Big Data KE

Hyperaxes:  Semantically Aware Knowledge Exchange Platforms

Our Semantic Knowledge Exchange’s Information Retrieval ( IR) and Natural Language Processing ( NLP) capabilities will organize, query and socialize content that is formally published, web crawled, user-generated or operationally created in structured, unstructured or semi-structured formats.

Think of this as “StackEchange meets big data.”  The Semantic Knowledge Exchange is a sophisticated social problem solving platform  fully integrated with a leading edge machine-learning based computational text analytics. With this approach, knowledge workers, customer communities, healthcare workers and other virtual teams can collaborate to solve problems in the virtual Knowledge Exchange while working in the context of large, complex bodies of semantically organized content.

Key capabilities of the Grassfed Hyperaxes(™) Engine:

  • Machine Learning based Information Retrieval ( IR/ML)
  • Natural Language Processing (NLP)
  • Document Understanding
  • Graph-based Reasoning
  • Bayesian reasoning
  • Support Vector Machines (SVM)
  • Belief networks
  • Document Classifiers
  • Business Intelligence data mining

Unstructured Data Analysis

Capabilities include semantic analysis of unstructured or “semi-structured” text content such as web pages, documents,  social media, research papers, reports, medical records, work logs and forms,  RDF triplestores, and any free-form text:

  • Sentiment analysis (evaluating the sentiment of the author of a document)
  • Named Entity Recognition (parsing out significant references to real world objects)
  • Document classification (different ways of cauterizing and classifying documents)
  • Relevance recognition (determining how relevant a document is to a given topic)
  • Paragraph Gisting (extracting the core meaning of a paragraph)
  • Ontological search (recognizing similarities from context)
  • Semantic filtering (recognizing what a reference is about from context)
  • Auto-generation of tags to add to the search space of user-generated content
  • Auto generation of links between documents
  • Associative retrieval of documents

Structured Data Analysis

Support for semantic analysis of structured data such as that found in relational, B.I. or flat databases is supported with following capabilities:

  • Faceted search (allowing repetitive searches to filter the results of prior searches)
  • Linked Data Analysis  (across structured data sets)
  • Data Mining on Big Data collections (pattern matching and selection)
  • Predictive Analytics (locating and identifying trends in structured data)
  • Data Record Classification (naïve bayes, k-nearest neighbor)

Knowledge Workflows Driven by  Machine Intelligent Information Retrieval

When combining big data technologies with knowledge exchanges, we are working in an exciting new realm where content information retrieval ( IR), text analytics (TA) and machine learning (ML) are used to pre-digest vast amounts of structured and unstructured data which can be continually fed into collaborative knowledge workflows in a semantically accessible and familiar form..

The Grassfed Hyperaxes(™) Engine integrates advanced IR / TA / ML capabilities with existing business platforms, including collaboration suites, Business Intelligence software, CRM, SFA, legal systems and content management ( CMS) applications.

Making mountains of content understandable

In a traditional information retrieval application ..  you index the documents with TF/IDF and then query against them to get a search results listing…

http://en.wikipedia.org/wiki/Tf-idf

We support a very wide range ot TF/IDF applications with our customized ElasticSearch IR / TA platform.. but we can also do the opposite… index a large set of queries ( i.e., rules, business logic, metadata structures, exploratory categorization routines) and then throw incoming documents and structured content against the indexed queries..

This so called “reverse indexing” approach makes it possible to quickly parse and process a large number of heterogeneous documents, papers, research notes,  transaction records, annotations, social media content, and unstructured / semi structured text records looking for categories, topics, tags, and fuzzy emergent patterns.

More importantly our engine helps capture and share the intelligence for these reusable queries between your knowledge workers.

In IT/ TA terms.. the underlying mechanism is called percolation. (well at least in Elasticsearch related realms, not sure what HP Autonomy or Oracle is calling it these days)

https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-percolate-query.html

We have on our team top level Hadoop /  NL / ML experts who write software that extends the IR/TA capabilities into the most advanced reaches of probabilistic machine learning. By integrating our engine with other platforms we create new forms of social knowledge sharing applications in finance, insurance, intelligence, marketing, publishing, e-commerce and healthcare.

 If your team has a mountain of data that needs to be searched and organized and then used in a collaborative knowledge workflow..  the answer may be a “semantic knowledge exchange” .

 

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