Visualize your Enterprise from a New Perspective with Semantic Enhancement Server

Semaphore’s visualization tools let you quickly see how information within your enterprise is related; the topics, concepts and relationships between them. Our easy-to-use tools let you explore your model and immediately access information using classification output to drive search and information discovery.

The concepts within your model are represented by a series of bubbles which allow you browse your taxonomy.

Related concepts are identified using alternate colors and topics can be quickly drilled into to view a particular model/topic in greater detail. Topics/models can also be drilled up level(s) to display less detail to gain a broader perspective.

Semantic Enhancement Server drives exceptional search results

Semaphore Semantic Enhancement Server (SES) provides users with an exceptional search experience. Our visualization tools drive enterprise-wide collaboration and provide a mechanism that allow information scientists and subject matter experts to browse, view and understand the concepts, labels and relationships within your model.

Semaphore Ontology Editor lets you build a model that reflects the topics, concepts, vocabulary and the relationships among them. Semaphore Rulebase Generator creates model-driven rulebases by extracting the concepts, labels and relationships from within your taxonomy or ontology. We pass these rulebases to Semaphore Classification Server to result in a precise, consistent and complete set of rules that drive content classification.

Our classification process is enhanced with Advanced Language Packs, which employ advanced linguistic techniques and strategies that apply context and meaning to content. Processing results are deployed to Classification and Semantic Enhancement Server to improve classification results and user’s search experience.

Semantic Enhancement Server provides extensions to integrate with Apache Solr, Microsoft SharePoint and other search engines by creating a scalable, resilient, high-speed index of the information within your taxonomy or ontology. This information can be used to perform real-time queries using:

  • Concept mapping - search using familiar vocabulary. The taxonomy is used to identify the possible topics the user is interested in based on search criteria.
  • Topic maps – show users how the results breakdown by listing the topics in the results and indicate how often a topic is represented. Topic maps allow users to refine their search by selecting the topics of interest. 
  • Best bets - provide links to web pages, documents, applications and other resources associated with a selected topic. The link is managed centrally and stored within the taxonomy separate from the content.
  • Related topics – related topic boxes display other topics (associated subjects), which may be of interest. When you select a related topic, a new search is executed and results returned. Related topics are identified using the taxonomy or ontology and can be quickly modified.
  • Taxonomy navigator - view the taxonomy hierarchy and drill down through it to select a topic from the taxonomy. Once a topic is selected, all content for that topic is displayed.
  • Dynamic summaries - enhance search results by highlighting the evidence found during the classification process – that is – the preferred labels, synonyms and related topics are highlighted in the search results.
  • Topic path browser – shows the user how the concept they are searching for fits into the overall taxonomy or ontology. Users can drill up to view broader concepts and a more generic result set or drill down to view narrower topics and a more granular result set or they can drill across to review sibling topics.

Semaphore's visualization tools, enhance collaboration and quickly identify and present users with the concepts, topics and relationships within their taxonomy and ontology. Our search engine integration provides a sophisticated and rich user search and retrieval experience, which can be used to gain insight, find new opportunities and drive change.

Drive innovation and information discovery with graph-based technology

Ontology Editor and Classification Server results can be expressed in RDF standard triples and stored in compatible graph databases. Using a graph query language, the knowledge graph can be traversed associating the concepts in the model to the relevant data points allowing you to identify patterns, discover trends and answer questions you could not have answered before.

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