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Semantics
CSI
Unveiling the “crimes”
We give inaccurate and
misleading or ambiguous
names
We don’t provide adequate
human readable definitions of
elements
We m...
Why we give bad names
● We don’t know any other interpretations.
● We assume other interpretations are
irrelevant and that...
How to give good names
● Always contemplate an element’s name in
relative isolation and try to think all the
possible and ...
Why we define bad synonyms
● We forget or ignore that synonymy is a
vague and context dependent phenomenon.
● We mix synon...
How to define good synonyms
● Insist on meaning equivalence over mere
relatedness
● Get multiple opinions (from people and...
Why we define bad (sub-)classes
● Ambiguity of the “is a” expression
● Second-order classes not allowed
● Defining two sen...
How to define good (sub-)classes
● Check whether the instances of your
subclasses are also instances of their
superclasses...
How to handle vagueness
● Identify which of your model’s elements
have a vague meaning
● Investigate whether these element...
● Meaning accuracy
● Meaning explicitness
● Meaning agreement
Take Aways
Shared semantics are
important in Knowledge
Graph...
Currently writing a book on
semantic data modeling
To be published by O’Reilly in
September 2020
Early release available a...
From Taxonomies and Schemas to Knowledge Graphs: Part 3
From Taxonomies and Schemas to Knowledge Graphs: Part 3
From Taxonomies and Schemas to Knowledge Graphs: Part 3
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From Taxonomies and Schemas to Knowledge Graphs: Part 3

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Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started.

Part 3

From Taxonomies and Schemas to Knowledge Graphs: Part 3

  1. 1. Semantics CSI Unveiling the “crimes”
  2. 2. We give inaccurate and misleading or ambiguous names We don’t provide adequate human readable definitions of elements We model as synonyms terms that are not We model instances as subclasses We treat vague elements as crisp BAD SEMANTIC MODELING PRACTICES GIVING BAD NAMES OMITTING OR GIVING BAD HUMAN DEFINITIONS BAD SYNONYMS BAD (SUB-)CLASSES IGNORING VAGUENESS
  3. 3. Why we give bad names ● We don’t know any other interpretations. ● We assume other interpretations are irrelevant and that people will know what we mean ● We assume that the correct meaning will be inferred by the context.
  4. 4. How to give good names ● Always contemplate an element’s name in relative isolation and try to think all the possible and legitimate ways this can be interpreted by a human. ● If a name has more that one interpretations, make it more specific, even if the other interpretations are not within the domain or not very likely to occur ● Observe how the name is used in practice by your modelers, annotators, developers and users.
  5. 5. Why we define bad synonyms ● We forget or ignore that synonymy is a vague and context dependent phenomenon. ● We mix synonymy with hyponymy and semantic relatedness and similarity ● We are unaware of subtle but important differences in meaning for our particular domain or context ● We don’t document biases, assumptions and choices
  6. 6. How to define good synonyms ● Insist on meaning equivalence over mere relatedness ● Get multiple opinions (from people and data) ● If you can’t be sure that your synonyms are indeed synonyms, then don’t call them like that. ● Always document the criteria, assumptions and biases of your synonymy.
  7. 7. Why we define bad (sub-)classes ● Ambiguity of the “is a” expression ● Second-order classes not allowed ● Defining two senses in one entity ● Assuming subclass hierarchies are the same as narrower/broader hierarchies ● Misleading terminology and guidelines
  8. 8. How to define good (sub-)classes ● Check whether the instances of your subclasses are also instances of their superclasses. ● Try and express the subclass relation with the “is a kind of ” pattern instead of the “is a” and see if it makes sense. ● Check if the two classes share identity criteria ● Name and define your classes in a more accurate and clear way
  9. 9. How to handle vagueness ● Identify which of your model’s elements have a vague meaning ● Investigate whether these elements are indeed vague ● Investigate and make the vague meaning of the element as specific as possible by specifying potential dimensions and applicability contexts. ● Make sure that everyone is aware of the above that by explicitly mentioning this in the description and documentation of the element
  10. 10. ● Meaning accuracy ● Meaning explicitness ● Meaning agreement Take Aways Shared semantics are important in Knowledge Graphs ● Ambiguity ● Variety/Diversity ● Vagueness ● Semantic Change ● Bad modeling practices With several challenging ”enemies” that grow stronger in scale ● Be wary of the semantic gap ● Understand basic semantic phenomena ● Understand and avoid key semantic modeling pitfalls That we can start facing by some “simple” actions
  11. 11. Currently writing a book on semantic data modeling To be published by O’Reilly in September 2020 Early release available at O’Reilly Learning Platform from November 2019 To get news about the book as well as a free preview chapter send me an email to p.alexopoulos@gmail.com
  • GeorgeCushen

    Nov. 10, 2019

Do you have experience in data modeling, or using taxonomies to classify things, and want to upgrade to modeling knowledge graphs? This hands-on workshop with one of the leading knowledge graph practitioners will help you get started. Part 3

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