Abstract: A framework for a question and answering (Q&A) system defines a mapping of recognized semantics of user questions, to a well structured query model that can be executed on arbitrary data warehouses. Embodiments may utilize a plugin-based architecture, with various elements responsible for: extracting information from a user's question, formulating and executing a structured query, and post-processing a result by rendering a chart. Plugins within a certain processing step may be executed independently of one another, imparting a significant degree of parallelism. The framework may build on top of natural language processing technologies, and in particular embodiments may be based upon established standards (e.g. RDF and SparQL) thereby allowing adaptation to a variety of domains and use cases.

Abstract: A framework for a question and answering (Q&A) system defines a mapping of recognized semantics of user questions, to a well structured query model that can be executed on arbitrary data warehouses. Embodiments may utilize a plugin-based architecture, with various elements responsible for: extracting information from a user's question, formulating and executing a structured query, and post-processing a result by rendering a chart. Plugins within a certain processing step may be executed independently of one another, imparting a significant degree of parallelism. The framework may build on top of natural language processing technologies, and in particular embodiments may be based upon established standards (e.g. RDF and SparQL) thereby allowing adaptation to a variety of domains and use cases.

Abstract: Methods and systems are described that involve recognizing complex entities from text documents with the help of structured data and Natural Language Processing (NLP) techniques. In one embodiment, the method includes receiving a document as input from a set of documents, wherein the document contains text or unstructured data. The method also includes identifying a plurality of text segments from the document via a set of tagging techniques. Further, the method includes matching the identified plurality of text segments against attributes of a set of predefined entities. Lastly, a best matching predefined entity is selected for each text segment from the plurality of text segments. In one embodiment, the system includes a set of documents, each document containing text or unstructured data. The system also includes a database storage unit that stores a set of predefined entities, wherein each entity contains a set of attributes.

Abstract: Embodiments of the present invention include a computer-implemented method of extracting information. In one embodiment, the present invention comprises defining a plurality of reusable operators, wherein each operator performs a predefined information extraction task different from the other operators. Composite annotators may be created by specifying a composition of the reusable operators. Each operator may receive a searchable item, such as a web page or an annotation, and may generate one or more output annotations. The output annotations may be further processed by other reusable operators and the annotations may be stored in a repository for use during a search.

Abstract: Methods and systems are described that involve recognizing complex entities from text documents with the help of structured data and Natural Language Processing (NLP) techniques. In one embodiment, the method includes receiving a document as input from a set of documents, wherein the document contains text or unstructured data. The method also includes identifying a plurality of text segments from the document via a set of tagging techniques. Further, the method includes matching the identified plurality of text segments against attributes of a set of predefined entities. Lastly, a best matching predefined entity is selected for each text segment from the plurality of text segments. In one embodiment, the system includes a set of documents, each document containing text or unstructured data. The system also includes a database storage unit that stores a set of predefined entities, wherein each entity contains a set of attributes.

Abstract: Embodiments of the present invention include a computer-implemented method of extracting information. In one embodiment, the present invention comprises defining a plurality of reusable operators, wherein each operator performs a predefined information extraction task different from the other operators. Composite annotators may be created by specifying a composition of the reusable operators. Each operator may receive a searchable item, such as a web page or an annotation, and may generate one or more output annotations. The output annotations may be further processed by other reusable operators and the annotations may be stored in a repository for use during a search.