Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.

Abstract: A reconfigurable automatic document-classification system and method provides classification metrics to a user and enables the user to reconfigure the classification model. The user can refine the classification model by adding or removing exemplars, creating, editing or deleting rules, or performing other such adjustments to the classification model. This technology enhances the overall transparency and defensibility of the auto-classification process.

Abstract: A reconfigurable automatic document-classification system and method provides classification metrics to a user and enables the user to reconfigure the classification model. The user can refine the classification model by adding or removing exemplars, creating, editing or deleting rules, or performing other such adjustments to the classification model. This technology enhances the overall transparency and defensibility of the auto-classification process.

Abstract: Content of different formats may be sourced from various data sources such as content servers and ingested into a data integration server by an ingestion broker embodied on a non-transitory computer readable medium. The ingestion broker may normalize the content of different formats into a uniform representation that can be indexed and delivered across multiple digital channels for a variety of applications. The normalized content may be analyzed and semantic metadata may be determined from the normalized content. The normalized content can be semantically enriched by associating the semantic metadata and the like with the content. The semantic metadata can be stored in a semantic index that can be used for searching via the data integration server. During search, the semantic metadata can be instantiated as facets for user navigation and refinement of search criteria and additional semantic relationships can be assigned to the words in the normalized content.

Abstract: A source content processor receives content from a crawler and calls a text mining engine. The text mining engine mines the content and provides metadata about the content. The source content processor applies a source content filtering rule to the content utilizing the metadata from the text mining engine. The source content filtering rule is previously built based on at least one of a named entity, a category, or a sentiment. The source content processor determines whether to persist the content according to a result from applying the source content filtering rule to the content and either stores the content in a data store or deletes the contents from the data ingestion pipeline such that the content is not persisted anywhere. Embodiments disclosed herein can significantly reduce the amount of irrelevant content through the data ingestion pipeline, prior to data persistence.

Abstract: A source content processor receives content from a crawler and calls a text mining engine. The text mining engine mines the content and provides metadata about the content. The source content processor applies a source content filtering rule to the content utilizing the metadata from the text mining engine. The source content filtering rule is previously built based on at least one of a named entity, a category, or a sentiment. The source content processor determines whether to persist the content according to a result from applying the source content filtering rule to the content and either stores the content in a data store or deletes the contents from the data ingestion pipeline such that the content is not persisted anywhere. Embodiments disclosed herein can significantly reduce the amount of irrelevant content through the data ingestion pipeline, prior to data persistence.

Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.

Abstract: Methods, systems and computer-readable media enable various techniques related to semantic navigation. One aspect is a technique for displaying semantically derived facets in the search engine interface. Each of the facets comprises faceted search results. Each of the faceted search results is displayed in association with user interface elements for including or excluding the faceted search result as additional search terms to subsequently refine the search query. Another aspect automatically infers new metadata from the content and from existing metadata and then automatically annotates the content with the new metadata to improve recall and navigation. Another aspect identifies semantic annotations by determining semantic connections between the semantic annotations and then dynamically generating a topic page based on the semantic connections.

Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.

Abstract: Methods, systems and computer-readable media enable various techniques related to semantic navigation. One aspect is a technique for displaying semantically derived facets in the search engine interface. Each of the facets comprises faceted search results. Each of the faceted search results is displayed in association with user interface elements for including or excluding the faceted search result as additional search terms to subsequently refine the search query. Another aspect automatically infers new metadata from the content and from existing metadata and then automatically annotates the content with the new metadata to improve recall and navigation. Another aspect identifies semantic annotations by determining semantic connections between the semantic annotations and then dynamically generating a topic page based on the semantic connections.

Abstract: Content of different formats may be sourced from various data sources such as content servers and ingested into a data integration server by an ingestion broker embodied on a non-transitory computer readable medium. The ingestion broker may normalize the content of different formats into a uniform representation that can be indexed and delivered across multiple digital channels for a variety of applications. The normalized content may be analyzed and semantic metadata may be determined from the normalized content. The normalized content can be semantically enriched by associating the semantic metadata and the like with the content. The semantic metadata can be stored in a semantic index that can be used for searching via the data integration server. During search, the semantic metadata can be instantiated as facets for user navigation and refinement of search criteria and additional semantic relationships can be assigned to the words in the normalized content.

Abstract: Content of different formats may be sourced from various data sources such as content servers and ingested into a data integration server by an ingestion broker embodied on a non-transitory computer readable medium. The ingestion broker may normalize the content of different formats into a uniform representation that can be indexed and delivered across multiple digital channels for a variety of applications. The normalized content may be analyzed and semantic metadata may be determined from the normalized content. The normalized content can be semantically enriched by associating the semantic metadata and the like with the content. The semantic metadata can be stored in a semantic index that can be used for searching via the data integration server. During search, the semantic metadata can be instantiated as facets for user navigation and refinement of search criteria and additional semantic relationships can be assigned to the words in the normalized content.

Abstract: A source content processor receives content from a crawler and calls a text mining engine. The text mining engine mines the content and provides metadata about the content. The source content processor applies a source content filtering rule to the content utilizing the metadata from the text mining engine. The source content filtering rule is previously built based on at least one of a named entity, a category, or a sentiment. The source content processor determines whether to persist the content according to a result from applying the source content filtering rule to the content and either stores the content in a data store or deletes the contents from the data ingestion pipeline such that the content is not persisted anywhere. Embodiments disclosed herein can significantly reduce the amount of irrelevant content through the data ingestion pipeline, prior to data persistence.

Abstract: A source content processor receives content from a crawler and calls a text mining engine. The text mining engine mines the content and provides metadata about the content. The source content processor applies a source content filtering rule to the content utilizing the metadata from the text mining engine. The source content filtering rule is previously built based on at least one of a named entity, a category, or a sentiment. The source content processor determines whether to persist the content according to a result from applying the source content filtering rule to the content and either stores the content in a data store or deletes the contents from the data ingestion pipeline such that the content is not persisted anywhere. Embodiments disclosed herein can significantly reduce the amount of irrelevant content through the data ingestion pipeline, prior to data persistence.

Abstract: Methods, systems and computer-readable media enable various techniques related to semantic navigation. One aspect is a technique for displaying semantically derived facets in the search engine interface. Each of the facets comprises faceted search results. Each of the faceted search results is displayed in association with user interface elements for including or excluding the faceted search result as additional search terms to subsequently refine the search query. Another aspect automatically infers new metadata from the content and from existing metadata and then automatically annotates the content with the new metadata to improve recall and navigation. Another aspect identifies semantic annotations by determining semantic connections between the semantic annotations and then dynamically generating a topic page based on the semantic connections.

Abstract: Methods, systems and computer-readable media enable various techniques related to semantic navigation. One aspect is a technique for displaying semantically derived facets in the search engine interface. Each of the facets comprises faceted search results. Each of the faceted search results is displayed in association with user interface elements for including or excluding the faceted search result as additional search terms to subsequently refine the search query. Another aspect automatically infers new metadata from the content and from existing metadata and then automatically annotates the content with the new metadata to improve recall and navigation. Another aspect identifies semantic annotations by determining semantic connections between the semantic annotations and then dynamically generating a topic page based on the semantic connections.

Abstract: A reconfigurable automatic document-classification system and method provides classification metrics to a user and enables the user to reconfigure the classification model. The user can refine the classification model by adding or removing exemplars, creating, editing or deleting rules, or performing other such adjustments to the classification model. This technology enhances the overall transparency and defensibility of the auto-classification process.

Abstract: A reconfigurable automatic document-classification system and method provides classification metrics to a user and enables the user to reconfigure the classification model. The user can refine the classification model by adding or removing exemplars, creating, editing or deleting rules, or performing other such adjustments to the classification model. This technology enhances the overall transparency and defensibility of the auto-classification process.

Abstract: A source content processor receives content from a crawler and calls a text mining engine. The text mining engine mines the content and provides metadata about the content. The source content processor applies a source content filtering rule to the content utilizing the metadata from the text mining engine. The source content filtering rule is previously built based on at least one of a named entity, a category, or a sentiment. The source content processor determines whether to persist the content according to a result from applying the source content filtering rule to the content and either stores the content in a data store or deletes the contents from the data ingestion pipeline such that the content is not persisted anywhere. Embodiments disclosed herein can significantly reduce the amount of irrelevant content through the data ingestion pipeline, prior to data persistence.

Abstract: Disclosed is a flexible and scalable artificial intelligence and analytics platform with advanced content analytics and content ingestion. Disparate contents can be ingested into a content analytics system of the platform through a content ingestion pipeline operated by a sophisticated text mining engine. Prior to persistence, editorial metadata can be extracted and semantic metadata inferred to gain insights across the disparate contents. The editorial metadata and the semantic metadata can be dynamically mapped, as the disparate contents are crawled from disparate sources, to an internal ingestion pipeline document conforming to a uniform mapping schema that specifies master metadata of interest. For persistence, the semantic metadata in the internal ingestion pipeline document can be mapped to metadata tables conforming to a single common data model of a central repository.