Abstract: A method for determining relevant search results includes provided a searchable database containing a plurality of source documents with corresponding citations. The source documents and the corresponding citations have keywords. The keywords are extracted from the source documents and citations using a parser and are stored in the searchable database in association with the source documents and citations. The citations are linked to their source documents in a graph databased based upon the keywords shared between them.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: A method for determining relevant search results includes provided a searchable database containing a plurality of source documents with corresponding citations. The source documents and the corresponding citations have keywords. The keywords are extracted from the source documents and citations using a parser and are stored in the searchable database in association with the source documents and citations. The citations are linked to their source documents in a graph databased based upon the keywords shared between them.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: In an environment containing big data, noisy data, and/or unstructured data, it is desirable to identify an entity referenced by input data. The entity can be identified by generating records corresponding to characteristics of the entity based on the input data. These records can be merged when it is determined that more than one record corresponds to the same entity. By doing so it is possible to more easily identify and classify information related to an entity, though such information may have been obtained in a manner that might otherwise be deemed unstructured or noisy. The method can be applied across large sets of data (“big data”) to obtain meaning from data that may otherwise be unclassifiable to a human observer.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.

Abstract: Systems and methods are described herein for extrapolating trends in trust scores. A trust score may reflect the trustworthiness, reputation, membership, status, and/or influence of the entity in a particular community or in relation to another entity. An entity's trust score may be calculated based on data from a variety of data sources, and this data may be updated periodically as data is updated and new data becomes available. However, it may be difficult to update a trust score for an entity due to a scarcity of information. The trust score for such entities may be updated based on trends observed for the updated trust scores of other entities over a similar period of time. In this manner, trust scores may be updated for entities for which updated data is not available.