Challenges of Semantic Knowledge Management

Forrester (Moore 2007) estimate that more than 80% of all corporate information is unstructured. Knowledge workers are increasingly overwhelmed by information from a bewildering array of information sources: emails, intranets, the web, etc. and yet still find it hard to access the specific information required for the task at hand. This implies that knowledge worker productivity is reduced and that organisations may be making decisions on the basis of incomplete knowledge. Furthermore, an inability to access key information can lead to compliance failure.

As we have described in this volume, semantic technology is helping address these issues by associating unstructured information with domain ontologies. This makes possible more intelligent information access facilities by annotating documents (and parts of documents) with semantic meta-information—information, formally expressed, which tells the machine what the document or subdocument is about. This allows more sophisticated analysis of documents: for example, named entity recognition is a language processing technique which can identify particular locations, organisations or people mentioned in textual documents with ontological descriptions of those entities. Similarly, knowledge discovery techniques can be used to analyse content and classify it against an ontology, or indeed to derive new ontologies from content. Ontology management is then the set of tools, techniques and technology for managing the resulting ontologies and associated metadata.