KMi Seminars
Towards Nootropia: a Non-Linear Approach to Adaptive Document Filtering
This event took place on Monday 02 February 2004 at 12:30

 
Nikolaos Nanas

In recent years, it has become increasingly difficult for users to find relevant information within the accessible glut. Adaptive Information Filtering (AIF) tackles this problem through a tailored representation of the user interests, called "user profile". The user profile must be able to represent the user's multiple topics of interest and adapt to various changes in them.

With our experimental system 'Nootropia', we achieve adaptive document filtering with a single, multi-topic user profile. A hierarchical term network that takes into account topical and lexical correlations between terms and identifies topic-subtopic relations between them, is used to represent a user's multiple interests and distinguish between them. A series of non-linear document evaluation functions is then established on the hierarchical network. Adaptation is then achieved through a process of self-organisation that constantly readjusts the profile stucturally, in response to user feedback. The approach has been tested experimentally with positive results. Furthemore, Nootropia may support additional personalisation services like automatic query formulation and expert finding, or can be applied in combination with collaborative filtering. It is also interesting that, in principle, the approach can be applied to other media like audio and image for which features can be automatically extracted.

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KMi Seminars Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

Multimedia and Information Systems is...


Multimedia and Information Systems
Our research is centred around the theme of Multimedia Information Retrieval, ie, Video Search Engines, Image Databases, Spoken Document Retrieval, Music Retrieval, Query Languages and Query Mediation.

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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