LIO Step 7 – Temporal Analysis

To capture the dynamic nature of research and disease progression, the Step 4 Agent integrates the dimension of time into the Matrix of Meaning. Understanding when information was generated or when events occurred is crucial for interpreting findings and identifying trends within the CF domain.

Purpose: To timestamp information and events accurately, enabling analysis of evolution, progression, and temporal relationships within the dataset. This allows users to ask questions like, “How has the sentiment towards gene therapy evolved since 2020?” or “Map the timeline of publications related to the 2184insA mutation.”

Methodology & Scope: The Agent analyzes the data package to identify and normalize temporal information by:

• Extracting Document Timestamps: Utilizing publication dates, report dates, or other metadata associated with the source documents ingested into the system.

• Recognizing Temporal Expressions: Employing Natural Language Understanding (NLU) models, potentially adhering to standards like TIMEX3, to identify and interpret explicit time expressions within the text itself (e.g., “In 2023,” “diagnosed five years ago,” “following the 2018 conference,” “previously reported”).

• Normalization: Converting extracted dates and times into a standardized format (e.g., ISO 8601) to allow for consistent comparison and ordering.

• Associating Time with Data: Linking the normalized temporal information to the specific text segments, entities, extracted relationships, classifications, or sentiment scores it pertains to. An event described in the text would be associated with the time it reportedly occurred, while the statement itself would be associated with the document’s publication date.

• Integration into the Matrix: Temporal data is woven into the matrix structure as timestamps or temporal attributes associated with relevant nodes (e.g., text segments, entities, events) and edges (e.g., relationships occurring within a specific timeframe). This could involve:

  • Adding creationDate, publicationDate, eventStartDate, eventEndDate properties to data objects.
  • Creating explicit ‘Time’ nodes or a temporal axis within the matrix structure, allowing data points to be plotted or queried based on their position in time. This temporal dimension is essential for understanding trends, causality (as events precede effects), and the historical context of findings within the CF research landscape.