Before going into implementation details, here’s an example of a session:

12:41 < ww> biblio forget
12:41 < biblio> ww: ok
12:41 < ww> Solzhenitsyn's name is "Aleksander Isayevitch Solzhenitsyn"
12:42 < ww> He was born on December 11th 1918
12:42 < ww> He died on August 3rd 2008
12:42 < ww> He wrote TFC in 1968
12:42 < ww> TFC's title is "The First Circle"
12:42 < ww> "YMCA"'s name is "YMCA Press"
12:42 < ww> They published TFC in 1978
12:42 < ww> biblio think
12:42 < biblio> ww: I learned 25 things in 0:00:00.218296
12:42 < ww> biblio paste
12:42 < biblio> ww: http://pastebin.ca/1913826

Broadly, the process follows the following steps:

• (NLTK) Tokenise the sentence and classify for parts of speech
• Create references for named entities (capitalised words, URIs and phrases enclosed in double quotes)
• (NLTK) Create a lexicon, the part of a grammar that grounds it to individual words and append it to the canned grammar that describes the structure of sentences. This is a feature grammar not a context-free grammar
• (NLTK) Parse the input sentences creating a syntax tree with the root at the main verb in the sentence
• The syntax tree is annotated with the logical structure of the sentence (see Analysing the meaning of sentences). This logical representation is cunningly constructed so as to also be runnable Python code (with eval). Running it transforms the syntax tree into an RDF representation.
• (FuXi) the “biblio think” command causes the RDF of the current session to be run through a number of inference rules that encode higher level meaning. That if “X wrote Y” then X must be a person, Y must be a work and X must have contributed to Y.

statement(
  predicate(
    bnode(
      rdf_type(umbel("Verb")),
      label("is"),
      racine("be"),
      tense(nlp("Present")) 
    ), 
    named("aHLIkuXm14335") # "The First Circle" 
  ), 
  posessive(
    bnode(
      rdf_type(umbel("Noun")), label("title"), racine("title")
    ), 
   named("aHLIkuXm14333") # "TFC"
  ) 
)

 entity:aHLIkuXm14333 a nlp:NamedEntity;
      rdfs:label "TFC".

 entity:aHLIkuXm14335 a nlp:NamedEntity;
      rdfs:label "The First Circle".

 [ a umbel:Verb;
      rdfs:label "is";
      lvo:nearlySameAs lve:be;
      nlp:directObject entity:aHLIkuXm14335;
      nlp:subject [ a umbel:Noun;
                    rdfs:label "title";
                    lvo:nearlySameAs lve:title;
                    nlp:owner entity:aHLIkuXm14333];
      nlp:tense nlp:Present].

I suppose since IRC is not necessarily the most accessible of media — though I can’t really see why — the same engine could be easily glued to a web server with a simple chat-like interface. Perhaps this is easier or more natural than web forms. Perhaps not. More research is needed.

In any case, I’m working on improving the natural language parsing and the inference rules as time permits so hopefully the robot will become more and more clever.

Source code for the IRC bot is available at: http://bitbucket.org/ww/sembot

You can play with a live version of the bot by joining irc://irc.oftc.net/ and joining #okfn or engaging in a private chat with biblio. It understands the command “sembot help” and I’ll try not to break it too badly while anyone’s playing with it.