Difference between homonyms and synonyms in data science with examples

Question

Please share the difference between homonyms and synonyms in data science with examples.

Answer 1

Synonyms for concepts:

When you determine that two concepts are synonyms (say, sofa and couch), you use the class expression owl:equivalentClass. The entailment here is that any instance that was a member of class sofa is now also a member of class couch and vice versa. One of the nice things about this approach is that "context" of this equivalence is automatically scoped to the ontology in which you make the equivalence statement. If you had a very small mapping ontology between a furniture ontology and an interior decorating ontology, you could say in the map that these two are equivalent. In another situation if you needed to retain the (subtle) difference between a couch and a sofa, you do that by merely not including the mapping ontology that declared them equivalent.

Homonyms for concepts:

As Led Zeppelin says, "and you know sometimes words have two meanings…" What happens when a "word" has two meanings is that we have what WordNet would call "word senses." In a particular language, a set of characters may represent more than one concept. One example is the English word "mole," for which WordNet has 6 word senses. The Semantic Web approach is to give each its own namespace; for instance, I might refer to the counterspy mole as cia:mole and the burrowing rodent as the mammal:mole. (These are shortened qnames for what would be full namespace names.) The nice thing about this is, if the CIA ever needed to refer to the rodent they could unambiguously refer to mammal:mole.

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Answer 2

Homonyms- are words that have the same sound but have different in meaning. 2. Synonyms- are words that have the same or almost the same meaning.

Answer 3

Homonyms

Machine learning algorithms are now the subject of ethical debate. Bias, in layman's terms, is a pre-formed view created before facts are known. It applies to an estimating procedure's proclivity to provide estimations or predictions that are, on average, off goal in machine learning and data mining.

A policy's strength can be measured in a variety of ways, including confidence. "Decision trees" are diagrams that show how decisions are being made and what consequences are available. Rescale a statistic to match the scale of other variables in the model to normalise it.

Confidence is a statistician's metric for determining how reliable a sample is (we are 95 percent confident that the average blood sugar in the group lies between X and Y, based on a sample of N patients). Decision tree algorithms are methods that divide data across pieces that are becoming more and more homogeneous in terms of the outcome measure as they advance.

A graph is a graphical representation of data that statisticians call plots and charts. A graph seems to be an information structure that contains the ties and links among items, according to computer programmers. The act of arranging relational databases and their columns such that table relationships are consistent is known as normalisation.

Synonyms

Statisticians use the terms record, instance, sample, or example to describe their data. In computer science and machine learning, this can be called an attribute, input variable, or feature. The term "estimation" is also used, though its use is generally limited to numeric outcomes.

Statisticians call the non-time-series data format a record, or record. In statistics, estimation more often refers to the use of a sample statistic to measure something. Predictive modelling involves developing aggregations of low-level predictors into more informative "features".

The spreadsheet format, in which each column is still a variable, so each row is a record, is perhaps the most common non-time-series data type. Modeling in machine learning and artificial intelligence often begins with some very low-level prediction data.