Knowledge Management Glossary

Knowledge Management Glossary

Knowledge Management Models - play an increasingly important role in enterprises because many systems – from search engines to database integrations to text analytic applications – use information models that identify concepts, the terms that represent them, and the resulting relationships between those terms. Knowledge models provide a new perspective for managing assets by taking into account the multiple meanings a single information asset possesses; notably the nature of and interrelations between assets. Knowledge models thus capture and codify knowledge about assets that heretofore existed only in the minds of subject matter experts and others interested in a particular domain of human knowledge.

Text Analytics - is gathering momentum as more organizations attempt to develop and deploy applications that analyze unstructured and semi-structured content in order to identify and apply structure to specific kinds of information objects, often called “entities." Text analytics applications are an important part of corporate efforts to reduce information glut and increase “find-ability” and are becoming a permanent part of the information processing landscape. Classification and indexing systems are using text analytics to provide enhanced searching and navigation.

Text Analytics applications typically follow two approaches:

• Statistical, which uses a system trained with test data to apply statistical algorithms and methods to unstructured content in order to identify and classify a document’s concepts and subjects
• Linguistic, which applies rule-based tokenization and analysis to unstructured content in order to identify specific kinds of information objects called “entities” and then extract these for further processing.

Taxonomies and Ontologies - are hierarchical classifications of concepts or terms. Strictly speaking, taxonomies are based on the principle of inheritance, while ontologies enable description of relationships between taxonomies or objects in taxonomies. Describing the relationships between taxonomies is the next logical step in knowledge modeling beyond taxonomies/thesauri. Ontologies can also encompass all aspects of less complicated forms of knowledge models.

Topic Maps - By serving as a knowledge layer between storage devices such as Web sites or databases and user interfaces like Web browsers, topic maps enable guided navigation through enormous volumes of content. Just as important, they let users share knowledge independently of proprietary systems. Topic maps also create semantic networks, which are a meaningful description of the relationship between subjects.

Faceted Classification - assigns multiple classifications to each information object within a collection. Thus faceted classification allows for multiple views over the same set of information objects, providing flexibility to the order in which the categories and subcategories of classification are displayed. Sometimes, taxonomic classification is used and thus multiple taxonomies may be applied to the same set of information objects, while the order of the categories displayed for view can vary according to the ‘facets’ of interest.

Web Ontology Language (OWL) - is a markup language for publishing and sharing data using ontologies on the Internet. A vocabulary extension of the Resource Description Framework (RDF), OWL represents the meanings of terms in vocabularies and the relationships between those terms in a way that is suitable for processing by software.

Resource Description Framework (RDF) - is a World Wide Web Consortium (W3C) specification designed as a metadata model for Web resources using XML. RDF has now come to be used as a general method of modeling knowledge. The RDF metadata model is based upon the idea of making statements about resources.

Machine Aided Indexing (MAI) - typically begins with text analytics processing integrated with a knowledge model or controlled vocabulary in order to identify potential, authorized index terms. MAI brings these elements together in a user interface that enables subject matter experts to rapidly review and select the actual index terms with respect to business rules, a knowledge model and the original article. MAI integrates this within a larger workflow process, which greatly enhances consistency in selection of index terms for documents.

Knowledge Management