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Associative Query Answering via Query Feature Similarity

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

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Associative Query Answering via Query Feature Similarity

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  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

  8. 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.

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