Abstract: A method assigns importance ranks to documents within repositories or databases, such as any database of documents such as books or other printed material, electronic documentation, and pages within the world-wide web. The method uses a corpus of indexed documents that has been annotated to the terms of one or more ontologies in order to assign a semantic similarity score to queries based on terms taken from the ontologies. A statistical model is used to test the significance of matches between query terms and documents or categories. The method results in an acceleration of over 10,000-fold for realistic queries and ontologies, and makes it practicable to calculate P-values dynamically or to keep database annotations and the related P-value distributions up to date by frequent recalculation.

Abstract: Systems and methods are provided for processor-assisted indexing and searching, in which categories or individual documents within a database are annotated to the terms of an ontology, searched and then prioritized by rank. Items of the domain of interest, as well as the ontologies that describe attributes of those items, are embedded into a Bayesian network. Search queries correspond to user activations of items. An error model mimics incomplete or falsely specific items. Search results correspond to a subset of the items to which the query items are semantically-related. A probabilistic inference is performed to obtain marginal probabilities of each search result to explain the presence of the queries. These probabilities can then be used for ranking the search results.

Abstract: Systems and methods are provided for processor-assisted indexing and searching, in which categories or individual documents within a database are annotated to the terms of an ontology, searched and then prioritized by rank. Items of the domain of interest, as well as the ontologies that describe attributes of those items, are embedded into a Bayesian network. Search queries correspond to user activations of items. An error model mimics incomplete or falsely specific items. Search results correspond to a subset of the items to which the query items are semantically-related. A probabilistic inference is performed to obtain marginal probabilities of each search result to explain the presence of the queries. These probabilities can then be used for ranking the search results.

Abstract: A method assigns importance ranks to documents within repositories or databases, such as any database of documents such as books or other printed material, electronic documentation, and pages within the world-wide web. The method uses a corpus of indexed documents that has been annotated to the terms of one or more ontologies in order to assign a semantic similarity score to queries based on terms taken from the ontologies. A statistical model is used to test the significance of matches between query terms and documents or categories. The method results in an acceleration of over 10,000-fold for realistic queries and ontologies, and makes it practicable to calculate P-values dynamically or to keep database annotations and the related P-value distributions up to date by frequent recalculation.