Tech Reports
Tech Report kmi-02-06 Abstract
A Spreading Activation Framework for Ontology-enhanced Adaptive Information Access
Techreport ID: kmi-02-06
Date: 2002
Author(s): Md Maruf Hasan, Motta, E., Domingue, J.B., Buckingham-Shum, S., Vargas-Vera, M. and Lanzoni, M.
This research investigates a unique Indexing Structure and Navigational Interface which make use of (1) ontology-driven knowledge (2) statistically derived indexing parameters, and (3) experts' feedback into a single Spreading Activation Framework to harness knowledge from heterogeneous knowledge assets within an organisation. Organisational ontologies capture precise knowledge about organisational entities: people, projects, activities, information sources and so on. We extract useful entities and their relationships from an ontology-driven knowledge base. We also process collections of documents (archives) accumulated in heterogeneous information-bases within an organisation and derive indexing parameters. Such information is then mapped to a weighted graph (network). The network contains three sets of nodes consists of documents, ontological entities and statistically derived entities. Document nodes are connected to both ontology-driven entities and statistically derived entities, and vice-versa with relevant weights. Retrieval is performed by spreading query-based activation into the network and selecting the most-activated nodes. Experts in the organisation either navigate the network using associative relations among nodes or with specific queries. Expert’s feedback is captured and the network weights are continuously adapted. This framework essentially combines precise knowledge (ontology-driven), non-precise knowledge (statistically driven) and Expert’s feedback (adaptation) into a single framework for adaptive information retrieval and navigation.
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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