Mining Knowledge from Textual Databases: An Approach using Ontology-based Context Vectors
This event took place on Monday 31 January 2005 at 12:30
Alexandre Goncalves KMi, The Open University
The increase in research activities claim ways to discover patterns in order to understand the behavior of these activities as well as to manage the resources used to support them. In this paper we propose a semantic mining approach to knowledge discovery based on context vectors and ontology. The approach is illustrated using ontology and resumes from a Science & Technology database as inputs. The involved phases in the proposed model are described emphasizing preprocessing and pattern generation. The main contribution of this paper is the proposal of a semantic component toward data mining. Initial results show a suitable cluster generation in terms of number and quality. The approach produced better classification when comparing the generated clusters against a set of vectors representing knowledge areas.
This event took place on Monday 31 January 2005 at 12:30
The increase in research activities claim ways to discover patterns in order to understand the behavior of these activities as well as to manage the resources used to support them. In this paper we propose a semantic mining approach to knowledge discovery based on context vectors and ontology. The approach is illustrated using ontology and resumes from a Science & Technology database as inputs. The involved phases in the proposed model are described emphasizing preprocessing and pattern generation. The main contribution of this paper is the proposal of a semantic component toward data mining. Initial results show a suitable cluster generation in terms of number and quality. The approach produced better classification when comparing the generated clusters against a set of vectors representing knowledge areas.
Future Internet
KnowledgeManagementMultimedia &
Information SystemsNarrative
HypermediaNew Media SystemsSemantic Web &
Knowledge ServicesSocial Software
Multimedia and Information Systems is...
We focus on content-based information retrieval over a wide range of data spanning form unstructured text and unlabelled images over spoken documents and music to videos. This encompasses the modelling of human perception of relevance and similarity, the learning from user actions and the up-to-date presentation of information. Currently we are building a research version of an integrated multimedia information retrieval system MIR to be used as a research prototype. We aim for a system that understands the user's information need and successfully links it to the appropriate information sources, be it a report or a TV news clip. This work is guided by the vision that an automated knowledge extraction system ultimately empowers people making efficient use of information sources without the burden of filing data into specialised databases.
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