2. Inability of existing KR systems to ergonomically conform to human patterns of learning and reasoning. If so, this might be due to a lack of sufficient understanding how to transition between informal natural language based reasoning and formalized reasoning, or it may simply be that the chosen formalisms are not the best ones for empowering human thought.
I haven’t studied knowledge representation much, but my passing impression is that this is the main problem. I suspect that KR people tried too hard to make their structures look like natural language, when in fact the underlying structures of human thought not are not particularly language-shaped.
Central example driving my intuition here: causal graphs/Bayes nets. These seem to basically-correctly capture human intuition about causality. Once you know the language of causal graphs, it’s really easy to translate intuition about causality into the graphical language—indicating a “knowledge representation” which lines up quite well with human reasoning. And sure enough, causal graphs have been pretty widely adopted.
On the other hand, somewhat ironically, things like concept graphs and semantic networks do a pretty crappy job of capturing concepts and the semantics of words. Try to glean the meaning of “cat” from a semantic graph, and you’ll learn that it has a “tail”, and “whiskers”, is a “mammal”, and so forth. Of course, we don’t really know what any of those words mean either—just a big network of links to other strings. It would be a great tool for making a fancy Markov language model, but it’s not great for actually capturing human knowledge.
Interesting, can you give some examples to illustrate how causal/Bayes nets are used to aid reasoning / discovery?
I see merit in the idea that semantic networks may focus too much on the structure of language, and not enough on the structure of the underlying domain being modelled. As active thinkers, we are looking to build an understanding of the domain, not an understanding of how we talked about that domain.
Issues of language use, such as avoiding ambiguity, could sometimes be useful especially in more abstract argumentation, but more important is being able to track all of the relationships among the domain specific entities and organizing lines of evidence.
I haven’t studied knowledge representation much, but my passing impression is that this is the main problem. I suspect that KR people tried too hard to make their structures look like natural language, when in fact the underlying structures of human thought not are not particularly language-shaped.
Central example driving my intuition here: causal graphs/Bayes nets. These seem to basically-correctly capture human intuition about causality. Once you know the language of causal graphs, it’s really easy to translate intuition about causality into the graphical language—indicating a “knowledge representation” which lines up quite well with human reasoning. And sure enough, causal graphs have been pretty widely adopted.
On the other hand, somewhat ironically, things like concept graphs and semantic networks do a pretty crappy job of capturing concepts and the semantics of words. Try to glean the meaning of “cat” from a semantic graph, and you’ll learn that it has a “tail”, and “whiskers”, is a “mammal”, and so forth. Of course, we don’t really know what any of those words mean either—just a big network of links to other strings. It would be a great tool for making a fancy Markov language model, but it’s not great for actually capturing human knowledge.
Interesting, can you give some examples to illustrate how causal/Bayes nets are used to aid reasoning / discovery?
I see merit in the idea that semantic networks may focus too much on the structure of language, and not enough on the structure of the underlying domain being modelled. As active thinkers, we are looking to build an understanding of the domain, not an understanding of how we talked about that domain.
Issues of language use, such as avoiding ambiguity, could sometimes be useful especially in more abstract argumentation, but more important is being able to track all of the relationships among the domain specific entities and organizing lines of evidence.