For ChrisRoyse's https://github.com/ChrisRoyse/Self-Conceptualizing-KG.git Includes some use cases for research Bradley Ross is working on
CogniGraph Validation and Implementation Plan
This plan outlines how to validate and implement CogniGraph (Self-Conceptualizing Knowledge Graph) with real-world benchmarks and a robust framework for performance evaluation. It also describes integration steps with GitHub for continuous testing and scoring. The goal is to ensure CogniGraph is practically useful and meets key performance metrics in symbolic reasoning and coding intelligence scenarios.
- Real-World Benchmarks
To demonstrate CogniGraph’s capabilities, we will apply it to two practical domains and evaluate its performance:
1.1 Symbolic AI & AGI Knowledge Graphs
Use Case: Utilize CogniGraph to structure a knowledge graph that can support symbolic reasoning tasks, as might be required in an AGI system. This involves automatically conceptualizing raw information into a structured form (entities, relationships, and rules) for reasoning. • Knowledge Structuring for Reasoning: Feed CogniGraph with domain knowledge (e.g. a set of factual documents or a knowledge base excerpt) and have it generate a knowledge graph. The system should identify key concepts and relationships autonomously (“self-conceptualization”). This addresses the challenge that manually crafting ontologies and graphs is time-consuming and doesn’t scale well . By automating conceptualization, CogniGraph can rapidly build the symbolic knowledge needed for reasoning. • Symbolic Reasoning Tasks: Integrate the resulting knowledge graph with a symbolic reasoning engine or rule-based system. For example, we can define logical rules or use a query language (like SPARQL or Cypher) to answer questions that require multi-hop reasoning. Benchmark tasks might include answering queries such as “Is X a kind of Y?” or “What sequence of events leads to Z?” using only the structured knowledge (and not raw text). The correctness of answers derived from the CogniGraph structure will be compared to a baseline (e.g. an LLM using raw text). • Expected Outcomes: We expect CogniGraph’s structured knowledge to enable high accuracy on multi-step reasoning queries, potentially outperforming unstructured retrieval. Knowledge graphs can improve search relevance by using explicit relationships, leading to more accurate responses in AI applications . For instance, on complex queries requiring combining multiple facts, a well-structured knowledge graph can achieve very high accuracy (one study noted KGs achieved near 100% accuracy on certain complex reasoning tasks, far above vector search) . We anticipate CogniGraph will allow symbolic AI modules to reliably answer such queries without hallucination, thanks to the graph’s precision and explicit semantics. • Implementation Steps: 1. Data Selection: Choose a domain for evaluation (e.g. a commonsense knowledge domain or a specialized corpus). As a simple benchmark, we might use a known dataset like ConceptNet or a small slice of Wikidata to compare CogniGraph’s auto-generated graph against a ground truth graph. 2. CogniGraph Execution: Run CogniGraph on the input data to build the knowledge graph. Verify that major entities and relations are captured. 3. Reasoning Integration: Use a reasoning tool (like an RDF reasoner or custom rule engine) on the CogniGraph output. Pose a set of query tasks (fact retrieval, inference of implicit facts, consistency checks). 4. Evaluation: Measure the accuracy of the answers or inferences. For example, if the task is question-answering, calculate the percentage of questions correctly answered using CogniGraph’s knowledge vs. using a traditional Retrieval-Augmented Generation (RAG) baseline on the same data. • Modifications Needed: If CogniGraph doesn’t already support logical querying, we may need to implement an interface (e.g. export to an RDF store or provide a graph query API). Additionally, to better support symbolic reasoning, CogniGraph might be extended to capture not just entities and relations but also hierarchies and ontologies (type information, class memberships, etc.), enabling more complex reasoning. Ensuring the graph is stored in a format compatible with symbolic reasoners (such as Neo4j, RDF graphs, or an in-memory networkx graph) will be important.
1.2 Coding Intelligence (Code Knowledge Graphs)
Use Case: Leverage CogniGraph to build a knowledge graph from a codebase, summarizing code structure (files, functions, classes, and their relationships such as function calls or API usage). This structured representation will support code understanding, search, and retrieval – a form of “coding intelligence” for developers or AI assistants. • Graph Construction from Code: Input to CogniGraph will be a collection of source code files (e.g. a small open-source project). We will implement a parser or use existing tools to extract code elements: functions, classes, modules, and the relationships between them (function A calls function B, module X imports module Y, etc.). CogniGraph will then organize these elements into a knowledge graph. Each node could represent a code entity (with properties like name, type, documentation) and edges represent relationships like “calls”, “defines”, “uses”, “inherits”. The result is a graph that serves as a code map for the project. • Enabling Vector-Based Retrieval: Once the code knowledge graph is built, we will integrate it with vector search for semantic retrieval. For example, we can generate embeddings for each code node (perhaps combining code signatures and docstrings) and store them alongside the graph. This hybrid approach allows two modes of querying: (a) structured graph queries – e.g., find all functions that call a certain API (traverse “calls” edges), and (b) semantic similarity queries – e.g., find functions related to a keyword via vector similarity. We will use CogniGraph’s structure to refine or filter the vector-based results. (This is analogous to emerging “Graph-RAG” approaches that combine graph traversal with embedding search .) • Benchmark Tasks: Evaluate the effectiveness of the code knowledge graph for answering code-related questions. For instance, benchmark on tasks like: • API Usage: “Which functions in the codebase use the X library or API?” – CogniGraph should quickly retrieve relevant functions via the “uses” relationship, whereas a traditional keyword search might miss synonyms or require scanning many files. • Code Navigation: “What is the call chain of function foo (which functions does foo call, and what calls foo)?”. The knowledge graph can produce this chain by traversal. We can compare this with a baseline where an LLM searches through code without a graph. • Bug Impact Analysis: “If function bar has a bug, what parts of the system could be affected?” – the graph can help find all modules or functions connected to bar via calls or data flows, providing a quick impact analysis graph. • Code Search Accuracy: Given a natural language description of a function’s behavior, retrieve the top relevant function from the code. Here we compare CogniGraph-enhanced retrieval vs. pure vector search. The expectation is that adding the graph structure (for example, filtering results to those in the relevant module or connected to relevant concepts) will improve precision of the search results . • Expected Outcomes: Code knowledge graphs are expected to improve recall and precision in code retrieval tasks. Prior research shows that RAG systems on code can struggle with complex code bases and miss relevant snippets due to shallow text matching . By contrast, using a graph of code, an LLM or tool can execute precise queries to fetch relevant code segments, leading to competitive or improved performance on code understanding benchmarks . We anticipate CogniGraph will enable higher retrieval accuracy for code queries – e.g., retrieving the correct function in fewer tries – as the graph provides rich context (which functions relate to which concepts). In the CodexGraph study, leveraging a graph database for code allowed the LLM agent to better comprehend code structure and retrieve needed code fragments more effectively . We expect similar benefits: more exact answers to “where is this used?” and better multi-hop navigation in code. There is a trade-off that building and querying the graph may add overhead, but the gain is in correctness and the ability to handle more complex queries (like multi-hop dependency questions) that pure vector search might fail. • Implementation Steps: 1. Codebase Selection: Choose a repository for testing (for example, a well-documented smaller project where ground truth is known for certain queries). Alternatively, use a subset of a larger code dataset (like a few modules from a popular open source library) to ensure manageability. 2. Parser Integration: Develop or integrate a code parser to feed CogniGraph. This could use existing tools (AST parsers in Python for Python code, or universal ctags for multiple languages) to extract entities and relationships. If CogniGraph already has a text ingestion pipeline, we may adapt it to accept structured input (e.g., a JSON of relationships) for code. 3. Graph Construction: Use CogniGraph to ingest the parsed code relations and build the internal knowledge graph. Ensure the schema (types of nodes/edges) can represent code relationships; we might extend CogniGraph’s schema if needed (e.g., add new relation types like “calls”). 4. Embedding Index (optional): For semantic search, generate vector embeddings for each code node (using codeBERT or similar model for code documentation). Set up a simple vector index (using an open-source vector DB or even in-memory FAISS) to support similarity queries. This index will be used in combination with the graph. 5. Query Experiments: Pose a set of queries to the system. Some queries will use purely graph traversal (to test structured lookup), others will use vector search first then refine via graph context. Compare each query’s result to a baseline. For baseline, we can use a standard code search approach (text or embedding search without the graph). For example, ask an LLM to answer the question using only file text vs. using CogniGraph’s knowledge, and compare correctness. 6. Evaluation: Measure success in terms of accuracy (did we find the correct code element?), and effort (how many files or lines had to be examined). We expect the CogniGraph approach to find the correct answers more directly. If available, use metrics from code understanding benchmarks – e.g., precision@K (does the correct result appear in the top K suggestions). Also note the time taken for queries with and without the graph. • Modifications Needed: CogniGraph may require enhancements to handle code as input. This includes: • Adding a code ingestion module (if one isn’t in the codebase yet) that reads code and emits the initial graph structure. This might be a new component in the repository. • Extending the knowledge graph schema to include code-specific entity types (Function, Class, Module, etc.) and relation types. • Optimizations for graph size: even moderate codebases can have thousands of nodes (functions, variables). We may need to optimize in-memory storage or allow CogniGraph to use an external graph database for scalability. • If performing vector similarity, integrating a vector search library (this can be optional or for evaluation only, not necessarily part of CogniGraph’s core, but a supporting script).
Both the symbolic and coding benchmarks will validate CogniGraph’s versatility: from abstract knowledge reasoning to concrete code analysis.
- Benchmarking Framework
A thorough benchmarking framework will be established to quantify CogniGraph’s performance. We will define clear metrics and test the system on real data to measure each. Key performance indicators include scalability, retrieval accuracy, and computational efficiency. Additional considerations like conceptualization quality and maintainability will also be observed.
2.1 Performance Metrics: • Scalability: This measures how well CogniGraph handles increasing graph size and data volume. We will evaluate: • Graph Build Scalability: How does the time and memory required to construct the knowledge graph grow as we feed more data (more documents or larger codebases)? We will record the build time and peak memory for different sizes (e.g., 1k, 10k, 100k knowledge triples). • Query Scalability: How does query performance degrade as the graph size grows? For instance, on a small graph, a typical lookup might be 50ms, but on a graph 10× larger, is it 500ms, 5 seconds, or worse? We will simulate heavy loads by running batches of queries on graphs of various sizes. Method: Use synthetic data generation if needed to create progressively larger knowledge graphs (for a controlled scalability test). For example, replicate a subgraph structure multiple times to simulate thousands of nodes. Measure throughput (queries per second) and latency (per query) at each scale. Also monitor memory footprint as a function of graph size. Targets: The aim is to identify the upper limits of CogniGraph on typical hardware and to ensure it can handle real-world scale knowledge graphs (potentially millions of nodes/edges) with acceptable performance. If performance bottlenecks are found, this will guide optimization efforts. For reference, vector databases excel at scaling to millions of entries with millisecond latencies  , whereas graph traversals can slow down on huge graphs . We will document at what scale CogniGraph’s performance might need external database support or architectural changes. • Retrieval Accuracy: This assesses how accurately CogniGraph can retrieve or infer the correct information compared to baseline methods. • Structured vs Unstructured Querying: We will compare the success rate of answering questions using CogniGraph’s structured knowledge vs. using a traditional Retrieval-Augmented Generation pipeline (LLM + vector search). Concretely, for a given set of queries (could be factual questions for the symbolic KG or code queries for the code KG), does the CogniGraph approach return the correct answer (or relevant item) more often? • We will use precision, recall, and F1 as appropriate. For question-answering, accuracy (% of questions answered correctly) is a direct metric. For search-style tasks (like code search), precision@K and recall@K are useful. Method: Prepare a benchmark dataset of queries with known answers. For example, a set of factual questions that have answers in a knowledge source, or a list of code queries where the relevant function is known. Run two systems: (A) CogniGraph-driven (i.e. query the knowledge graph or use it to assist an LLM) and (B) baseline (RAG with just vector DB or keyword search plus LLM). Compare results against the ground truth. Expected Results: We anticipate CogniGraph will improve retrieval accuracy, especially for complex, multi-hop queries or ones requiring precise linking of facts. Knowledge graphs can leverage rich relationships to return exactly the results needed, whereas vector search may return semantically related but not precise info. In enterprise settings, knowledge graphs have been noted to provide more accurate results than vector databases because of their precise, rich relationships . We expect to see this advantage in our benchmarks: e.g., higher question-answer accuracy for CogniGraph on multi-fact questions, and higher precision in code retrieval (fewer false positives). If the baseline RAG answers 80% of questions correctly, CogniGraph should aim above that (perhaps 90%+ in structured domains). Any mistakes CogniGraph makes will be analyzed to refine the conceptualization process (e.g. missing a relationship that caused an answer to be incomplete). • Computational Efficiency: This covers both speed and resource usage during operation. • Processing Time: Measure the end-to-end processing time for building the knowledge graph from a given input dataset. Also measure the time to answer queries using the graph. We will compare query latency with CogniGraph vs. a baseline. Note that graph traversal might be slower for simple lookups compared to a direct vector search, so we want to quantify that overhead . For instance, retrieving a fact via graph might take a few hops in the data structure – we measure that in milliseconds. If using an index or cache, we note that as well. • Memory Usage: Track memory consumption during graph construction and querying. Knowledge graphs store explicit relationships, which can increase memory usage as the graph grows. We will see how memory scales (possibly super-linearly with data if many relationships are created). The plan might involve stress-testing memory by loading a large dataset. • Efficiency vs. Baseline: Also calculate the difference in compute cost between CogniGraph and baseline RAG for the same task. RAG’s cost is mostly embedding and vector search (which is quite fast, but may use heavy compute for embedding large text chunks). CogniGraph’s cost includes concept extraction and graph building. We might find CogniGraph is initially more expensive to set up, but once built, querying can be efficient for certain queries (especially ones that would cause an LLM to use a lot of tokens reasoning). Method: Use profiling tools or built-in timers. For example, instrument CogniGraph code to log the time taken for each major stage (parsing, node extraction, linking, etc.). Use Python’s time or perf_counter for fine-grained timing. For memory, Python’s tracemalloc or an external monitor can capture peak usage. Each test run (for different dataset sizes) will produce a report of time and memory. Targets: Ensure that CogniGraph can build a moderately large graph (e.g. 100k facts) within a reasonable time (perhaps within a few minutes) and answer queries in interactive time (a few hundred milliseconds). If any stage is too slow (e.g., concept extraction from text might be slow if using an LLM; we might then consider caching or optimizations), we will note it and plan improvements. The efficiency benchmark will highlight any bottlenecks to address in code optimizations (for instance, replacing a naive graph traversal with an indexed lookup if needed). • Other Quality Metrics (Conceptualization Accuracy): In addition to the above, we will measure how accurately CogniGraph “conceptualizes” or extracts the knowledge from input. This can be done by comparing the triples/connections CogniGraph creates against a gold-standard. For example, if a text says “Paris is the capital of France”, CogniGraph should have an entity “Paris”, “France”, and a relation “capitalOf” linking them. Any missed or spurious relations can be counted. This gives a precision/recall on the knowledge graph construction itself. Automating this requires ground truth graphs for at least a subset of data, or manual evaluation. We will start with small-scale evaluation where humans verify the generated subgraph for correctness. The goal is to continuously improve CogniGraph’s extraction accuracy, since errors here directly affect the usefulness of the KG.
2.2 Testing with Real-World Datasets:
We will apply the above metrics in realistic scenarios: • For the symbolic KG case, a real-world dataset could be a snapshot of a knowledge base like WikiData or a domain-specific ontology (e.g. a medical knowledge dataset). We might also use a well-known QA dataset (like a subset of CommonsenseQA or OpenBookQA) where answers are based on facts – we can see if CogniGraph captures those facts. • For the code KG case, use an actual code repository (or several). For example, we could take a popular repository (like a web framework’s source) and build the KG. We may also use benchmarks from academic research: the CodeSearchNet dataset or others for code search can provide queries and expected answers to directly measure accuracy in a standardized way.
By testing on real data, we ensure the benchmarks are practical. Each test run will produce a Benchmark Report summarizing: • Graph size (nodes/edges), • Build time, memory, • Query accuracy results (with and without CogniGraph), • Query performance metrics.
These results will guide any necessary tuning of CogniGraph’s algorithms (e.g., if retrieval accuracy is lower than expected, we might improve the entity linking or add missing context to the graph; if scalability is an issue, we might implement database-backed storage or graph compression).
The benchmarking framework will be implemented as an automated suite (see GitHub Integration below) so it can be re-run regularly (e.g., on new releases) to track improvements or regressions. Measurable targets (like handle 1M facts, achieve >90% accuracy on test queries, keep query latency <500ms) will be set based on initial baseline runs.
- GitHub Integration for Testing and Scoring
To ensure continuous validation, we will integrate these benchmarks into CogniGraph’s GitHub repository via automated testing and reporting. This will allow developers to get immediate feedback on how changes affect performance and correctness. The integration will include: • Automated Testing Scripts: We will develop a suite of test scripts (e.g., Python scripts or Jupyter notebooks turned into tests) that execute the validation steps. These will be placed in a dedicated tests/ directory or as part of a continuous integration workflow. The scripts will cover: • Unit Tests for Conceptualization: Small-scale tests that feed CogniGraph a simple input and check the output graph for expected structure. For example, give a mini paragraph or a few code lines with a known relation, and assert that CogniGraph’s output contains that relation. This tests accuracy of knowledge extraction. • Integration Tests for Performance: These tests run CogniGraph on a larger sample dataset (perhaps included in the repository or downloaded in CI) and measure the metrics discussed (accuracy, time, memory). We will programmatically assert certain conditions, e.g., build time should be below a threshold, accuracy above a threshold. If a threshold is not met, the test can flag a warning or failure. For example, we might assert that the accuracy on a QA set is at least 85%, so if a change causes it to drop to 80%, the test fails. • Regression Tests: Ensure that previously encountered bugs in knowledge graph construction do not reappear. For instance, if a certain pattern of input once caused an error or incorrect graph, add a test for that specific case. These scripts will utilize Python profiling for timing and memory where needed, and they will output results in a structured way (possibly JSON or markdown). • Benchmark Results in the Repository: The plan is to make benchmarking outcomes visible within the GitHub repo for transparency. After the tests run, we can have the workflow post the results. This could be done by: • Generating a markdown report (e.g., BENCHMARK.md) that gets updated with the latest scores (accuracy, performance metrics). This can be achieved by the CI job committing the updated file or by using a GitHub Pages/site for detailed results. • Creating badges in the README for key metrics (similar to how test coverage or build status badges are shown). For example, an “Accuracy: 88% on QA benchmark” badge or “Graph Build 100k triples: 120 seconds” badge that updates over time. There are services or custom scripts that can push these updates. • The repository’s wiki or documentation could also have a section for “Performance Benchmarks” where we maintain a history of improvements. By having results directly in the repo, users and contributors can immediately see how CogniGraph performs and that the project maintains certain standards. This fosters trust and makes it easier to compare CogniGraph with other solutions. • Continuous Integration Workflow (Pull Request Checks): We will set up a CI pipeline (using GitHub Actions) that triggers on each pull request and push to the main branch. The CI will run the automated tests described above. Key aspects: • Accuracy and Efficiency Checks: The CI can be configured to fail the build if critical tests do not pass (e.g., if conceptualization accuracy falls below the expected baseline, or if a new commit increases the average query time significantly). This ensures that any code change that inadvertently reduces performance or correctness is caught early. For example, if a contributor refactors a function and it slows down graph building by 2×, the CI can flag this regression. • Resource Monitoring: Because performance is part of our validation, the CI environment will need enough resources to run meaningful tests. We might use GitHub Actions runners with increased memory for large-scale tests or only test smaller scale in CI and reserve full-scale benchmarking for periodic runs. If needed, we can integrate with external benchmarking pipelines for heavy tests, but at least a representative subset will run on each PR. • Automated Feedback: The CI can post a comment on the pull request with a summary of the performance metrics after the tests. For instance, “This change resulted in Build Time: 60s (prev 58s), Query Accuracy: 89% (prev 90%).” This immediate feedback helps developers understand the impact of their changes. If the impact is negative beyond an allowable margin, maintainers can request improvements before merging. • Pull Request Workflow Example: A contributor opens a PR to add a feature to CogniGraph. The GitHub Actions workflow triggers: 1. Set up the environment (install dependencies, etc.). 2. Run unit tests and integration tests. 3. Run the benchmarking script on a standard dataset (this could be a smaller version due to time constraints on CI). 4. Gather results: suppose the accuracy on the mini-benchmark is 88% and build time 30s. 5. The workflow compares this to stored baseline (perhaps from the main branch or from a config file). If accuracy dropped significantly or build time increased beyond a tolerance, it marks failure or warning. 6. Regardless, it posts a summary, and possibly updates a results artifact. 7. The PR cannot be merged until critical tests pass. Non-critical regressions could be just warnings that maintainers review. This process automates validation, making sure CogniGraph remains reliable as it evolves. Notably, since knowledge graphs and symbolic AI components can be complex to integrate, having such automated checks addresses the known challenge that integration of software with knowledge graphs is non-trivial . By continuously testing, we ensure the integration points (e.g., query engines, reasoning components) remain robust. • Reporting and Logging: All test runs will log detailed info, which can be stored as CI artifacts for further analysis. Over time, we accumulate data points (we can plot trends of performance if needed, though plotting in CI might not be directly in the scope of the deliverables, the data will be available). • Codebase Modifications for Integration: To facilitate the above, a few changes to CogniGraph’s codebase and repo structure will be necessary: • Add the tests directory with test cases. This may involve creating some dummy data files (which could live in a data/ folder in the repo or generated on the fly). • Possibly add a command-line interface or utility functions in CogniGraph to easily run the graph construction and querying from a script (if not already present). For instance, a cognigraph.build_graph(input) -> graph function that we can call in tests, and a cognigraph.query(graph, query) for queries. If CogniGraph’s functionality is currently entangled with an interactive workflow, refactoring it into callable functions will help automation. • Implement logging of performance metrics in a machine-readable way. We might add an option like --benchmark that, when enabled, causes CogniGraph to output timing info and counts of nodes/edges, etc., which our CI scripts can parse. • Set up configuration for GitHub Actions (YAML workflow files). This includes installing any dependencies (e.g., graph databases or specific libraries if needed for tests). • Create baseline expectation values for metrics to use in tests (e.g., store expected accuracy in a config, or better, store a “golden” output for certain input to do diff-based comparisons of the graph). • If the repository doesn’t already use continuous integration, enable it and possibly include status badges (like a badge showing “CI: Passing” and another for “Benchmark: Good” vs “Needs Improvement”, etc.).
By implementing the above integration, we align development with continuous validation. Every commit will be a chance to verify that CogniGraph is meeting its design goals of scalable, accurate knowledge graph construction. Moreover, having benchmark scores versioned in the repository creates a form of accountability: if someone proposes a major change, its effect on key metrics is immediately visible. This practice is especially important for projects combining neural and symbolic components, as their interaction can be complex and unintuitive .
⸻
In summary, the validation plan for CogniGraph consists of: (1) Using real-world use cases (symbolic AGI reasoning and code knowledge graphs) to ensure the system delivers practical value, (2) Establishing a rigorous benchmarking framework with clear metrics like scalability, accuracy, and efficiency, and (3) Integrating these tests into the GitHub development workflow for continuous monitoring. Through these steps, CogniGraph’s capabilities will be demonstrated and strengthened, guiding its evolution into a robust tool for both symbolic AI reasoning and intelligent code analysis. The expected result is a CogniGraph system that is well-tested, performance-tuned, and ready for deployment in scenarios where structured knowledge is key – from powering neuro-symbolic AGI components to assisting developers with intelligent code retrieval. All necessary adjustments to the codebase (parsers, interfaces, optimizations, and test harnesses) will be implemented as part of this plan to achieve the desired outcomes. Each improvement will be backed by measurable evidence recorded in the repository, ensuring that CogniGraph’s development remains grounded in empirical performance data.