Onto.PT: Towards the Automatic Construction of a Lexical Ontology ...

144 Chapter 8. Onto.PT: a lexical ontology for Portuguese
To give an idea on the quality of each relation type, table 8.6 presents the results
of the evaluation of the 300 non-hypernymy tb-triples. The number of evaluated
sb-triples is not enough to take strong conclusions, but the quantity of dizSeDoQue
correct tb-triples stands out. It is higher than the extraction accuracy of these
relations (adj property-of v, 71-77% correct in section 4.2.5) from dictionaries. Given
that most of the problems about these relations were due to incorrect arguments, we
view this improvement as a consequence both of the new lemmatisation rules, and
of the removal of tb-triples with arguments not occurring in the corpus, performed
before ontologisation. On the other hand, the quality of the parteDe sb-triples is
quite low. Similarly to what happens with hypernymy, the main problem about this
relation seems to be the ambiguity and underspecification of its arguments. This
has a negative impact both on the establishment of correct synsets with these words
and on the ontologisation of tb-triples.
8.3.3 Global coverage
If compared to the number of sb-triples in Princeton WordNet 3.0 (see section 3.1.1),
developed manually between 1985 and 2006, Onto.PT v.0.35 is larger because all
of its relations can be inverted. This means that Onto.PT contains about 346,000
sb-triples against the 285,000 of WordNet 3.0. This number, which may soon increase,
if more resources are exploited, highlights the potential of an automatic
approach.
As this number is insufficient to quantify the coverage of Onto.PT, we evaluated
its coverage of base concepts, that should be represented in wordnets. The Global
WordNet Association 5 provides several lists with this kind of concepts. One of them
contains 164 base concepts, referred to as the “most important” in the wordnets
of English, Spanish, Dutch and Italian 6 . The concepts are divided into 98 abstract
and 66 concrete, and are represented as Princeton WordNet 1.5 synsets.
In order to evaluate the global coverage of Onto.PT, we tried to make rough
matches, manually, between the 164 base concepts and Onto.PT synsets. Given the
WordNet synset denoting each of the 164 concepts, we selected the Onto.PT synset
closer to its meaning. In the end, we concluded that Onto.PT roughly covers most
of the concepts in the list, more precisely 92 abstract and 61 concrete synsets (93%).
All the defined matches are reported in the appendix B of this thesis. More
precisely, the concrete concepts are in table B.1, and the abstract in tables B.2.
There, we can see that the Onto.PT synsets are, on average, larger than WordNet’s,
which means, on the one hand, that they are very rich, with many synonyms –
most synsets include various levels of language (formal, informal, figurative, older
forms...) and variants of Portuguese (Portugal, Brazil, Africa). This can be very
useful for tasks from information retrieval (see section 8.4.3) to creative writing (e.g.
poetry). On the other hand, the matches show that there are synsets that go beyond
including only synonyms – most noisy items are more like near-synonyms and some
are closely related words.
Considering just the abstract concepts not covered by Onto.PT (e.g. change
magnitude, definite quantity, visual property), they seem to have been created ar-
5 See website at http://www.globalwordnet.org/ (September 2012)
6 See more about this list in http://www.globalwordnet.org/gwa/ewn_to_bc/corebcs.html
(September 2012)