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Tech Report kmi-02-03 Abstract


The Task Ontology Component of the Scheduling Library
Techreport ID: kmi-02-03
Date: 2002
Author(s): Dnyanesh Gajanan Rajpathak

Scheduling is a ubiquitous task spanning over many activities in day to day life. Usually, a scheduling problem comes in a variety of flavours, which makes it a hard problem both in theory as well as in practice. The scheduling task concerns with the assignment of jobs to the resources and time ranges within a pre-defined time framework while maintaining various constraints and satisfying requirements. Due to the diverse nature of scheduling problem the nature of its main building blocks differs according to the target domain. Such a changing nature of target domain increases the overall time and cost required for building an application system. An ontology can be seen as a reference model that describe the various entities that exist in universe of discourse along with their properties. These entities can be individuals, classes, relationships, and functions. In sum anything that can be useful for describing the classes of task in hand. In this report we propose generic task ontology for constructing scheduling applications. The proposed task ontology is generic to emphasise that it is both domain as well as application independent. We refer to it as task ontology such that it describes the classes of scheduling task independent of the various ways by which this task can be solved. We envisage moving beyond the current brittle approaches to the system development by providing the firm theoretical as well engineering foundations for the various classes of knowledge-intensive scheduling applications.
 
KMi Publications 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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