Pat Hayes, a famous AI research started off today's conference day. His keynote, while somewhat entertaining and somewhat insightful was extremely scattered and altogether gave the impression that he had prepared it the night before (which indeed he had). He talked about his "9 deadly sins of AI". These are as follows (and yes, I know there are only four):
Not wanting to accept that the ship has sunk: some researchers still hang on to trying to make techniques and ideas work that where bad ideas when they were first invented and have caused no end of trouble since.
Worshipping philosophy (or, for that matter, worshipping anything): philosophy is useful, but it is a different field to knowledge representation. Just because something is important in philosophy doesn't mean that we have to pay any attention to it in KR.
Taking paradoxes too seriously: A logical paradox is just a humorous distraction for a Sunday night. Just because Kurt G??del's incompleteness theorem shook the very foundations of logic and mathematics, doesn't mean that a paradox is something we have to worry about in practical system. Yeah, so OWL-full allows for paradoxes. Just don't create them and stop complaining about it.
Worshipping logic: (first-order) Logic is attractively simple. Everything in the world can be expressed using AND, NOT and FOR-ALL. However, this is too much of an abstraction from real useful things. It requires too large a framework of axioms on top of it to make it do something useful. We should push more expressivity into the logic layer, thereby bringing it closer to the ontology layer.
Other topics of today:
Nokia and Airbus are working together to shorten their product development feedback cycle. They want to create more mature (useable, useful and acceptable) products more quickly. They aim to achieve this using a system of active documentation. Documentation not just for the sake of it, but in order to involve all project stakeholders in the design, prototype, evaluation and requirements capture processes.
Harith Alani uses four measures for ranking ontologies returned from an ontology search engine:
- Class match: the degree to which the searched for terms are present in the ontology
- Centrality: how close the search terms are to the middle of the is-A hierarchy
- Density: how much information context there is on the search terms (restrictions, etc)
- Semantic similarity: how many links need to be followed from one search term in order to reach another
Harith also mentioned that there is a graph query API called JUNG. I'll have to check this out for my work.