otivation •Natural language is more accessible to the average user. It also relieves the user from having to know the underlying schema. •Knowledge bases contain crisp facts from which complex information needs can be answered, possibly by joining (⋈) these facts. q1 q2 q3 a writer (⋈) Casablanca played played in Who (⋈) married married to is married to was born born Rome writer bornIn bornOnDate Max_Born Born_(film) Sydne_Rome Rome White_House Casablanca Casablanca_(film) Played_(film) actedIn hasMusicalRole person Married_(series) married_person marriedTo Construct a Disambiguation Graph Perform Disambiguation 4. Joint disambiguation via a constrained dense subgraph computation using ILP. q1 q2 q3 a writer (⋈) Casablanca played in Who (⋈) is married to was born Rome writer bornIn Rome Casablanca_(film) actedIn person marriedTo Generate Query 5. Translate subgraph to structured query: classes are type constrained variables, joins expressed by common phrases between q-units. Challenges •Detection and mapping of entities, classes & relations •Joint disambiguation of entities, classes & relations •Structured query generation 1. Detect candidate phrases Rome born was born Casablanca 2. Map candidate phrases to (potential) semantic items Rome Sydne_Rome Rome 3. D
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