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Tech Report kmi-96-01 Abstract


A Taxonomy of Intellectual Capital and a Methodology for Auditing It
Techreport ID: kmi-96-01
Date: 1996
Author(s): Annie Brooking and Enrico Motta
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Ownership of intellectual property is rarely measured. While many companies spend huge amounts of money filing and protecting patents, too often that activity is defensive. Patents are not exercised and do not generate wealth for the inventor. Their windows of opportunity remain a mystery to their owners, as does their value. Organisations that are unaware of the value of their intellectual property are missing an important asset that ought to be included in any exercise which aims to measure the value of the organisation. Other intangible assets such as know-how, customer relations, networking infrastructure, business processes and so on, which also add value to the organisation, are neither measured nor their growth monitored and nurtured as part of a corporate growth strategy. These elements are collectively referred to as Intellectual Capital (IC). Organisations that would agree that the above mentioned intangibles are valuable, are unable to record, measure and audit their IC, due to the absence of a taxonomy for Intellectual Capital and metrics with which to measure it. This paper discusses these issues and proposes a Taxonomy of Intellectual Capital together with a methodology for identifying and valuing it. It discusses mechanisms and tools for locating and identifying IC within the organisation, together with a means for designing and maintaining the IC Knowledge Base. Finally the relationship between corporate goals, Intellectual Capital and the ability of the organisation to succeed is discussed.

Publication(s):

17th Annual National Business Conference, McMaster University, Hamilton, Ontario, Canada, January 24-26, 1996
 
KMi Publications Event | SSSW 2013, The 10th Summer School on Ontology Engineering and the Semantic Web Journal | 25 years of knowledge acquisition
 

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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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