1 Wordnet gets many hits from users worldwide.

2 WordNet: The actual lexical database files.

3 WordNet is a very large project based on data whose basic underpinnings are very rigorously defined.

4 I think that most applications of WordNet in semantic transparency involve working with pointers.

5 I showed in my earlier article how WordNet can be used to add much more intelligence to even application-specific searches.

6 Indeed, the WordNet model says that "wisteria" is a hyponym of "vine," and "vine" is a hyponym of "traceophyte."

7 If you have any thoughts on WordNet usage, or experience of your own, please share by posting on the Thinking XML discussion forum.

8 There are many WordNet projects languishing with older versions of the data, in many cases because they haven't gotten around to the database format conversion.

9 This is a matter of querying the WordNet server starting from the search word, and following pointers to related words, and then continuing recursively.

10 PyWordNet does not come with the WordNet database files. These you must download separately.

11 If one schema has an anchor to "name" and the other to "identifier", a machine can navigate WordNet automatically to recognize the lexical similarity of those terms.

12 This code is different from Listing 3 in that it pulls the wnssid (the WordNet synsetid) only.

13 I assembled an RDF representation such as that in Listing 2 from all the synsets in WordNet and performed a similar monolithic query of the resulting database.

14 My task is now to use this to create a tool for serializing WordNet into XML upon request.

15 It allows you to get raw synset XML from URLs based on WordNet pointers, for example http://localhost:8080/raw/pointer/noun/5955443.

16 A machine can use equivalence of the URLs to check semantic equivalence -- and WordNet allows you to go even further, using its thesaurus-like facilities for richer semantics.

17 This problem requires one refinement over the code presented in my last installment -- specifically, I need the WordNet server to return raw XML representing synsets, and not just full word forms.

18 This wraps up my three-part discussion of how to use the WordNet natural language semantic database in XML and RDF applications.

19 I frame pointers between synsets using relationships with the same name as the WordNet pointers (hypernym, frames, etc.).

20 Next I'll use the bound reasoner to create an InfModel from the WordNet model.

21 Actually, they are the index numbers returned by my query to my local copy of the WordNet database.

22 Here, I build on the basic code to extract the lexical information from WordNet in XML form.

23 The next step is to bind the reasoner to the WordNet ontology.

24 The attributes part-of-speech and target provide all the information you need to uniquely identify a synset in the entire WordNet corpus.

25 I'm actually going to use a subset of the WordNet model here, containing only those nouns beneath "plant life" in the hyponym hierarchy.

26 To create an RDF/XML format, you can find all the necessary information content for WordNet in the XML representation discussed in the first article of this mini-series.

27 This will help make the middleware code clearer and will provide the performance needed to incorporate huge models such as the WordNet model demonstrated a few installments ago.

28 One of the imports is from wnxmllib.py, which is basically the code I presented in the last column on WordNet but bundled into one file and updated a bit to use a simpler 4Suite API.