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Associative Query Answering via Query Feature Similarity

This system provides additional information relevant to user queries by leveraging query feature similarity. It focuses on relational and extended associative attributes to enhance search results. By utilizing a semantic model and user context, it offers tailored information based on query specifics.

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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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