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If you want to play with something similar, including the sourcecode, check out conceptnet: http://conceptnet5.media.mit.edu/ http://conceptnet5.media.mit.edu/

If you want to play with something similar, including the sourcecode, check out conceptnet: http://conceptnet5.media.mit.edu/ http://conceptnet5.media.mit.edu/ If you want to get an interesting introduction to the idea of concept graphs take a look here: http://xnet.media.mit.edu/docs/conceptnet/install.html http://xnet.media.mit.edu/docs/conceptnet/install.html This works with conceptnet4, but I think it's a nice simple introduction that you can run easily. Conceptnet5 is very different from conceptnet4, but the examples for conceptnet4 are a good intro. I was never very successful with using conceptnet5. Conceptnet data is considered noisy, ambiguous, redundant, etc. by researchers, but I think that describes our own knowledge as well. The problem that we found with conceptnet is that there's just too much knowledge in it and so relationships between concepts were washed out. It's almost like talking to an extremely knowledgable but boring person with no opinions - we were trying it out to look at relationships between concepts and emotions. By relationship between an emotion and a concept, I mean the distance in the graph between the concept of the emotion and another concept. For example, 'cake' is closer to happiness in the graph than 'rain'. And related to this, you get contradictory relationships between concepts. For example, some people from some cultures will have positive emotions close to 'dog', but other cultures have negative emotions associated with 'dog'. By trying to fill a concept graph with all information it can find (wikipedia, news articles, web crawling, etc) you just get a big mess. Another problem with conceptnet 4 is that the data is culturally limited - for example it's obvious that it was filled by MIT students when you search for concepts related to happiness and the concept of getting a good grade on a test is higher than the happiness of kissing a girl. What would be useful is to develop personal conceptnets by analyzing all communication of that person. If you can include voice communication you can get even better emotional data than you would from just analyzing text. Even better would be to keep a time series of personal conceptnets and blend them over time, and from that you can analyze what external factors changed a person's emotions/concepts. You could even do experiments - apply some external factor and see how they change.

jkldotio 12y ago
I've been playing around with ConceptNet for a year now, it's extremely impressive but I haven't quite applied it to a domain yet. I often wonder about the cultural aspect you mention as I worked with a group that was attempting to model space, time and opinion data: i.e. 'dog isA food' differs according to geographic location. The old joke goes: scientists create a super intelligent computer and ask it 'is there a God?', and the computer replies 'there is now'. If future AIs are trained primarily on the English speaking Internet then perhaps God really will be an Englishman. For that to really be the case though the computer's reply would have to actually be a joke. Perhaps it's time, given people are claiming they've passed the Turing test on dubious grounds for media attention, that a new test is established based on whether the computer can tell a funny joke. Higher level comedy plays with meaning and context, thus to make a joke you have to display mastery over the possible meanings as well as building a mental model of how your interlocutor will process it. Thus a being that can tell a joke is displaying a distinct sophistication. Many humans might fail that one though.

jkldotio 12y ago

I've been playing around with ConceptNet for a year now, it's extremely impressive but I haven't quite applied it to a domain yet. I often wonder about the cultural aspect you mention as I worked with a group that was attempting to model space, time and opinion data: i.e. 'dog isA food' differs according to geographic location. The old joke goes: scientists create a super intelligent computer and ask it 'is there a God?', and the computer replies 'there is now'. If future AIs are trained primarily on the English speaking Internet then perhaps God really will be an Englishman. For that to really be the case though the computer's reply would have to actually be a joke. Perhaps it's time, given people are claiming they've passed the Turing test on dubious grounds for media attention, that a new test is established based on whether the computer can tell a funny joke. Higher level comedy plays with meaning and context, thus to make a joke you have to display mastery over the possible meanings as well as building a mental model of how your interlocutor will process it. Thus a being that can tell a joke is displaying a distinct sophistication. Many humans might fail that one though.