Handle Tables Carefully in a Knowledge Pipeline

A practical approach

Step 1

Inspect tables from representative source formats. Identify merged headers, repeated headings, units and notes that affect interpretation.

Step 2

Represent rows with their associated labels and column names. Keep the table title and source location so a result can be checked in context.

Step 3

Test questions that require the correct row and column, plus questions involving exceptions or missing values. Treat a blank cell differently from zero.

Worked example

A training matrix lists beginner and advanced prerequisites across several programmes. A flattened extract might place ‘none’ beside the wrong programme. A row-oriented representation such as ‘Programme: API foundations; prerequisite: basic programming’ makes the relationship more explicit and easier to validate.

A mistake to avoid

OCR errors may turn similar-looking characters into different identifiers or values. Do not assume a parser’s successful exit means semantic correctness. For consequential numeric answers, use structured data and deterministic queries where possible.

Try it yourself

Choose a table with at least three columns and a footnote. Parse it, reconstruct five facts from the output and compare them with the original. Record any relationship that was lost.

Your implementation checklist

• Cells keep row and column meaning.
• Units and footnotes remain attached.
• Blank values are not converted to zero.
• Parsed output is compared with the original table.