Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
An efficient context-free parsing algorithm
An efficient context-free parsing algorithm
Accurate unlexicalized parsing
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
MASQUE/SQL: an efficient and portable natural language query interface for relational databases
IEA/AIE'93 Proceedings of the 6th international conference on Industrial and engineering applications of artificial intelligence and expert systems
Bayesian inference for layer representation with mixed Markov random field
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
Q2Semantic: a lightweight keyword interface to semantic search
ESWC'08 Proceedings of the 5th European semantic web conference on The semantic web: research and applications
Fast forensic video event retrieval using geospatial computing
Proceedings of the 1st International Conference and Exhibition on Computing for Geospatial Research & Application
| Hi-index | 0.00 |
Recent advances in computer vision and artificial intelligence algorithms have allowed automatic extraction of metadata from video. This metadata can be represented by using the RDF/OWL ontology which can encode scene objects and their relationships in an unambiguous and well-formed manner. The encoded data can be queried using SPARQL. However, SPARQL has a steep learning curve and cannot be directly utilized by a general user for video content search. In this paper, we propose a method to bridge this gap by automatically translating user provided natural language query into an ontology-based SPARQL query for semantic video search. The proposed method consists of three major steps. First, semantically labeled training corpus of natural language query sentences is used for learning the Semantic Stochastic Context Free Grammar (SSCFG). Second, given a user provided natural language query sentence, we use the Earley-Stolcke parsing algorithm to determine the maximum likelihood semantic parsing of the query sentence. This parsing infers the semantic meaning for each word in the query sentence from which the SPARQL query is constructed. Third, the SPARQL query is executed to retrieve relevant video segments from the RDF-OWL video content database. The method is evaluated by running natural language queries on surveillance videos from maritime and land-based domains, though the framework itself is general and extensible to search videos from other domains.