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On the Semantic Mapping of Schema-agnostic 
Queries: A Preliminary Study 
André Freitas, João C. Pereira da Silva, Edward Curry 
Insight Centre for Data Analytics 
NLIWoD, ISWC 2014 
Riva del Garda
On the Semantic Mapping of Schema-agnostic 
Queries: A Preliminary Study 
André Freitas, João C. Pereira da Silva, Edward Curry 
Insight Centre for Data Analytics 
NLIWoD, ISWC 2014 
Riva del Garda
Outline 
 Goals 
 Semantic Tractability 
 Dimensions of Query-Database Semantic Heterogeneity 
 Definitions 
 Semantic Resolvability 
 Summary
Motivation 
What is being evaluated by the test collection ? 
semantic matching 
QA/NLI 
Q0, R0 
Q1, R1 
... 
Qn, Rn 
f-measure
Goals 
 Provide a preliminary categorization on the semantic 
matching (schema-agnosticism) classes. 
 Support a conceptual understanding on the semantic 
phenomena behind schema-agnostic queries. 
 Applications: 
- Help on the design and evaluation of schema-agnostic 
query mechanisms 
- Relevant to Question Answering and Natural 
Language Interfaces
Semantic Tractability 
 Popescu et al. (2003) 
Towards a Theory of Natural Language Interfaces to Databases 
 Definition focuses on soundness and completeness 
conditions for mapping Natural Language Queries to Database 
elements
Semantic Tractability 
 Leaves many queries outside the tractability scope 
 Conditions: 
- Query-Database syntactic isomorphism 
- Explicit and unambiguous synonymic mapping 
 Goal is to provide an all inclusive categorization system
Dimensions of Query-Database Semantic 
Heterogeneity 
Methodology for the creation of a taxonomy of lexico-semantic 
differences 
 Listing of concepts expressed in the existing semantic 
heterogeneity taxonomies 
- George, 2005 
- Colomb, 1997 
- Parent & Spaccapietra, 1998 
- Kashyap & Sheth, 1996 
 Elimination of concepts which were not relevant in the context of 
the query-database semantic differences 
 Merging and renaming of equivalent concepts
Taxonomy of Semantic Differences
Semantic Mapping 
 Query Tokens 
 Dataset Lexical Element 
 Associated Semantic Knowledge Base (M) 
Query 
Token 
Semantic Reachability 
M token q 
Dataset 
Lexicon 
M Σ 
... 
Query-Dataset Semantic mapping:
Semantic Resolvability
Resolved Schema-agnostic Query
Semantic Mapping Types 
 Classifies each semantic mapping 
 According to the semantic heterogeneity classes 
 Taking into account some semantic phenomena 
(ambiguity, vagueness)
AP: Abstraction Process 
 Trivial 
 Lexical 
 Synonymic 
 Generalization/specialization 
 Conceptual 
 Functional/Aggregation
PS: Predicate Structure 
 Predication preseving 
 Predication difference
M: Semantic Knowledge Base 
 Self-Sufficient 
 Dependent on External Knolwedge Base
SE: Semantic Evidence & Uncertainty 
 Absolute 
 Context resolvable
CT: Context 
 Sufficient 
 Insufficient
MC: Mapping Cardinality 
 1:1 
 1:N 
 N:1 
 M:N
Semantic Intepretation Model
Example
Semantic Resolvability Classes 
Easier 
Harder
Example test collection analysis 
Test collection X 
 Has 4 distinct semantic resolvability classes 
 50% are trivial mappings 
 23% are lexical mappings 
 27% are synonymic mappings 
 100% of the predicates are structure preserving 
 100% of the mapping cardinalities are 1:1
Example system evaluation 
System Y 
 Addresses 5 out of 10 semantic resolvability classes 
 (AP=conceptual, PS=*, MC=1:1, SE=*, M=*, CT=*) 
- map = 0.51, recall = 0.7 
...
Summary 
 NLI/QA Systems have semantic matching (schema-agnosticism) 
at its center 
 The proposed categorization can be used for a more principled 
interpretation of the results of NLI/QA systems 
 ... and also on which dimensions evaluation campaigns actually 
measure 
 It supports deeper comparative analysis 
 Future work includes the categorization of the QALD test 
collection

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On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study

  • 1. On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study André Freitas, João C. Pereira da Silva, Edward Curry Insight Centre for Data Analytics NLIWoD, ISWC 2014 Riva del Garda
  • 2. On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study André Freitas, João C. Pereira da Silva, Edward Curry Insight Centre for Data Analytics NLIWoD, ISWC 2014 Riva del Garda
  • 3. Outline  Goals  Semantic Tractability  Dimensions of Query-Database Semantic Heterogeneity  Definitions  Semantic Resolvability  Summary
  • 4. Motivation What is being evaluated by the test collection ? semantic matching QA/NLI Q0, R0 Q1, R1 ... Qn, Rn f-measure
  • 5. Goals  Provide a preliminary categorization on the semantic matching (schema-agnosticism) classes.  Support a conceptual understanding on the semantic phenomena behind schema-agnostic queries.  Applications: - Help on the design and evaluation of schema-agnostic query mechanisms - Relevant to Question Answering and Natural Language Interfaces
  • 6. Semantic Tractability  Popescu et al. (2003) Towards a Theory of Natural Language Interfaces to Databases  Definition focuses on soundness and completeness conditions for mapping Natural Language Queries to Database elements
  • 7. Semantic Tractability  Leaves many queries outside the tractability scope  Conditions: - Query-Database syntactic isomorphism - Explicit and unambiguous synonymic mapping  Goal is to provide an all inclusive categorization system
  • 8. Dimensions of Query-Database Semantic Heterogeneity Methodology for the creation of a taxonomy of lexico-semantic differences  Listing of concepts expressed in the existing semantic heterogeneity taxonomies - George, 2005 - Colomb, 1997 - Parent & Spaccapietra, 1998 - Kashyap & Sheth, 1996  Elimination of concepts which were not relevant in the context of the query-database semantic differences  Merging and renaming of equivalent concepts
  • 9. Taxonomy of Semantic Differences
  • 10. Semantic Mapping  Query Tokens  Dataset Lexical Element  Associated Semantic Knowledge Base (M) Query Token Semantic Reachability M token q Dataset Lexicon M Σ ... Query-Dataset Semantic mapping:
  • 13. Semantic Mapping Types  Classifies each semantic mapping  According to the semantic heterogeneity classes  Taking into account some semantic phenomena (ambiguity, vagueness)
  • 14. AP: Abstraction Process  Trivial  Lexical  Synonymic  Generalization/specialization  Conceptual  Functional/Aggregation
  • 15. PS: Predicate Structure  Predication preseving  Predication difference
  • 16. M: Semantic Knowledge Base  Self-Sufficient  Dependent on External Knolwedge Base
  • 17. SE: Semantic Evidence & Uncertainty  Absolute  Context resolvable
  • 18. CT: Context  Sufficient  Insufficient
  • 19. MC: Mapping Cardinality  1:1  1:N  N:1  M:N
  • 23. Example test collection analysis Test collection X  Has 4 distinct semantic resolvability classes  50% are trivial mappings  23% are lexical mappings  27% are synonymic mappings  100% of the predicates are structure preserving  100% of the mapping cardinalities are 1:1
  • 24. Example system evaluation System Y  Addresses 5 out of 10 semantic resolvability classes  (AP=conceptual, PS=*, MC=1:1, SE=*, M=*, CT=*) - map = 0.51, recall = 0.7 ...
  • 25. Summary  NLI/QA Systems have semantic matching (schema-agnosticism) at its center  The proposed categorization can be used for a more principled interpretation of the results of NLI/QA systems  ... and also on which dimensions evaluation campaigns actually measure  It supports deeper comparative analysis  Future work includes the categorization of the QALD test collection