Abstract:
Knowledge Extraction is the process of getting structured data from unstructured or semi structured sources. Knowledge extraction is a sub field under natural language processing and
therefore most sources where knowledge extraction is applied to is text. There has been a
reasonable amount of research applied, and many technologies have been borne out of this
research, namely MUC and ACE. However, in recent years, with the rise of the semantic web and
ontologies A new approach has made its way into the field of knowledge extraction – Ontology
Based Information Extraction. Applying these technologies to the web has become a key effort in
the past few years. This is since the web has changed from the time of Web 1.0 where the web was
simply a bunch of static pages where user interaction was minimal. With the rise of web 2.0, the
internet is no longer a medium to access static information. Users can now interact with pages and
contribute their own efforts. Users can now share their own thoughts easily thus increasing the
amount of user generated content. This has made the web ripe with knowledge, however not all
this information can easily be accessed. This is has led to several years of research in natural
language processing and information extraction. However, the amount of information being mined
is significantly less than it could be. This paper proposes a system that mines social knowledge
from social media platforms and Question and Answer sites.
