Abstract: A system for identifying word patterns in text is conducted in real time and is highly suitable for network and Internet use. The system comprises a semantic network that may be compiled on a local computer or at a remote host and a software text analysis module for receiving the text to be analyzed, parsing the text, submitting the text to the semantic network, and receiving the results. Recognized, words are then examined, together with surrounding words in the text to determine whether the words are part of a word pattern. Word patterns are located at nodes in the semantic network in a hierarchical structure, and certain word patterns correspond to objects of the semantic network. When all word patterns involving a word are located, links are followed to objects corresponding to the word patterns. Several nodes may point to a single object, but each object is represented only once in the semantic network.

Abstract: A method for identifying word patterns in text is conducted in real time and is highly suitable for network and Internet use. The method involves receiving a stream of text, breaking the stream of text into a plurality of threads, tokenizing the words in each thread, and comparing the words to identified words in the semantic network. Recognized, words are then examined, together with surrounding words in the text to determine whether the words are part of a word pattern. Word patterns are located at nodes in the semantic network in a hierarchical structure, and certain word patterns correspond to objects of the semantic network. When all word patterns involving a word are located, links are followed to objects corresponding to the word patterns. Several nodes may point to a single object, but each object is represented only once in the semantic network. Identified objects may thus be identified in real time, as the text streams through the text analysis module.

Abstract: A system for identifying word patterns in text is conducted in real time and is highly suitable for network and Internet use. The system comprises a semantic network that may be compiled on a local computer or at a remote host and a software text analysis module for receiving the text to be analyzed, parsing the text, submitting the text to the semantic network, and receiving the results. Recognized, words are then examined, together with surrounding words in the text to determine whether the words are part of a word pattern. Word patterns are located at nodes in the semantic network in a hierarchical structure, and certain word patterns correspond to objects of the semantic network. When all word patterns involving a word are located, links are followed to objects corresponding to the word patterns. Several nodes may point to a single object, but each object is represented only once in the semantic network.

Abstract: Systems and methods for collecting, processing, analyzing, and indexing large amounts of data in such a manner that queries can be formulated and exercised against the data in an expedient manner. Embodiments of the present invention provide for static or dynamic presentation of the indexed data based upon the queries. The data organization and access techniques applied in embodiments of the present invention are structured in a way that allows for a large variety of queries to be performed on the data without having to reorganize the data. Additionally, indexes and presentations of the data are continually updated and modified in virtually real-time.

Abstract: A semantic network is disclosed herein that is useful for identifying word patterns in text. The semantic network may be used to identify word patterns accurately and substantially in real time. The semantic network is comprised of recognized words, which form word patterns, and objects, which are identified by certain of the word patterns. The word patterns are located at nodes in the semantic network. The nodes are linked in a hierarchical structure, with varying, identifiable levels. Under the method of building a semantic network, a computer program is provided and loaded onto a computer. The software is preferably linked with a knowledge base from which identified objects can be obtained. The objects are each classified by object type as they are received. Each object has a label which corresponds to a upper level word pattern. The word pattern is parsed or “tokenized” and combinations of the words in the label are assigned as word patterns at various nodes.

Abstract: A system and method for identifying word patterns in text is conducted in real time and is highly suitable for network and Internet use. The system comprises a semantic network that may be compiled on a local computer or at a remote host and a software text analysis module for receiving the text to be analyzed, parsing the text, submitting the text to the semantic network, and receiving the results. The method involves receiving a stream of text, breaking the stream of text into a plurality of threads, tokenizing the words in each thread, and comparing the words to identified words in the semantic network. Recognized, words are then examined, together with surrounding words in the text to determine whether the words are part of a word pattern. Word patterns are located at nodes in the semantic network in a hierarchical structure, and certain word patterns correspond to objects of the semantic network.

Abstract: A semantic network is disclosed herein that is useful for identifying word patterns in text. The semantic network may be used to identify word patterns accurately and substantially in real time. The semantic network is comprised of recognized words, which form word patterns, and objects, which are identified by certain of the word patterns. The word patterns are located at nodes in the semantic network. The nodes are linked in a hierarchical structure, with varying, identifiable levels. Under the method of building a semantic network, a computer program is provided and loaded onto a computer. The software is preferably linked with a knowledge base from which identified objects can be obtained. The objects are each classified by object type as they are received. Each object has a label which corresponds to a upper level word pattern. The word pattern is parsed or “tokenized” and combinations of the words in the label are assigned as word patterns at various nodes.