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

52 Chapter 3. Related Work
Due to the scalability issues of KnowItAll, its authors proposed the paradigm of
Open Information Extraction (Banko et al., 2007) (OIE, see section 2.3.2 for more
details). OIE systems make a single data-driven pass over a corpus and extract a
large set of relational tuples, without requiring any human input.
TextRunner (Banko et al., 2007) is a fully-implemented OIE system. In order
to get a classifier that labels candidate extractions as trustworthy or not, a small
corpus sample is given as input . Then, all tuples that are potential relations are
extracted from the corpus. In the last step, relation names are normalised and
tuples have a probability assigned. TextRunner is more scalable than KnowItAll,
has a lower error rate and, considering only a set of 10 relation types, both systems
extract an identical number of relations. However, since TextRunner does not take
as input the name of the relations, its complete set of extractions contains more
types of relations.
More recently, ReVerb (Etzioni et al., 2011; Fader et al., 2011), a new and more
efficient OIE system that does not need a classifier was presented. ReVerb is
solely based on two constraints: (i) a syntactic constraint requires that the relation
phrase matches a POS regular expression (verb | verb prep | verb word*
prep); (ii) a lexical constraint requires that each relevant relation phrase occurs in
the corpus with different arguments. The following illustrate ReVerb extractions:
• {Calcium, prevents, osteoporosis}
• {A galaxy, consists of, stars and stellar remnants}
• {Most galaxies, appear to be, dwarf galaxies, which are small}
The Never Ending Language Learner (NELL, Carlson et al. (2010a)) learns
from reading contents on the Web and gets better at reading as it reads the same
text multiple times. NELL’s starting point is: (i) a set of fundamental categories
(e.g. person, sportsTeam, fruit, emotion) and relation types (e.g., playsOn-
Team(athlete,sportsTeam), playsInstrument(musician,instrument)), that constitute
an ontology; and (ii) a set of 10 to 15 seed examples for each category and relation.
Then, NELL reads web pages continuously, 24 hours a day, for extracting new category
instances and new relations between instances, which are used to populate the
ontology. The extracted contents are used as a self-supervised collection of training
examples, used in the acquisition of new discriminating patterns. NELL employs
coupled-training (Carlson et al., 2010b), which combines the simultaneous training
of many extraction methods. The following are examples of NELL extractions:
• musicArtistGenre(Nirvana, Grunge)
• tvStationInCity(WLS-TV, Chicago)
• sportUsesEquip(soccer, balls)
The main difference between NELL and OIE systems is that NELL learns extractors
for a fixed set of known relations, while an OIE system can extract meaningful
information from any kind of corpora, on any domain, as relations are not given as
a starting point (Etzioni et al., 2011). This has also an impact on the quantity of
extracted knowledge. Still, recently, Mohamed et al. (2011) reported how a system
like NELL can learn new relation types between already extracted categories.