Semantic technology

What is taxonomy?

A hierarchical or associative ordering of terms.

Examples of types of taxonomy

Examples of types of taxonomy

A taxonomy is a hierarchical or associative ordering of terms representing categories. A taxonomy takes the form of a tree or a graph in the mathematical sense. A taxonomy typically has minimal nodes, representing lowest or most specific categories in which no sub-categories are included as well as a top-most or maximal node or lattice, representing the maximum or general category.

What are folk taxonomies?

A category hierarchy with 5-6 levels that has its most cognitively basic categories in the middle.

Source: George Lakoff

Source: George Lakoff

In folk taxonomies, categories are not merely organized in a hierarchy from the most general to the most specific, but are also organized so that the categories that are most cognitively basic are “in the middle” of a general-to-specific hierarchy. Generalization proceeds upward from the basic level and specialization proceeds down.

A basic level category is somewhere in the middle of a hierarchy and is cognitively basic. It is the level that is learned earliest. Usually has a short name and is used frequently. It is the highest level at which a single mental image can reflect the category. Also, there is no definitive basic level for a hierarchy – it is dependent on the audience. Most of our knowledge is organized around basic level categories.

What is the Watson Ecosystem?

IBM launches cognitive computing cloud platform.

Cognitive computing is going mainstream

IBM is taking Watson and cognitive computing into the mainstream

The Watson Ecosystem empowers development of “Powered by IBM Watson” applications. Partners are building a community of organizations who share a vision for shaping the future of their industry through the power of cognitive computing. IBM’s cognitive computing cloud platform will help drive innovation and creative solutions to some of life’s most challenging problems. The ecosystem combines business partners’ experience, offerings, domain knowledge and presence with IBM’s technology, tools, brand, and marketing.

IBM offers a single source for developers to conceive and produce their Powered by Watson applications:

  • Watson Developer Cloud — will offer the technology, tools and APIs to ISVs for self-service training, development, and testing of their cognitive application. The Developer Cloud is expected to help jump-start and accelerate creation of Powered by IBM Watson applications.
  • Content Store — will bring together unique and varying sources of data, including general knowledge, industry specific content, and subject matter expertise to inform, educate, and help create an actionable experience for the user. The store is intended to be a clearinghouse of information presenting a unique opportunity for content providers to engage a new channel and bring their data to life in a whole new way.
  • Network — Staffing and talent organizations with access to in-demand skills like linguistics, natural language processing, machine learning, user experience design, and analytics will help bridge any skill gaps to facilitate the delivery of cognitive applications. .These talent hubs and their respective agents are expected to work directly with members of the Ecosystem on a fee or project basis.

How does cognitive computing differ from earlier artificial intelligence (AI)?

Cognitive computing systems learn and interact naturally with people to extend what either humans or machine could do on their own. In traditional AI, humans are not part of the equation. In cognitive computing, humans and machines work together. Rather than being programmed to anticipate every possible answer or action needed to perform a function or set of tasks, cognitive computing systems are trained using artificial intelligence (AI) and machine learning algorithms to sense, predict, infer and, in some ways, think.

Cognitive computing systems get better over time as they build knowledge and learn a domain – its language and terminology, its processes and its preferred methods of interacting. Unlike expert systems of the past which required rules to be hard coded into a system by a human expert, cognitive computers can process natural language and unstructured data and learn by experience, much in the same way humans do. While they’ll have deep domain expertise, instead of replacing human experts, cognitive computers will act as a decision support system and help them make better decisions based on the best available data, whether in healthcare, finance or customer service.

Smart solutions demand strong design think

IBM unveils new Design Studio to transform the way we interact with software and emerging technologies

IBM unveils new Design Studio to transform the way we interact with software and emerging technologies

The era of smart systems and cognitive computing is upon us. IBM’s product design studio in Austin, Texas will focus on how a new era of software will be designed, developed and consumed by organizations around the globe.

In addition to actively educating existing IBM team leads from engineering, design, and product management on new approaches to design, IBM is recruiting design experts and is engaging with leading design schools across the country to bring designers on board, including the Institute of Design at Stanford University, Rhode Island School of Design, Carnegie Mellon University, North Carolina State University, and Savannah College of Art & Design. Leading skill sets at the IBM Design Studio include Visual Design, Graphic artists, User Experience Designers, Design Developers, including Mobile developers, and Industrial designers.

Why is visualization important?

Patterns provide a 60% faster way to locate, navigate, and grasp meanings.

Examples of information visualization. Source: VisualComplexity

Examples of information visualization. Source: VisualComplexity

Information visualization technologies can enable most users to locate specific information they are looking for as much as 60 percent faster than with standard navigation methods.

Visualization techniques exploit multiple dimensions, e.g.:

  • 1D — Links, keywords lists, audio.
  • 2D — Taxonomies, facets, thesauri, trees, tables, charts, maps, diagrams, graphs, schematics typography, image
  • 2.5D — Layers, overlays, builds, multi-spaces, 2D animation, 2D navigation in time
  • 3D/4D — 3-dimensional models, characters, scenes, 3D animation, virtual worlds, synthetic worlds, and reality browsing.

Semantic Verses

Semantic capabilities for desktop and mobile apps.

Semantic capabilities for desktop and mobile apps.

I had an interesting conversation with Dr. Walid Saba about semantic search, enrichment,  summarization, and recommendation capabilities that he and his colleagues have been developing at Magnet. As he describes it, the basic issues they are addressing can be described this way:

Why is the retrieval of semantically/topically relevant information difficult?

Two reasons:

  • Bad (semantic) precision — A document might mention a phrase several times, although the document is not at all about that topic.
  • Bad (semantic) recall — A document might never mention a phrase explicitly, but it is essentially about a semantically/topically related topic.

The essential problem is how do we determine what a certain document is (semantically, or topically) about, regardless of the actual words being used. To do this, we must go from words to meanings.

First, we must perform word-sense disambiguation with very high accuracy. This involves recognizing named entities.

Second, using some concept algebra, we must make topics (compound meanings) from simpler ones, for example: “Android phones” “Apple devices”,“’s web site”, “text message”

Third, we must go from topics to key topics. We understand what a document is essentially about when we can determine the set of key topics. Semantic Verses does this by identifying a potentially infinite set of topics, as opposed to a pre-engineered ontology of a finite set of topics. This enables semantically comparing topics written using completely different sets of words across languages and across media.

As highlighted in the figure above, Dr. Saba and his team are developing a number tools for individuals and businesses to tap these semantic capabilities. This includes plug-ins for browsers; plug-ins for MS Word and Powerpoint; and tools for blogs as well as an API.

The underlying semantic (concept computing) engine runs quickly on a single node e.g.: 10 queries  per second on a database of 10 million documents.  No training required to get started.  On a single node it can process 50 documents per second. Its dynamic index has a small footprint. Adding to it is real-time and does not require re-clustering or re-indexing.

Dr. Walid Saba

Dr. Walid Saba

Seven examples of the business value of ontologies

Each year at the National Institutes of Standards and Technology, the Ontolog Forum brings together a community of  researchers, educators, and practitioners to discuss the role of ontologies in next generation solutions.  This presentation highlights seven case examples showing how ontologies deliver business value.


Industrial giants placing big bets on smart technologies and concept computing

GE’s vision of the industrial internet

GE’s vision of the industrial internet

In the fall of 2012 General Electric came out with a study predicting huge economic growth resulting from the Industrial Internet.  The two authors are GE’s top strategist and chief economist. It’s a serious report.

Here is the thesis. Mechanization of work over the past 200 years has resulted in a 50X worker productivity increase.  The next stage is the integration of machines with computing and the Internet. The result, they predict will be tens of trillions of dollars in economic expansion and improved quality of life worldwide.

Industrial internet fuels global economic expansion.

Industrial internet fuels global economic expansion.

Here are two slides from the GE report.

The one on the left identifies three key elements of the industrial internet.  The implication is that patterns of work will change and that industrial products and processes will gain a cradle to sunset life history.

The diagram to the right projects the value of the industrial internet in the form of potential performance gains across five economic sectors. This is a minimal projection, the power of 1 percent, but we’re still talking $ billions. GE’s overall projection for industrial internet fueled economic expansion to 2030 is closer to $40 trillion.

Agent Smith (from the Matrix) helping GE promote smart technologies.

Agent Smith (from the Matrix) helping GE promote smart technologies.

During 2013 GE began taking its industrial internet thesis to the street. Their recent TV commercials bring back Agent Smith from the Matrix. This scene is about the interconnection and intelligent interaction of machines, software, and healthcare professionals to deliver improved outcomes for patients — a waiting room becomes, just a room.

One version of the ad ends with agent Smith offering a child a choice of lollypops — a red one or a blue one.