data-mapping-model/explainability.json

Here's a breakdown of how to generate output in a machine-parsable format suitable for downstream reasoning systems, along with the canonical record generation:

**1. Understanding the Goal: Machine Comprehension & Reasoning**

The overall goal is to provide information that a computer can understand and use to perform tasks like answering questions, generating new knowledge, or making decisions. This requires:

*   **Structured Representation:**  Data must be organized in a predictable and consistent manner (e.g., JSON, XML, RDF).
*   **Explicit Relationships:** Relationships between entities and concepts must be clearly defined (e.g., "agent performs action on object").
*   **Reasoning Steps:**  Each logical inference or deduction that leads to the final conclusion must be documented.

**2. Steps to Generate Machine-Parsable Output**

Here's a general process, adaptable based on the input type (e.g., text, sensor data):

**a. Input Analysis & Preprocessing:**

*   **Understanding the Input:** Identify the type of input (e.g., text, sensor data).  Analyze its format and content.
*   **Tokenization & Normalization (if applicable):** Break down text into tokens (words, numbers, symbols). Normalize the tokens (e.g., lowercase).  Handle punctuation and other special characters.
*   **Named Entity Recognition (NER) (if applicable):** Identify and classify entities in the input (e.g., people, organizations, locations, dates). Tag them.
*   **Relationship Extraction (if applicable):** Identify relationships between entities (e.g., "John works for IBM"). Tag them.
*   **Event Extraction (if applicable):** Identify events and their participants (e.g., "The car crashed"). Tag them.
*   **Semantic Role Labeling (SRL) (if applicable):** Label roles of event participants (e.g., agent, patient, instrument, location).

**b. Reasoning & Inference:**

*   **Knowledge Base (if applicable):**  Use a knowledge base (KB) or external data source to provide background information, domain knowledge, and inference rules.
*   **Reasoning Engine:** Employ a reasoning engine (e.g., rule-based inference, logical deduction, probabilistic inference) to derive new knowledge or conclusions based on the input and KB (if used).
*   **Trace Generation:**  Document each step of the reasoning process. This will form the "reasoning steps" key in the output.

**c. Canonical Record Generation:**

*   **Entity Resolution (if applicable):** Resolve multiple mentions of the same entity to a single canonical representation.
*   **Canonicalization:**  Standardize the representation of entities, relationships, and events. This might involve mapping synonyms to canonical terms, converting units, or applying other normalizations.
*   **Record Creation:**  Generate a structured representation (e.g., JSON) that captures the canonicalized information. This will form the "canonical record" key in the output.

**3. Output Format: JSON with Reasoning Steps & Canonical Record**

Generate a JSON object with the following keys:

*   **reasoning steps:** A list of dictionaries. Each dictionary represents a reasoning step. Each dictionary has keys like:
    *   **step_number:** The numerical order of the step.
    *   **description:** A natural language description of the step (e.g., "Applying rule X to deduce Y").
    *   **input:** The input to the reasoning step (e.g., facts, observations).
    *   **output:** The output of the reasoning step (e.g., new facts, conclusions).
    *   **rule:** (Optional) The rule applied in the step.
*   **canonical record:** A structured representation of the canonical information. The structure depends on the input type and the task. General considerations:
    *   **Entities:**  Represent entities with unique identifiers (UUIDs recommended). Include attributes like name, type, and other relevant details.
    *   **Relationships:**  Represent relationships between entities using predicates (e.g., "works_for," "located_in"). Include attributes like start date, end date, etc.
    *   **Events:**  Represent events with unique identifiers (UUIDs recommended). Include attributes like event type, participants (linked entities), location, time, and other relevant details.

**4. Example: Text Input - "John works for IBM in New York."**

**a. Input Analysis & Preprocessing:**

*   Tokenization: John, works, for, IBM, in, New, York
*   NER: John (PERSON), IBM (ORGANIZATION), New York (LOCATION)
*   Relationship Extraction: John works for IBM; IBM located in New York.

**b. Reasoning & Inference:**

*   Knowledge Base (Hypothetical): Contains general knowledge about employment, organizational locations, etc.
*   Reasoning: Can derive the following:
    *   John's employer is IBM.
    *   IBM's location is New York.

**c. Canonical Record Generation:**

*   Entity Resolution: Create UUIDs for John and IBM.
*   Canonicalization: Ensure standardized names (e.g., "John Doe").
*   Record Creation:

    {
        "reasoning steps": [
            {"step_number": 1, "description": "Tokenized input.", "input": "John works for IBM in New York.", "output": null, "rule": null},
            {"step_number": 2, "description": "Performed Named Entity Recognition (NER).", "input": "John works for IBM in New York.", "output": {"PERSON": "John", "ORGANIZATION": "IBM", "LOCATION": "New York"}, "rule": null},
            {"step_number": 3, "description": "Extracted relationship: John works for IBM.", "input": {"PERSON": "John", "ORGANIZATION": "IBM"}, "output": null, "rule": null},
            {"step_number": 4, "description": "Extracted relationship: IBM located in New York.", "input": {"ORGANIZATION": "IBM", "LOCATION": "New York"}, "output": null, "rule": null},
            {"step_number": 5, "description": "Inferred John's employer is IBM and IBM's location is New York.", "input": null, "output": null, "rule": null}
        ],
        "canonical record": {
            "entities": [
                {"uuid": "GENERATED-UUID-PERSON", "name": "John Doe", "type": "PERSON"},
                {"uuid": "GENERATED-UUID-ORGANIZATION", "name": "IBM", "type": "ORGANIZATION", "location": {"uuid": "GENERATED-UUID-LOCATION", "name": "New York", "type": "LOCATION"}}
            ],
            "relationships": [
                {"uuid": "GENERATED-UUID-RELATIONSHIP", "type": "works_for", "entity1": "GENERATED-UUID-PERSON", "entity2": "GENERATED-UUID-ORGANIZATION"}
            ],
            "events": []
        }
    }

**5. Considerations & Enhancements:**

*   **Knowledge Base (KB):**  Use a formal KB (e.g., Wikidata, ConceptNet) to improve reasoning capabilities and canonicalization.
*   **Disambiguation:** Handle ambiguity in the input (e.g., multiple John Does).
*   **Uncertainty:**  Represent uncertainty using probabilities or confidence scores.
*   **Domain-Specific Knowledge:** Incorporate domain-specific knowledge (e.g., medical knowledge) for more specialized reasoning.
*   **Feedback:** Provide feedback to the user about the reasoning process and the final conclusion.
*   **Evaluation:** Evaluate the system's performance using appropriate metrics (e.g., accuracy, precision, recall).
*   **Scalability:** Design the system to handle large volumes of data.
*   **User Interface:** Create a user interface to visualize the reasoning process and the canonical record.
*   **Standard Terminologies:**  Employ standardized terminologies (e.g., SNOMED CT, UMLS) to enhance interoperability.
*   **Automatic Rule Generation:**  Explore techniques for automatically generating inference rules from data.