The program implements a toy context-free grammar (CFG) parser, that extracts knowledge (facts) from text. This program was developed as part of an assignment, for the undergraduate NLP course in CS at University of Piraeus. The parser understands sentences that follow some simple grammar rules for the English language. Also the vocabulary used in the text must be defined in advanced in the knowledge base.
Resources: [1], [2]
You can add facts to the knowledge base:
?- say('vaggelis runs fast').
The fact runs(vaggelis,fast) was added to the KB.
true .
?- say('chris gave a program to themis').
The fact gave(chris,program,themis) was added to the KB.
true .
And then ask questions:
?- ask('who runs fast?').
Answer: vaggelis
true .
?- ask('how does vaggelis run?').
Answer: fast
true .
?- ask('what did chris give to themis?').
Answer: program
true .
?- ask('who gave a program to themis?').
Answer: chris
true .
Lets say that we import the following sentence:
?- say('nicky thinks that the man in the street is drunk').
The fact thinks(nicky,drunk(man)) was added to the KB.
true .
Now we can ask the following questions:
?- ask('who thinks of the drunk man').
Answer: nicky
true .
?- ask('who thinks that the man is drunk?').
Answer: nicky
true .
?- ask('what is nicky thinking of?').
Answer: drunk(man)
true .
?- ask('what was nicky thinking of?').
false.
Notice that at the last question we got a negative answer. This is because we asked the system what nicky was thinking, but the fact we imported specifies only what nicky is thinking right now.
However, notice what happens when we ask the following sentence:
?- say('nicky thought that the man in the street was drunk').
The fact thought(nicky,drunk(man)) was added to the KB.
true .
?- ask('what was nicky thinking of?').
Answer: drunk(man)
true .
You can enable verbose output with the verbose predicate, which prints the parsed structure of the sentence and the parse tree.
?- verbose.
verbose ON
true.
?- say('nicky thinks that the man in the street is drunk').
The fact thinks(nicky,drunk(man)) was added to the KB.
Structure = s(s(np(pn(nicky)),vp(iv(thinks))),conj(that),s(np(det(the),nbar(n(man)),pp(prep(in),np(det(the),nbar(n(street))))),vp(av(is),adj(drunk))))
-s
|-s
| |-np
| | |-pn
| | | |-nicky
| |-vp
| | |-iv
| | | |-thinks
|-conj
| |-that
|-s
| |-np
| | |-det
| | | |-the
| | |-nbar
| | | |-n
| | | | |-man
| | |-pp
| | | |-prep
| | | | |-in
| | | |-np
| | | | |-det
| | | | | |-the
| | | | |-nbar
| | | | | |-n
| | | | | | |-street
| |-vp
| | |-av
| | | |-is
| | |-adj
| | | |-drunk
true .
You can also import fact from a text file. The file must have one sentence per line.
?- readfile('Haste makes Waste.txt').
Importing facts... :
had(woman,mongoose)
had(woman,baby)
faithful(mongoose)
went(woman,market)
left(woman,baby,mongoose)
entered(big(cobra),house)
fought(mongoose,cobra)
killed(mongoose,cobra)
returned(woman,house)
laid(mongoose,entrance)
saw(woman,mongoose)
had(mongoose,bloody(mouth))
noticed(woman,had(mongoose,bloody(mouth)))
hasteful(woman)
thought(woman,killed(mongoose,baby))
became(woman,furious)
threw(woman,waterpot,mongoose)
killed(woman,mongoose)
entered(woman,house)
filled(woman,remorse)
alive(baby)
played(baby,cheerfully)
laid(big(cobra),nearby)
dead(big(cobra))
cried(woman)
true .
[1]: Striegnitz, Kristina, et al. "Algorithms for Computational Linguistics." (2003).
[2]: Blackburn, Patrick, Johannes Bos, and Kristina Striegnitz. Learn prolog now!. Vol. 7. No. 7. London: College Publications, 2006.
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