SIOC Post profile for "AI3:::Adaptive Information"
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primary topic: A Primer on Knowledge Statistics
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A Primer on Knowledge Statistics
type: Post
has container: weblog
has creator: Mike Bergman
maker: Mike Bergman
created: 2015-05-18T05:09:24Z
content: Trying to Cut Through the Terminology Confusion and Offer Simple Guidelines Download PDF Semantics is a funny thing. All professionals come to know that communication with their peers and outside audiences requires accuracy in how to express things. Yet, even with such attentiveness, communications sometimes go awry. It turns out that background, perspective and context can all act to switch circuits at the point of communication. Despite, and probably because of, our predilection as a species to classify and describe things, all from different viewpoints, we can often exhort in earnest a thought that is communicated to others as something different from what we intended. Alas! This reality is why, I suspect, we have embraced as a species things like dictionaries, thesauri, encyclopedias, specifications, standards, sacred tracts, and such, in order to help codify what our expressions mean in a given context. So, yes, while sometimes there is sloppiness in language and elocution, many misunderstandings between parties are also a result of difference in context and perspective. It is important when we process information in order to identify relations or extract entities, to type them or classify them, or to fill out their attributes, that we have measures to gauge how well our algorithms and tests work, all attentive to providing adequate context and perspective. These very same measures can also tell us whether our attempts to improve them are working or not. These measures, in turn, also are the keys for establishing effective gold standards and creating positive and negative training sets for machine learning. Still, despite their importance, these measures are not always easy to explain or understand. And, truth is, sometimes these measures may also be mis-explained or mis-calculated. Aiding the understanding of important measures in improving the precision, completeness, and accuracy of communications is my purpose in this article. Some Basic Statistics as Typic…
encoded: Trying to Cut Through the Terminology Confusion and Offer Simple Guidelines Download PDF Semantics is a funny thing. All professionals come to know that communication with their peers and outside audiences requires accuracy in how to express things. Yet, even with such attentiveness, communications sometimes go awry. It turns out that background, perspective and context can all act to switch circuits at the point of communication. Despite, and probably because of, our predilection as a species to classify and describe things, all from different viewpoints, we can often exhort in earnest a thought that is communicated to others as something different from what we intended. Alas! This reality is why, I suspect, we have embraced as a species things like dictionaries, thesauri, encyclopedias, specifications, standards, sacred tracts, and such, in order to help codify what our expressions mean in a given context. So, yes, while sometimes there is sloppiness in language and elocution, many misunderstandings between parties are also a result of difference in context and perspective. It is important when we process information in order to identify relations or extract entities, to type them or classify them, or to fill out their attributes, that we have measures to gauge how well our algorithms and tests work, all attentive to providing adequate context and perspective. These very same measures can also tell us whether our attempts to improve them are working or not. These measures, in turn, also are the keys for establishing effective gold standards and creating positive and negative training sets for machine learning. Still, despite their importance, these measures are not always easy to explain or understand. And, truth is, sometimes these measures may also be mis-explained or mis-calculated. Aiding the understanding of important measures in improving the precision, completeness, and accuracy of communications is my purpose in this article. Some Basic Statistics as Typic…
topic: Artificial Intelligence
topic: Big Structure
topic: Information Automation
topic: Knowledge-based Artificial Intelligence
topic: Linked Data
topic: Semantic Web
links to: Download PDF
links to: statistical hypothesis testing
links to: null hypothesis
links to: statistical model
links to: normal
links to: power law
links to: many others
links to: statistical test(s)
links to: statistical power
links to: random
links to: significance
links to: variance
links to: confidence interval
links to: two types of errors
links to: [1]
links to: information retrieval
links to: natural language processing
links to: machine learning
links to: Structured Dynamics
links to: [2]
links to: [3]
links to: statistical classification
links to: binary classification
links to: gold standard
links to: confusion matrix
links to: sensitivity
links to: recall
links to: true positive rate
links to: Precision
links to: positive predictive value
links to: harmonic mean
links to: [4]
links to: accuracy
links to: , as do a variety of
links to: predictive values
links to: likelihood ratios
links to: Relevance
links to: prevalence
links to: information entropy
links to: statistical inference
links to: pointwise mutual information
links to: variation of information
links to: uncertainty coefficients
links to: information gain
links to: AUC
links to: ROC
links to: [5]
links to: True positive
links to: False discovery rate
links to: False omission rate
links to: Negative predictive value
links to: False positive rate
links to: Positive likelihood ratio
links to: F-score
links to: False negative rate
links to: True negative rate
links to: Negative likelihood ratio
links to: [6]
links to: knowledge-based artificial intelligence
links to: Type II error
links to: How Are Precision and Recall Calculated
links to: Effective Distant Supervision for End-To-End Knowledge Base Population Systems
links to: Template:DiagnosticTesting Diagram
links to: On how to perform a gold standard based evaluation of ontology learning

Mike Bergman
type: User

Mike Bergman
type: Person

statistical model
label: binomial

normal
label: Gaussian

random
label: statistical sampling

two types of errors
label: Type I error
label: Type II error

information retrieval
label: Fall-out

gold standard
label: Gold standard

sensitivity
label: specificity
label: Sensitivity
label: Specificity

true positive rate
label: True positive rate

Precision
label: Recall

positive predictive value
label: Positive predictive value

accuracy
label: Accuracy

, as do a variety of
label: total population
label: Total population

prevalence
label: Prevalence

True positive
label: False positive
label: False negative
label: True negative