Authors:
Abduladem Aljamel
;
Taha Osman
and
Giovanni Acampora
Affiliation:
Nottingham Trent University, United Kingdom
Keyword(s):
Information Extraction, Relation Extraction, Knowledge-Base, Supervised Machine Learning, Natural Language Processing, Semantic Web.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Evolutionary Computing
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Soft Computing
;
Symbolic Systems
Abstract:
The increasing accessibility and availability of online data provides a valuable knowledge source for information analysis and decision-making processes. In this paper we argue that extracting information from this data is better guided by domain knowledge of the targeted use-case and investigate the integration of a knowledge-driven approach with Machine Learning techniques in order to improve the quality of the Relation Extraction process. Targeting the financial domain, we use Semantic Web Technologies to build the domain Knowledgebase, which is in turn exploited to collect distant supervision training data from semantic linked datasets such as DBPedia and Freebase. We conducted a serious of experiments that utilise the number of Machine Learning algorithms to report on the favourable implementations/configuration for successful Information Extraction for our targeted domain.