Qbase, LLC

Sanjay Boddhu

Scott Lightner

Rakesh Dave

Franz Weckesser

Robert Flagg

Joseph Becknell

A system and method for detecting and summarizing events based on data feeds from a plurality of sources. Such sources may include social media networks, text messages, news feeds among others. The system may receive raw information from such sources containing data related with possible events. Method for event detection may include pre-processing and normalizing data input from any source registered, this may also include; extracting and disambiguating events and entities, associate event and entities, correlate events and entities associated from a data input which results from a different data source, for validating/verifying an event. Subsequently, the validated/verified event may be stored in a local data storage and/or in a web-server.

Data ingestion module for event detection and increased situational awareness

A system for building a knowledge base of co-occurring features extracted from a document corpus is disclosed. The method includes a plurality of feature extraction software modules that may extract different features from each document in the corpus. The system may include a knowledge base aggregator module that may keep count of the co-occurrences of features in the different documents of a corpus and determine appropriate co-occurrences to store in a knowledge base.

Featured co-occurrence knowledge base from a corpus of documents

A system and method for detecting events based on input data from a plurality of sources. The system may receive input from a plurality of sources containing information about possible events. A method for event detection involves pre-processing and normalizing a data input from a plurality of sources, extracting and disambiguating events and entities, associate event and entities, correlate events and entities associated from a data input to results from a different data source to determine if an event has occurred, and store the detected events in a data storage.

Event detection through text analysis using dynamic self evolving/learning module

The present disclosure relates to a method for performing automated discovery of new topics from unlimited documents related to any subject domain, employing a multi-component extension of Latent Dirichlet Allocation (MC-LDA) topic models, to discover related topics in a corpus. The resulting data may contain millions of term vectors from any subject domain identifying the most distinguished co-occurring topics that users may be interested in, for periodically building new topic ID models using new content, which may be employed to compare one by one with existing model to measure the significance of changes, using term vectors differences with no correlation with a Periodic New Model, for periodic updates of automated discovery of new topics, which may be used to build a new topic ID model in-memory database to allow query-time linking on massive data-set for automated discovery of new topics.

Method of automated discovery of new topics

A method for searching for related entities using entity co-occurrence is disclosed. Embodiments of the method may be employed in any search system that may include at least one search engine, at least one entity co-occurrence knowledge base, an entity extraction module, and at least an entity indexed corpus. The method may extract and disambiguate entities from search queries by using an entity co-occurrence knowledge base, find extracted entities in an entity indexed corpus and finally present search results as related entities of interest.

Method for searching related entities through entity co-occurrence

A method for generating search suggestions of related entities based on co-occurrence and/or fuzzy score matching is disclosed. The method may be employed in a search system that may include a client/server type architecture. The search system may include a user interface for a search engine in communication with one or more server devices over a network connection. The server device may include an entity extraction module, a fuzzy-score matching module, and an entity co-occurrence knowledge base database. In one embodiment, the search system may process a partial search query from a user and present search suggestions to complete the partial query. In another embodiment, the complete search query may be used as a new search query. The search system may process the new search query, run an entity extraction, find related entities from the entity co-occurrence knowledge base, and present said related entities in a drop down list.

Search suggestions of related entities based on co-occurrence and/or fuzzy-score matching

Methods and systems for discovering and exploring feature knowledge included in large corpora are disclosed. The described systems and methods may include the application of in-memory analytics to records, where the analytic methods applied to the records and the level of precision of the methods may be dynamically selected by a user.

Method of discovering and exploring feature knowledge

A computer system and method for automated discovery of topic relatedness are disclosed. According to an embodiment, topics within documents from a corpus may be discovered by applying multiple topic identification (ID) models, such as multi-component latent Dirichlet allocation (MC-LDA) or similar methods. Each topic model may differ in a number of topics. Discovered topics may be linked to the associated document. Relatedness between discovered topics may be determined by analyzing co-occurring topic IDs from the different models, assigning topic relatedness scores, where related topics may be used for matching/linking a feature of interest. The disclosed method may have an increased disambiguation precision, and may allow the matching and linking of documents using the discovered relationships.

Method of automated discovery of topics relatedness

A method for generating search suggestions by using fuzzy-score matching and entity co-occurrence in a knowledge base is disclosed. Embodiments of the method may be employed in any search system that may include an entity extraction computer module that may perform partial entity extractions from provided search queries, a fuzzy-score matching computer module that may generate algorithms based on the type of entity extracted and perform a search against an entity co-occurrence knowledge base. The entity co-occurrence knowledge base, which may include a repository where entities may be indexed as entities to entities, entities to topics, or entities to facts among others, may return fast and accurate suggestions to the user to complete the search query. The suggestions may include alternates to the partial query provided by the user that may enhance and save time when performing searches.

Search suggestions using fuzzy-score matching and entity co-occurrence

A system and method for extracting facts from unstructured text files are disclosed. Embodiments of the disclosed system and method may receive a text file as input and perform extraction and disambiguation of entities, as well as extract topics and facts. The facts are extracted by comparing against a fact template store and associating facts with events or topics. The extracted facts are stored in a data store.

Extracting facts from unstructured text

A method for disambiguating features in unstructured text is provided. The disclosed method may not require pre-existing links to be present. The method for disambiguating features in unstructured text may use co-occurring features derived from both the source document and a large document corpus. The disclosed method may include multiple modules, including a linking module for linking the derived features from the source document to the co-occurring features of an existing knowledge base. The disclosed method for disambiguating features may allow identifying unique entities from a knowledge base that includes entities with a unique set of co-occurring features, which in turn may allow for increased precision in knowledge discovery and search results, employing advanced analytical methods over a massive corpus, employing a combination of entities, co-occurring entities, topic IDs, and other derived features.

Method for disambiguated features in unstructured text

Methods for providing in-loop validation of disambiguated features are disclosed. The disclosed methods may include disambiguating features in unstructured text that may use co-occurring features derived from both the source document and a large document corpus. The disambiguating systems may include multiple modules, including a linking on-the-fly module for linking the derived features from the source document to the co-occurring features of an existing knowledge base. The system for disambiguating features may allow identifying unique entities from a knowledge base that includes entities with a unique set of co-occurring features, which in turn may allow for increased precision in knowledge discovery and search results, employing advanced analytical methods over a massive corpus, employing a combination of entities, co-occurring entities, topic IDs, and other derived features. The disclosed method may use validation to provide input to the system for disambiguating features.

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Dayton, OH, United States of America

Sanjay Boddhu