Tag Archives: interpretation
30 Nov Architecting Meaningful Relationships
How do you know — anything? Chemicals and electrical impulses splash around in the brain, and voila: we understand the meaning of life, the universe and everything. We have looked at how synapses connect neurons, and how taxonomical and other associations connect concepts, but is it even possible for a computer to understand the complex […]
27 Jun Enterprise Meta-Model Governance
Knowledge comes in multiple flavors. Information systems are used to process data or “content” that is structured or unstructured, across different flavors of knowledge. “Metadata is structured information that describes, explains, locates, or otherwise makes it easier to retrieve, use, or manage an information resource. Metadata is often called data about data or information about information” […]
05 Sep Fuzzy Idea Wars in Always Never Land
Trusted Rx How should a digital device answer a “Should I…” question? “Should I put on my left shoe first, or my right?” “Should I take the alternate route to avoid traffic? “Should I get a more fuel-efficient automobile?” “That stock price is lower, so should I buy now, or is the market likely to slide […]
11 May Thinking in Parallel
A Parallel Expert I once rode the Trans-Siberian Railway from Moscow to Ulan-Batar, Mongolia (not the picture at right – the engines were diesel). Several times along the journey we passed slower trains, and we were passed by faster ones. When people and freight are confined to a single lane, the speed of the slowest defines the speed of […]
27 Feb Six Reasons Businesses are Implementing Semantics
The wave is coming. I spent some quality time last summer in a large consumer products manufacturer in Cincinnati helping architect semantic capabilities to automate tasks in chemical compliance for selling a large number of products in dozens of countries, each with hundreds of rules that apply to thousands of chemicals constituents. You can imagine the combinatorial explosion. […]
23 Feb Inference in Knowledge Apps
In Section 5 we discussed different kinds of knowledge, including existential or hierarchical knowledge and causal knowledge. In Section 7 we discussed modeling approaches and search techniques that could be applied to any kind of knowledge. We saw that causal knowledge can be modeled as chains of causes and effects, and that existential knowledge can be […]
28 Jan Summarization and Translation Domains
Translation as A Sample Domain For our sample domain, we need something that requires expertise, is not trivial, and about which the author knows something. This limits us significantly, so we are taking the easy way out and going with the domain of Machine Translation (MT) of human languages. We considered the intricacies of communication […]
29 Dec Unhuman Expertise
Artificial Intelligence has suffered from a persistent scale problem: up to now, many techniques have been shown to work well and reliably in narrowly defined domains, but outside the domains of their expertise, they fall apart very quickly. No techniques of which I am aware, have exhibited common sense in the way we expect humans […]
27 Dec Visualization Deception
The differences between the way computers think about things and the way humans process information can create significant dissonance and opportunities for misunderstanding. Both are in the business of finding answers, but approaches differ. While for humans, things we see with our eyes may be earliest and foremost in our thought process, it is almost always the […]
23 Dec Visual Knowledge Dimensions
Visualizing knowledge in graphs and charts empowers decision makers by giving them actionable knowledge in understandable format. To make this most effective, the labels on the graph must provide clearly defined context cues that make it easy to interpret. Converging data strategies using Big Data (Hadoop, NoSQL, Cassandra, MapReduce…) can change the way we access content […]