The document discusses problems with current search engines and proposes solutions to improve semantic search. It argues that search engines rely too heavily on words and not enough on concepts, and ignore important linguistic variables. It suggests search could be improved by interacting with users to map words to concepts, taking linguistic scope fully into account, and introducing a formal notion of semantic equivalence. The document provides examples of how semantic search might work by having a system suggest related concepts at each step to iteratively refine the search space.

1. Semantic Search
Do you know what I mean?
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2. Search is broken
1. Search engines rely heavily on words and too little on concepts
2. Search engines only use geography to limit linguistic scope
3. Seach engines ignore the linguistic scope variables of industry,
organisation, product line, scientific discipline, project
4. No formal notion of semantic equivalence
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3. Words vs concepts
“software architecture”
words are
labels
“solution architecture”
“persistent datastore”
“database”
“integration”
“program”
“program code”
“software”
“structural &
dynamic models”
“architectural design”
“architecture”
“structural &
dynamic models”
“architectural design”
concepts contain
semantic links
“architecture”
“program”
“program code”
“software”
“product”
“application”
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4. Mapping between labels and semantics
depends on linguistic scope!
“solution architecture”
“solution architecture”
Organisation A
Organisation B
“persistent datastore”
“database”
“persistent datastore”
“database”
“integration”
“structural &
dynamic models”
“structural &
dynamic models”
“architectural design”
“architectural design”
“architecture”
“architecture”
≠
“program”
“program code”
“program”
“program code”
“software”
“software”
“product”
“application”
“application”
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5. Semantic equivalence
“software”
“architecture”
=
“software”
“architecture”
“software architecture”
“database”
“integration”
“solution”
“solution architecture”
=
“software”
“architecture”
=
“software”
“architecture”
“product”
“application”
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6. Fixing search
1. Interact with user to confirm translation of words into concepts
2. Fully take into account the significance of linguistic scope, in
particular the variables of industry, organisation, product line,
scientific discipline, project
3. Introduce a formal notion of semantic equivalence
to simplify search specification and execution
PS: The W3C Semantic Web standards are not the solution
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7. Example 1
Semantic Seach
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8. 1. System: Ask the user for one or two concepts
?
Matches: ∞
?
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9. 2. User: Nominate two concepts to reduce the search space
Matches: < 10,000
car
Jorn Bettin
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10. 3. System: Suggest statistically related 2nd level concepts
Matches: < 10,000
car
?
Jorn Bettin
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11. 4. User: Select a concept to reduce the search space
Matches:
< 100
car
owner
Jorn Bettin
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12. 5. System: Suggest statistically related concepts
?
car
owner
Jorn Bettin
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Matches:
< 100

13. 6. User: Select a concept to reduce the search space
Matches:
< 10
car
owner
Victoria
Jorn Bettin
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14. 7. System: Suggest statistically related 2nd level concepts
Matches:
< 10
?
car
owner
Victoria
Jorn Bettin
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15. 8. User: Select a concept to reduce the search space
Matches:
<3
Volkswagen
car
owner
Victoria
Jorn Bettin
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16. 9. System: Suggest statistically related 3rd level concepts
Matches:
<3
Volkswagen
?
car
owner
Victoria
Jorn Bettin
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17. 10. User: Select a concept to reduce the search space
Matches:
1
Volkswagen
Golf
car
owner
Victoria
Jorn Bettin
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18. Example 2
Semantic Seach
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19. What if the name of the owner is highly ambiguous?
Matches:
<3
Volkswagen
Golf
car
owner
Victoria
John Smith
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20. 11. System: Suggest statistically related 2nd level concepts
Matches:
<3
Volkswagen
Golf
car
owner
Victoria
John Smith
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?

21. 12. User: Select a concept to reduce the search space
Australia
Volkswagen
Golf
car
owner
Victoria
John Smith
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Matches:
1

22. Example 3
Semantic Seach
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23. 1. System: Ask the user for one or two concepts
?
Matches: ∞
?
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24. 2. User: Nominate one concept to reduce the search space
car
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Matches: < 100,000,000,000

25. 3. System: Suggest statistically related concepts
Matches: < 100,000,000,000
car
?
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26. 4. User: Select or nominate a concept to reduce the search space
Matches:
< 100
car
ABC 123
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27. 5. System: Suggest statistically related 2nd level concepts
Matches:
< 100
car
?
ABC 123
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28. 6. User: Select a concept to reduce the search space
Matches:
< 10
car
number plate
ABC 123
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29. 7. System: Suggest statistically related concepts
Matches:
< 10
?
car
number plate
ABC 123
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30. 8. User: Select a concept to reduce the search space
Matches:
1
car
number plate
Victoria
ABC 123
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31. Example 4
Semantic Seach
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32. 1. System: Ask the user for one or two concepts
Matches: ∞
?
?
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33. 2. User: Nominate two concepts to reduce the search space
Matches: < 1,000,000
architecture
software
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34. 3. System: Suggest statistically related concepts
?
architecture
software
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Matches: < 1,000,000

35. 4. User: Select or nominate a concept to reduce the search space
Melbourne
Matches:
< 1,000
architecture
software
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36. 5. User: Select or nominate concept to enlarge the search space
Sydney
Melbourne
architecture
software
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Matches:
< 3,000

37. 6. System: Suggest statistically related concepts
Sydney
Melbourne
?
architecture
software
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Matches:
< 3,000

38. 7. User: Select a concept to reduce the search space
Sydney
product development
Melbourne
architecture
software
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Matches:
< 1,500

39. 8. User: Connect concepts to reduce the search space
Sydney
product development
Melbourne
Matches:
< 1,000
architecture
software
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40. 9. User: Connect concepts to reduce the search space
Sydney
product development
Melbourne
architecture
software
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Matches:
< 300

41. 10. System: Suggest statistically related 2nd level concepts
Sydney
product development
?
Melbourne
architecture
software
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Matches:
< 300

42. 11. User: Select a 2nd level concept to reduce the search space
Sydney
product development
Melbourne
architecture
Web
software
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Matches:
< 200

43. 12. System: Suggest statistically related 2nd level concepts
Sydney
product development
Melbourne
architecture
Web
software
?
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Matches:
< 200

44. 13. User: Select a 2nd level concept to reduce the search space
Sydney
product development
Melbourne
architecture
Web
software
mobile
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Matches:
< 100

45. 14. System: Suggest statistically related concepts
?
product development
Sydney
Melbourne
architecture
Web
software
mobile
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Matches:
< 100

46. 15. User: Select a concept to reduce the search space
permanent
Sydney
product development
Melbourne
architecture
Web
software
mobile
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Matches:
< 50

47. 16. System: Suggest statistically related concepts
permanent
Sydney
product development
architecture
Web
Melbourne
?
software
mobile
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Matches:
< 50

48. 17. User: Select a concept to reduce the search space
permanent
Sydney
product development
architecture
Web
Melbourne
senior
software
mobile
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Matches:
< 20

49. 18. System: Suggest statistically related 2nd level concepts
permanent
Sydney
product development
architecture
Web
Melbourne
senior
software
?
mobile
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Matches:
< 20

50. 19. User: Select a 2nd level concept to reduce the search space
permanent
Sydney
product development
architecture
Web
Melbourne
senior
software
semantic search
mobile
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Matches:
1

51. Semantic search can be fixed
Cell Platform
http://s23m.org/S23M/cell-platform.html
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52. The latest book on Domain Engineering
Domain Engineering is of considerable practical
significance, as it provides methods and techniques that
help reduce time-to-market, development costs, and
project risks on one hand, and helps improve system
quality and performance on a consistent basis on the other.
•
The most comprehensive and up-to-date work on
domain engineering
•
Covers all important technological aspects, including
software product lines, domain-specific languages, and
conceptual modeling
•
Introduces novel approaches and techniques, and
includes a wealth of pointers for further research
•
ISBN 978-3-642-36653-6, published 2013
http://www.springer.com/computer/swe/book/978-3-642-36653-6
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53. Semantic Search
S23M knows what you mean!
S23M
info @ s23m.com
Nothing beats capturing the knowledge flow
of leading domain experts to co-create
organisations & systems that are
understandable by future generations of
humans & software tools.
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