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

120 Chapter 7. Moving from term-based to synset-based relations
Brazilian Portuguese and thus contains some unusual words or meanings for European
Portuguese. On the other hand, OT.PT is smaller, but made for European
Portuguese, and contains words and meanings not covered by TeP.
Therefore, we used TeP as a starting point for the creation of a new noun thesaurus
3 , TePOT, with the noun synsets from both TeP and OT.PT. The thesauri
are merged according to the following automatic procedure:
1. The overlap between each synset in OT.PT, Oi, and each synset of TeP,
Tj, is measured. For each Oi ∈ OT.PT a first set of candidates,
Ci = {Ci1, Ci2, ..., Cin} ⊂ TeP, will contain the TeP synsets that maximise the
Overlap measure, Overlap(Oi, Cik) = max(Overlap(Oi, Tj)):
Overlap(Oi, Tj) =
Oi ∩ Tj
min(|Oi|, |Tj|)
If max(Overlap(Oi, Tj)) = 0, it means that the OT.PT synset contains only
words that are not in TeP, and is thus added to TePOT as it is.
2. Otherwise, the candidate(s) in Ci with higher Jaccard coefficient are selected,
Cil ∈ C ′ i → Jaccard(Oi, Cil) = max(Jaccard(Oi, Cik)):
Jaccard(Oi, Cik) = Oi ∩ Cik
Oi ∪ Cik
Usually, C ′ i has just one synset but, if it has more than one, they are merged in
the same synset. Then, the new synset is merged with Oi. A new TePOT synset
Si will contain all words in Oi and in the synsets in C ′ i . Si = {w1, w2, ..., wm} :
∀(wj ∈ Si) → wj ∈ Oi ∨ wj ∈ Cil, Cil ∈ C ′ i .
3. Synsets of TeP which have not been merged with any OT.PT synset are finally
added to TePOT without any change.
In the end, TePOT contains 18,501 nouns, organised in 8,293 synsets – 6,237 of
the nouns are ambiguous and, on average, one synset has 3.84 terms and one term
is in 1.72 synsets.
Tb-triples
The algorithms were evaluated for ontologising tb-triples of three different types:
hypernymy, part-of and purpose-of, all held between nouns. The tb-triples used
were obtained from PAPEL 2.0, which was, at the time when we started to create
the gold reference, the most recent version of PAPEL. As a resource extracted automatically
from dictionaries, the reliability of PAPEL is not 100% (see section 4.2.5
for evaluation details), but it was the largest lexical-semantic resource of this kind
freely available. In order to minimise the noise of using incorrect tb-triples, we
added additional constraints to their selection, namely:
3 We only used nouns because the reported experimentations only dealt with semantic relations
between nouns, namely hypernymy, part-of, and purpose-of.