The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic or vocabulary-independent, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. Despite being a central requirement across natural language interfaces and entity search, there is a lack on the conceptual analysis of schema-agnosticism and on the associated semantic differences between queries and databases. This work aims at providing an initial conceptualization for schema-agnostic queries aiming at providing a fine-grained classification which can support the scoping, evaluation and development of semantic matching approaches for schema-agnostic queries.

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:

11. Semantic Resolvability

12. Resolved Schema-agnostic Query

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

20. Semantic Intepretation Model

21. Example

22. Semantic Resolvability Classes
Easier
Harder

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