Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
On modeling information retrieval with probabilistic inference
ACM Transactions on Information Systems (TOIS)
18th International Conference on Research Development in Information Retrieval
Probabilistic Datalog—a logic for powerful retrieval methods
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Probability kinematics in information retrieval
SIGIR '95 Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval
Retrieval of complex objects using a four-valued logic
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Networked information retrieval
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Journal of the American Society for Information Science and Technology - Mathematical, logical, and formal methods in information retrieval
| Hi-index | 0.00 |
Traditional Information Retrieval (IR) considers documents as atomic units. In this paper, we show the retrieval of the components of the documents which satisfy best the information need. This finer granularity eases the browsing of the retrieval result. The approach supports multimedia and networked IR since multimedia documents are composed of other objects and networks combine several collections comprising the documents. We gain a unified viewon networks, databases, and multimedia documents by considering them as complex objects - retrieval among a heterogeneous document corpus can be modeled appropriately. We present a probabilistic retrieval function where the initial estimation of probabilistic parameters is based on the logical structure of documents and the retrieval process is described as probabilistic logical inference. Probabilistic parameters and the retrieval process are represented in probabilistic Datalog programs which are executed by HySpirita system for processing probabilistic inference.