Extracting knowledge from web communities and linked data for case-based reasoning systems

Sauer, Christian and Roth-Berghofer, Thomas (2013) Extracting knowledge from web communities and linked data for case-based reasoning systems. Proceedings of the 18th UKCBR 2013 Workshop.

Extracting Knowledge from Web Communities and Linked Data for Case-based Reasoning Systems.pdf - Submitted Version

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The recent developments of Web 2.0, has driven the web
content from its static and formalised nature to a highly user-driven nature.
Such web content includes blogs, forum posts and tweets which
are mostly expressed in an unsystematic manner. Due to this reason,
retrieving and reusing this content has become challenging. As a solution,
Reichle et al. [6] present a novel architecture named SEASALT and
within this architecture present the docQuery project, carried out as one
instantiation of the presented architecture focusing on the domain of
travel medicine. The work presented in this paper is demonstrating the
use of Twitter feeds as a knowledge source within the SEASALT architecture,
expanding the knowledge-base of the docQuery project. A Multi
Agent System is developed to acquire Twitter feeds related to travel
medicine, which are then transferred for further knowledge extraction to
the Apprentice agent component of the SEASALT Architecture named:
Knowledge Extraction Workbench (KEWo). In this paper, Twitter is
analysed as a knowledge source in terms of the amount of data it can
provide on a specifi topic and how this provided amount of tweets has
an impact on the performance and quality of knowledge extracted from
them. Furthermore, the paper analyses how well the hash tag feature
provided in Twitter can be employed as a source of structuring information.
As a result of this analysis, a set of Group-By Features is introduced
to enhance the knowledge extraction based on attributes of Twitter feeds
such as retweet count and number of followers. As its fi�nal output, this
paper demonstrates how to create a virtual community of experts within
the SEASALT architecture for further knowledge extraction from said

Item Type: Article
Subjects: Computing
Depositing User: Christian Sauer
Date Deposited: 26 May 2016 12:31
Last Modified: 29 Sep 2016 15:33
URI: http://repository.uwl.ac.uk/id/eprint/2168


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