  Relation Extraction (RE), the task of detecting and characterizing semantic
relations between entities in text, has gained much importance in the last two
decades, mainly in the biomedical domain. Many papers have been published on
Relation Extraction using supervised machine learning techniques. Most of these
techniques rely on statistical methods, such as feature-based and
tree-kernels-based methods. Such statistical learning techniques are usually
based on a propositional hypothesis space for representing examples, i.e., they
employ an attribute-value representation of features. This kind of
representation has some drawbacks, particularly in the extraction of complex
relations which demand more contextual information about the involving
instances, i.e., it is not able to effectively capture structural information
from parse trees without loss of information. In this work, we present
OntoILPER, a logic-based relational learning approach to Relation Extraction
that uses Inductive Logic Programming for generating extraction models in the
form of symbolic extraction rules. OntoILPER takes profit of a rich relational
representation of examples, which can alleviate the aforementioned drawbacks.
The proposed relational approach seems to be more suitable for Relation
Extraction than statistical ones for several reasons that we argue. Moreover,
OntoILPER uses a domain ontology that guides the background knowledge
generation process and is used for storing the extracted relation instances.
The induced extraction rules were evaluated on three protein-protein
interaction datasets from the biomedical domain. The performance of OntoILPER
extraction models was compared with other state-of-the-art RE systems. The
encouraging results seem to demonstrate the effectiveness of the proposed
solution.
