on the semantic mapping of schema-agnostic queries: a preliminary study
DESCRIPTION
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.TRANSCRIPT
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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
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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
![Page 3: On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study](https://reader033.vdocuments.us/reader033/viewer/2022052908/55944d601a28ab3f6f8b46d0/html5/thumbnails/3.jpg)
Outline
Goals
Semantic Tractability
Dimensions of Query-Database Semantic Heterogeneity
Definitions
Semantic Resolvability
Summary
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Motivation
QA/NLI
Q0, R0
...Q1, R1
Qn, Rn
f-measure
What is being evaluated by the test collection ?
semantic matching
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Goals
Provide a preliminary categorization on the semanticmatching (schema-agnosticism) classes.
Support a conceptual understanding on the semanticphenomena behind schema-agnostic queries.
Applications:
- Help on the design and evaluation of schema-agnostic query mechanisms
- Relevant to Question Answering and Natural Language Interfaces
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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
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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
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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
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Taxonomy of Semantic Differences
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Semantic Mapping
Query Tokens
Dataset Lexical Element
Associated Semantic Knowledge Base (M)
Query
TokenM token q
Dataset
LexiconM Σ
...
Semantic Reachability
Query-Dataset Semantic mapping:
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Semantic Resolvability
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Resolved Schema-agnostic Query
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Semantic Mapping Types
Classifies each semantic mapping
According to the semantic heterogeneity classes
Taking into account some semantic phenomena (ambiguity, vagueness)
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AP: Abstraction Process
Trivial
Lexical
Synonymic
Generalization/specialization
Conceptual
Functional/Aggregation
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PS: Predicate Structure
Predication preseving
Predication difference
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M: Semantic Knowledge Base
Self-Sufficient
Dependent on External Knolwedge Base
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SE: Semantic Evidence & Uncertainty
Absolute
Context resolvable
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CT: Context
Sufficient
Insufficient
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MC: Mapping Cardinality
1:1
1:N
N:1
M:N
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Semantic Intepretation Model
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Example
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Semantic Resolvability Classes
Easier
Harder
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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
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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
...
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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