Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
Terminological Representation, Natural Language & Relation Algebra
GWAI '92 Proceedings of the 16th German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Annotating the semantic web using natural language
NLPXML '02 Proceedings of the 2nd workshop on NLP and XML - Volume 17
AquaLog: an ontology-portable question answering system for the semantic web
ESWC'05 Proceedings of the Second European conference on The Semantic Web: research and Applications
The virtual tele-tASK professor: semantic search in recorded lectures
Proceedings of the 38th SIGCSE technical symposium on Computer science education
Towards to an automatic semantic annotation for multimedia learning objects
Proceedings of the international workshop on Educational multimedia and multimedia education
Question answering from lecture videos based on an automatic semantic annotation
Proceedings of the 13th annual conference on Innovation and technology in computer science education
Question Answering from Lecture Videos Based on Automatically-Generated Learning Objects
ICWL '08 Proceedings of the 7th international conference on Advances in Web Based Learning
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Hi-index | 0.00 |
Our project is about an e-librarian service which is able to retrieve multimedia resources from a knowledge base in a more efficient way than by browsing through an index or by using a simple keyword search. The user can formulate a complete question in natural language and submit it to the semantic search engine. However, natural language is not a formal language and thus can cause ambiguities in the interpretation of the sentence. Normally, the correct interpretation can only be retrieved accurately by putting each word in the context of a complete question. In this paper we present an algorithm which is able to resolve ambiguities in the semantic interpretation of NL questions. As the required input, it takes a linguistic pre-processed question and translates it into a logical and unambiguous form, i.e. $\mathcal{ALC}$ terminology. The focus function resolves ambiguities in the question; it returns the best possible interpretation for a given word in the context of the complete user question. Finally, pertinent documents can be retrieved from the knowledge base. We report on a benchmark test with a prototype that confirms the reliability of our algorithm. From 229 different user questions, the system returned the right answer for 97% of the questions, and only one answer, i.e. the best one, for nearly half of the questions.