Language-model-based ranking in entity-relation graphs

ABSTRACT

We propose a language-model-based ranking approach for SPARQL-like queries on entity-relationship graphs. Our ranking model supports exact matching, approximate structure matching, and approximate matching with text predicates. We show the effectiveness of our model through examples.

REFERENCES

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INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Query formulation

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Retrieval models

General Terms:
Algorithms, Design, Languages