learn from new data and relationships over time, ensuring that it can evolve with new information.

This combination can make RAG-based systems much more effective in providing accurate, semantically valid, and contextually relevant responses, especially when dealing with complex, graph-based data.

Can RAG can be used for implementing agentic agents?

Yes, RAG (Retrieval-Augmented Generation) can indeed be used as a foundational component for implementing Agentic Agents. The combination of RAG with Graph Isomorphism Networks (GIN) and ontology-based reasoning can provide a powerful framework for intelligent agents that not only retrieve information but also reason, adapt, and provide context-aware outputs.

Here’s how RAG can support Agentic Agents:

1. Retrieval for Context-Aware Generation:

• RAG systems are based on combining a retrieval mechanism with a generative model. When an agent is tasked with generating responses or taking actions, it first retrieves relevant data (e.g., from a knowledge base or database). This ensures that the agent has access to a rich, contextually relevant set of information.
• GIN and ontology reasoning can help structure the data and guide the retrieval process, ensuring that the data retrieved is not just random but semantically consistent and meaningfully connected to the agent’s task.

2. Graph-based Reasoning for Better Understanding of Relationships:

• Graph Isomorphism Networks (GIN) specialize in capturing complex relationships between entities in a graph. By using GIN, you can model these relationships in a structured way, which can inform how an agent interprets new information and updates its understanding of its environment.
• In an Agentic Agent scenario, GIN can be used to enhance the retrieval process, helping the agent prioritize certain types of information based on graph relationships, semantic relevance, or contextual fit. This means that when the agent performs a task or makes a decision, the information it retrieves is not just based on keywords or basic patterns, but also on how different pieces of knowledge relate within a graph structure.

3. Ontology-based Reasoning for Structured Knowledge:

• Ontologies provide a formal, hierarchical structure of knowledge where entities are related by defined semantic relationships. This allows the agent to reason about the world in a rigorous, structured way, taking advantage of relationships like "is-a", "part-of", and transitive reasoning.
• RAG can benefit from ontology reasoning to ensure that the knowledge the agent retrieves is logically consistent and well-aligned with domain-specific knowledge. For example, an Agentic Agent in a healthcare setting might retrieve information about symptoms and diseases, but ontology reasoning would help the agent understand how these symptoms fit within broader categories or how they relate to other conditions, ensuring more accurate decision-making.

4. Dynamic Learning and Adaptation:

• One key advantage of using RAG in conjunction with GIN and ontologies is that the system can adapt and evolve over time. As the agent interacts with its environment, it can update its knowledge, learn new relationships, and refine its reasoning.
• GIN can be used to update the relationships between entities in a graph, while the ontology ensures that these updates are consistent with the semantic rules of the domain. This continuous learning process is crucial for Agentic Agents that need to adjust to new information and changing environments, similar to how human agents learn and adapt over time.

5. Explainability and Justification:

• One of the challenges with traditional generative models like GPT or BERT is that their reasoning can often be a "black box". With RAG, the retrieval process itself can be made more transparent, and the ontology can provide a structured explanation for why certain pieces of information were chosen.
• GIN and ontology reasoning help make the agent’s decision-making process more explainable by ensuring that the agent’s actions are based on structured relationships and verified knowledge.

Example Use Case: Healthcare Agentic Agent

Imagine a healthcare agentic agent that helps doctors diagnose diseases based on symptoms:

1. RAG would first retrieve relevant information from medical databases, such as symptoms, diseases, and treatment options.
2. GIN would ensure that the agent understands the relationships between diseases, symptoms, and their possible connections in the knowledge graph.
3. Ontology reasoning would help the agent reason about the hierarchical relationships (e.g., a disease is a type of condition) and validate the information retrieved (e.g., ensuring that the symptoms fit within known disease categories).
4. The agent could generate a recommendation or diagnosis, explain its reasoning process based on the data, and adapt as more medical knowledge is introduced.

Conclusion:

By integrating RAG with GIN and ontology reasoning, you can build Agentic Agents that are not only capable of retrieving relevant information but also reasoning about that information in a structured, semantically coherent way. This combination provides the agent with the ability to handle complex tasks, adapt to new information, and provide explainable outputs that can support decision-making in diverse domains, including healthcare, finance, and enterprise environments.

In short, RAG is a key enabler for implementing Agentic Agents, and when combined with GIN and ontology reasoning, it can create powerful systems capable of intelligent, context-aware decision-making and continuous learning.

Explaining RAGs for a child?

Imagine you're playing a game where you need to solve a mystery. You have a big book full of clues (this is like a knowledge base), but it's really hard to go through the whole book every time you need an answer. So, what do you do?

You ask your friend, who knows where to find the right clues in the book, to help you. Your friend looks at the book, quickly picks out the most useful clues, and then gives them to you. Now, with these clues, you can solve the mystery!

This is what RAG does in simple terms.

• RAG stands for Retrieval-Augmented Generation.
• Retrieval is like asking your friend to find the best clues.
• Augmented Generation is like you using those clues to help create a smart answer or solve the problem.

So, RAG helps computers be smarter by first finding the most useful pieces of information (like your friend helping you with the clues) and then using them to come up with the best response or solution. It's like a computer that has both a smart memory (the clues) and the ability to think on its own (making sense of those clues).

In short: RAG is like asking a helpful friend for the right clues, then using those clues to come up with a good answer!

Conclusion

Here is the end of this exploration of what Agentic Agents are.

Let's note I'm not an expert on the topic, even not a specialist.

I just tried to understand what it is about and to link it to what I know.

After the exercise, I'm a little bit less ignorant. Now I've to go deeper an particular use cases and eventually to experiment with some legacy tools, on some topics related to my expertise.

Waiting for that, all comments are welcome for improving and correcting what was collected.

Resources

Many explanation of Agentic Agent or RAG on the web. Probably to be analyzed and synthetised in order to improve the current article.