Information Technology Reference
In-Depth Information
Retrieving Wiki Content Using an Ontology
Carlos Miguel Tobar, Alessandro Santos Germer, Juan Manuel Adán-Coello,
and Ricardo Luís de Freitas
*
Abstract.
This chapter addresses a question regarding relevant information in a
social media such as a wiki that can contain huge amount of text, written in slang
or in natural language, without necessarily observing a fixed terminology set. This
text could not always be adherent to the discussed subject. The main motivation
leads to the need of developing methods that would allow the extraction of rele-
vant information in such scenario. A result system was designed upon ideas from
the semantic Web combined with adaptive mechanisms and a modification of the
classic vector model for information retrieval. The semantic information is not
embedded in the media but within a structurally independent ontology. It was im-
plemented using Java and a MySQL database. The objective was the achievement
of, at least, 80% for recall and precision on the system results. The system was
considered successful by achieving rates of 100% of recall and approximately
93% of precision.
1 Introduction
Social media can be understood as an on-line, in-the-Web, communication service
for humans. According to Mayfield [1], a social media can share: participation,
openness, conversation, community, and connectedness. A wiki is a social media
that allows people to edit content in a collaborative fashion.
Wikis can be private or open, and are used as informational resources for dis-
cussion, as a portal, maintained by a community. One such of these is the wiki for
the Fedora Marketing Project [2].
Usually, wikis contain ever increasing huge amounts of text data, which are in-
serted using natural language, without concerns with linguistic formalities, such as
spelling. They even can contain slang as well as bad formed words and expressions.
A wiki usually focuses an area of interest or a specific discussion subject. In the
case of general focus, wikis can be used for mining potential customers or infor-
mation on customer satisfaction among several other issues. Even with an estab-
lished focus, individuals usually insert not related text in a wiki.
