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

76 Chapter 4. Acquisition of Semantic Relations
Looking at the agreement numbers, we notice that there is good (Green, 1997)
or substantial agreement (Landis and Koch, 1977) in the classification of synonymy,
hypernymy and causation relations. On the other hand, the relations with less
classification agreement (fair and moderate) are also those less semantically welldefined.
We have already mentioned the problem of judging member-of triples. We
actually present two values for their agreement – the first value considers the four
possible classifications, while the second considers member-of as generic meronymy,
which means that the triples classified as 3 are considered as if they had been
classified as 2. This way, agreement is higher, but still lower than for synonymy,
hypernymy and causation. Another source of noise for the member-of relation is
that, sometimes, it can be overlapping with the hypernymy. For instance, bear is a
hyponym of mammal, but is it also a member-of of the class of mammals?
We have also mentioned the abstraction problem of the property-of relation
and the underspecification problem that occurs especially for property-of and for
purpose-of. Another problem that contributes to less agreement on the classification
of purpose-of relations is related with the relaxed semantic constraints of
its arguments. This relation may connect very different things. Just to give an
idea, it relates an action (verb), which can either be a general purpose (e.g. to fry,
to desinfect, to calculate, to censor, to dissociate) or just something one can do with
(e.g. to punish, to transport, to climb, to spend, to entertain), for instance, an instrument
(e.g. frying pan, desinfectant, whip), a concrete object (van, stairs), an
abstract means (e.g. credit, calculation, satire), a human entity (e.g. clown), or a
property (e.g. dissociation).
We conclude by referring that these results are, to some extent, comparable,
though higher, to those obtained in the creation of MindNet (Richardson et al.,
1993), where a sample of 250 relations of 25 different types, extracted from the
Longman Dictionary of Contemporary English, were hand-checked for correction.
The overall reported accuracy was 78%. It is also referred that the highest accuracy,
of about 87%, was obtained for hypernymy, and part-of was the less reliable relation,
only 15% accurate.
4.3 Discussion
We have presented the first step towards the automatic creation of a wordnetlike
lexical ontology for Portuguese. After explaining how semantic relations are
acquired from dictionaries, we described the creation of CARTÃO, the successor of
PAPEL and thus the largest term-based lexical-semantic network for Portuguese.
CARTÃO can be browsed using the interface Folheador (Gonçalo Oliveira et al.,
2012b; Costa, 2011), designed for facilitating the navigation on Portuguese LKBs
represented as tb-triples. Folheador is connected to the online services VARRA (Freitas
et al., 2012) and AC/DC (Santos and Bick, 2000; Santos, 2011) that query corpora
to provide authentic examples of the relations in context. VARRA is designed
not only to search for tb-triples in context, but also to discover new discriminating
patterns for each relation, and to identify good and bad examples of each tb-triple.
The examples might be useful to understand and to evaluate the triple. An exercise
using VARRA to validate part of PAPEL 2.0 is described in Freitas et al. (2012).