1. Associative Query Answering via Query Feature Similarity

2. Outline Associative Query Answering Approach and system overview
Database schema and semantic model
Query feature and similarity
Searching associative attributes from case bases
Conclusions

3. Associative Query Answering
To provide additional relevant information to the queries that:
not explicitly asked
user does not know how to ask

4. Examples “List airports in Tunisia that can land a C-5 cargo plane.”
Associative information depends on user type
Planner: railway facility near the airports.
Pilot: runway condition and weather condition.
“Tourists ask visitor’s information about a city.”
Additional information depends on the selection condition.
Florida: hurricanes
California: earthquakes

5. Associative Information
For a relational query:
Simple associative attributes: attributes of relations in the query
Extended associative attributes: attributes of relations introduced to the query by joins
Statistical associative information: aggregate functions related to the entity in the query
We focus on the first two types

6. Approach Similar case searching based on:
User type: case bases are separate for user types
Query context: query features
Similarity measure: based on domain knowledge represented by semantic model.

7. Property of Semantic Model
Semantic model derived from database schema
As a directed graph
Nodes: entities and complex associations
Edges: relationships among nodes, including user-defined
Weights on edges express the relative information of content
Nodes have equal weights

20. Conclusions
Query feature vector as a query representation for similarity matching.
Query topic
Output attribute list
Selection constraints
Developed a new type of semantic model constructed from database schema, user types, and user-defined relationships.
Methods to evaluate similarity measure of case base queries based on query feature vector via the semantic model.