Abstract: Indexing data is disclosed. An image and a text data associated with a dataset are received. A tag is generated using one or more hierarchical classifiers. The image and the text data are input into at least one of the one or more hierarchical classifiers. A search index is generated based at least on the generated tag.

Abstract: Indexing data is disclosed. An image and a text data associated with a dataset are received. A tag is generated using one or more hierarchical classifiers. The image and the text data are input into at least one of the one or more hierarchical classifiers. A search index is generated based at least on the generated tag.

Abstract: Indexing data is disclosed. An image and a text data associated with a dataset are received. A tag is generated using one or more hierarchical classifiers. The image and the text data are input into at least one of the one or more hierarchical classifiers. A search index is generated based at least on the generated tag.

Abstract: Highly relevant search results for unstructured queries are efficiently identified from normalized data. A server graphs relationships between each of the data objects. For each search term, the server identifies a candidate set of data objects mapped to the term. The server calculates priority scores for the candidate data objects based at least in part on one or more of: a link analysis of the graph; or an analysis of metadata describing structural constraints upon the candidate data objects. Based on the graph, the server identifies one or more search result subgraphs, each comprising at least one data object from each of the candidate sets. The server looks for subgraphs in an order that is based on the priority scores for the candidate data objects. The server may terminate its search early, in part because prioritizing the candidate data objects increases the likelihood of receiving relevant search results first.

Abstract: Techniques are disclosed for presenting semi-structured sets of search results comprising two or more differently-structured subsets of search results. The search results are divided into two or more groups of similarly-structured results. The search results are displayed in their respective groups rather than as a single set. Each group is displayed using a different display structure, in an order determined by a group ranking mechanism. The search results within a group ordered by a result ranking mechanism. Techniques are also disclosed for enhancing a display of similarly structured data by emphasizing highly relevant result fields. The highly relevant result fields may be identified based on metadata ranking mechanisms, uniqueness of their constituent values, historical feedback, keyword location, and/or other mechanisms. The fields are emphasized using, without limitation, highlighting, reordering, and filtering of unemphasized fields from the display.

Abstract: Highly relevant search results for unstructured queries are efficiently identified from normalized data. A server graphs relationships between each of the data objects. For each search term, the server identifies a candidate set of data objects mapped to the term. The server calculates priority scores for the candidate data objects based at least in part on one or more of: a link analysis of the graph; or an analysis of metadata describing structural constraints upon the candidate data objects. Based on the graph, the server identifies one or more search result subgraphs, each comprising at least one data object from each of the candidate sets. The server looks for subgraphs in an order that is based on the priority scores for the candidate data objects. The server may terminate its search early, in part because prioritizing the candidate data objects increases the likelihood of receiving relevant search results first.

Abstract: Metadata search is enhanced by utilizing relationship data indicating relationships between metadata items. A server generates an index mapping metadata items to terms associated with the metadata items and a graph describing relationships between each of the metadata items. When the server receives a search request, the server locates a candidate set of the metadata items based on the search term(s) and the index. The server performs a link analysis of the graph to determine a relationship score for each metadata item. For each particular metadata item in the candidate set of the metadata items, the server calculates a ranking score based at least on the relationship score for the particular metadata item. The server generates a ranked result set based on comparing the ranking scores for the candidate set of metadata items. The server then provides information indicating the ranked result set in response to the search request.

Abstract: Highly relevant search results for unstructured queries are efficiently identified from normalized data. A server graphs relationships between each of the data objects. For each search term, the server identifies a candidate set of data objects mapped to the term. The server calculates priority scores for the candidate data objects based at least in part on one or more of: a link analysis of the graph; or an analysis of metadata describing structural constraints upon the candidate data objects. Based on the graph, the server identifies one or more search result subgraphs, each comprising at least one data object from each of the candidate sets. The server looks for subgraphs in an order that is based on the priority scores for the candidate data objects. The server may terminate its search early, in part because prioritizing the candidate data objects increases the likelihood of receiving relevant search results first.

Abstract: Techniques are disclosed for presenting semi-structured sets of search results comprising two or more differently-structured subsets of search results. The search results are divided into two or more groups of similarly-structured results. The search results are displayed in their respective groups rather than as a single set. Each group is displayed using a different display structure, in an order determined by a group ranking mechanism. The search results within a group ordered by a result ranking mechanism. Techniques are also disclosed for enhancing a display of similarly structured data by emphasizing highly relevant result fields. The highly relevant result fields may be identified based on metadata ranking mechanisms, uniqueness of their constituent values, historical feedback, keyword location, and/or other mechanisms. The fields are emphasized using, without limitation, highlighting, reordering, and filtering of unemphasized fields from the display.

Abstract: Highly relevant search results for unstructured queries are efficiently identified from normalized data. A server graphs relationships between each of the data objects. For each search term, the server identifies a candidate set of data objects mapped to the term. The server calculates priority scores for the candidate data objects based at least in part on one or more of: a link analysis of the graph; or an analysis of metadata describing structural constraints upon the candidate data objects. Based on the graph, the server identifies one or more search result subgraphs, each comprising at least one data object from each of the candidate sets. The server looks for subgraphs in an order that is based on the priority scores for the candidate data objects. The server may terminate its search early, in part because prioritizing the candidate data objects increases the likelihood of receiving relevant search results first.

Abstract: Metadata search is enhanced by utilizing relationship data indicating relationships between metadata items. A server generates an index mapping metadata items to terms associated with the metadata items and a graph describing relationships between each of the metadata items. When the server receives a search request, the server locates a candidate set of the metadata items based on the search term(s) and the index. The server performs a link analysis of the graph to determine a relationship score for each metadata item. For each particular metadata item in the candidate set of the metadata items, the server calculates a ranking score based at least on the relationship score for the particular metadata item. The server generates a ranked result set based on comparing the ranking scores for the candidate set of metadata items. The server then provides information indicating the ranked result set in response to the search request.

Abstract: Techniques are disclosed for presenting semi-structured sets of search results comprising two or more differently-structured subsets of search results. The search results are divided into two or more groups of similarly-structured results. The search results are displayed in their respective groups rather than as a single set. Each group is displayed using a different display structure, in an order determined by a group ranking mechanism. The search results within a group ordered by a result ranking mechanism. Techniques are also disclosed for enhancing a display of similarly structured data by emphasizing highly relevant result fields. The highly relevant result fields may be identified based on metadata ranking mechanisms, uniqueness of their constituent values, historical feedback, keyword location, and/or other mechanisms. The fields are emphasized using, without limitation, highlighting, reordering, and filtering of unemphasized fields from the display.