Taxonomy and Ontology Management

The terms taxonomy, ontology, controlled vocabulary and thesauri are often used interchangeably, and while they are similar, they are not identical; each describes a specific way of modelling:

  • A taxonomy is a hierarchical way of naming, describing and classifying a subject matter area. Each level in the hierarchy further defines the level above it for example, a lion is a more specific term than a mammal and a mammal is a more specific term than an animal.
  • An ontology is a representational system which defines the concepts, topics, subjects and the relationships within a subject domain.
  • A controlled vocabulary is a recommended list of terms or headings with an assigned meaning, which are used to organize knowledge for subsequent retrieval. Controlled vocabularies are used in subject indexing schemes and subject headings to enforce the use of a predefined, authorized set of terms identified by the vocabulary owner.
  • A thesaurus can be thought of as a dictionary of words or terms such as synonyms and antonyms that are used to describe a particular subject domain with the purpose of locating and retrieving information at a later time.

Regardless of type, models are the key to auto classification and precise and consistent metadata. When metadata is applied to enterprise information assets, structured and unstructured information can be unified and used to drive key business decisions.

Why create models?

Models such as taxonomies, ontologies, controlled vocabularies and thesauri allow organizations to identify and create a consistent language, which users understand and describes the organization, processes and customers so they can:

  • Apply context and meaning to unstructured information within the enterprise. This valuable information comprises approximately 80% of the information within an organization and resides in file shares, content management systems, on hard drives and in applications.
  • Find the information they need and act on it to drive successful outcomes. The process of manual classification is time consuming and the results are often inconsistent. Auto classification eliminates the burden of manual processes and improves the precision and consistency of search and retrieval.
  • Apply text analytics to identify the meaningful information within enterprise information using machines and algorithms.
  • Apply a series of tags also known as metadata, which identify the core concepts found within information. This metadata is stored and used for recall by search engines such as Google Search Appliance and Solr, and by workflow applications to automate enterprise processes.
  • Identify important information found within information assets, which is not part of your model, but improves classification results such as, people, places, dates, measurements, nouns, zones of text, etc. using extraction.
  • Harmonize disparate information sources against a canonical model to unify information and drive key decisions.

These activities make content self-describing, where fact, entity and sentiment extraction consistently identify the critical elements of an information asset to: augment Business Intelligence; to unify structured and unstructed information and harmonize it for decision making and to identify and secure sensitive content to comply with governmental regulations.

Semaphore Ontology Editor

Semaphore Ontology Editor lets users create and manage taxonomies, ontologies, controlled vocabularies, thesauri and other model types to be used with automated classification processes so that organizations can leverage the hidden intelligence in their information.

With Semaphore you get:

  • A simple and intuitive web-based interface that allows stakeholders, subject matter experts and information scientists to collaboratively build models that reflect the language and characteristics of their organization.
  • The ability to link internal models, leverage public domain models and Linked Open Vocabularies or mine existing information using Text Miner to jump-start the model building process.
  • Task centric models that drive workflow & life-cycle management. Model changes are task-centric to drive workflows and improve feedback loops that mimic the way teams work.
  • Model driven classification systems like Semaphore organize, examine and process the volumes of data flowing into organizations. They let organizations discover relationships between documents and reveal patterns within text so that information can be governed, managed and leveraged to make key business decisions.

A solid infrastructure provides good results

Creating the right taxonomic infrastructure is essential to good classification. Ontology Editor helps you to do that. To learn more about the benefits and features of Semaphore Ontology Editor view our short video.

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