That is a lie!

The heart of semantics is truth, the ability to examine a proposition and determine whether it is true or false. Sometimes we may not have enough information to determine whether a given statement or network of statements is true, but sometimes claims may simply not be true in an objective sense. False claims may be inintentional or intentional. Regardless, any semantic system or semantic agent needs to be able to make judgments as to the truth of statements and propositions.

Some of the ways in which even simple statements can be false are:

• Outright lies
• Deceptions that hide behind some legalism
• Misleading by artful presentation of mostly truthful information
• Honest mistakes
• Simple misstatements
• Subjective truth
• Misunderstandings
• Confusion
• Misinterpretation
• Incomplete information
• Fuzzy statistical data
• Changed information
• Different points of view
• Wishful thinking
• Conjecture and speculation based on a weak foundation
• Semantic mismatches, contextual mismatches - true in one system of reasoning, but not necessarily true in a different system of reasoning
• Jokes and pranks
• Hoaxes
• Fraud
• Madness of crowds
• Emperor's New Clothes syndrome
• Folklore
• Political dogma
• Exaggeration
• Paradoxes
• Poor estimation
• Works of fiction
• Dramatization
• Hypotheticals
• News reports - it may have been "said", or reported to have been said, but is it true?

Semantic data mining in particular needs to be able to classify statements as to their truth content, not simply whether a statement is believed to be true, but what form of untruth it might be.

Semantic agents need to be able to validate the veracity of claims that it encounters.

How to do all of this? Overall, unknown at the present time, but there of lots of special cases and plenty of room for heuristics.

Maybe even a heuristic could be considered a "lie" to some extent.

-- Jack Krupansky