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

3.2. Lexical-Semantic Information Extraction 51
pmi(i, p) = log
The reliability of instance i, rl(i), is given by:
rl(i) =
3. Select the most reliable patterns.
p∈P ′
|x, p, y|
|x, ∗, y||∗, p, ∗|
pmi(i,p)
maxpmi
|P |
∗ rπ(p)
4. Extract new instances after applying the selected patterns to the corpus. The
most reliable instances are added to the seed set.
Ittoo and Bouma (2010) present a study on the acquisition of part-whole relations.
Using an algorithm inspired by Espresso, they notice that special attention
should be given when choosing the seed relations. Given that there are different
subtypes of part-whole relations (e.g. member-of, contained-in, located-in), they
confirm that, if the initial set of seeds mixes pairs of different subtypes, the algorithm
fails to capture these subtypes. But even when they carefully select seeds of
only one subtype, part-whole relations of other subtypes are discovered.
Relation extraction from the Web
In the last decade, with the explosion of available electronic contents, researchers
felt the need for developing systems that acquire open-domain facts from large
collections of text, including the Web. Given the size of the data to exploit, these
systems, whose final goal was to turn the texts into a large knowledge base, should
be robust and scalable enough.
An earlier approach to this problem was the Dual Iterative Pattern Expansion
(DIPRE, Brin (1998)), a weakly-supervised technique for extracting a structured
relation from the Web. DIPRE bootstraps from an initial set of seed examples,
which is the only required training. For instance, for the extraction of
locationOf(location, organisation) relations, the following seeds could be provided:
{Redmond, Microsoft}, {Cupertino, Apple}, {Armonk, IBM }, {Seattle, Boeing} and
{Santa Clara, Intel}. After finding all close occurrences of the related entities in
the collection, patterns where they co-occur are used to extract new pairs holding
the same relation. Snowball (Agichtein and Gravano, 2000) is a weakly-supervised
system for extracting structured data from textual documents built on the idea of
DIPRE, but extending it to incorporate automatic pattern and pair evaluation
KnowItAll (Etzioni et al., 2004) is an autonomous, domain-independent system
that extracts facts, concepts, and relationships from the Web. The only domainspecific
input to KnowItAll is a set of predicates that constitute its focus and a set
of generic domain-independent extractions. KnowItAll uses the extraction patterns
with classes (e.g. cities, movies) in order to generate extraction rules specific for each
class of instances to extract. A web search engine is queried with keywords in each
rule, and the rule is applied to extract information from the retrieved pages. The
likelihood of each candidate fact is later assessed with a kind of PMI-IR (Turney,
2001), using an estimation of the search engine hit counts.