Abstract: A contextual analysis engine systematically extracts, analyzes and organizes digital content stored in an electronic file such as a webpage. Content can be extracted using a text extraction module which is capable of separating the content which is to be analyzed from less meaningful content such as format specifications and programming scripts. The resulting unstructured corpus of plain text can then be passed to a text analytics module capable of generating a structured categorization of topics included within the content. This structured categorization can be organized based on a content topic ontology which may have been previously defined or which may be developed in real-time. The systems disclosed herein optionally include an input/output interface capable of managing workflows of the text extraction module and the text analytics module, administering a cache of previously generated results, and interfacing with other applications that leverage the disclosed contextual analysis services.

Abstract: A contextual analysis engine systematically extracts, analyzes and organizes digital content stored in an electronic file such as a webpage. Content can be extracted using a text extraction module which is capable of separating the content which is to be analyzed from less meaningful content such as format specifications and programming scripts. The resulting unstructured corpus of plain text can then be passed to a text analytics module capable of generating a structured categorization of topics included within the content. This structured categorization can be organized based on a content topic ontology which may have been previously defined or which may be developed in real-time. The systems disclosed herein optionally include an input/output interface capable of managing workflows of the text extraction module and the text analytics module, administering a cache of previously generated results, and interfacing with other applications that leverage the disclosed contextual analysis services.

Abstract: Techniques for generating a query statement to query a dataset may be provided. For example, the query statement can be generated from natural language input, such as a natural language utterance. To do so, the input can be analyzed to detect a sentence, identify words in the sentence, and tag the words with the corresponding word types (e.g., nouns, verbs, adjectives, etc.). Expressions using the tags can be generated. Data about the expressions can be inputted to a classifier. Based on a detected pattern associated with the expressions, the classifier can predict a structure of the query statement, such as what expressions correspond to what clauses of the query statement. Based on this prediction, words associated with the expressions can be added to the clauses to generate the query statement and accordingly query the dataset.

Abstract: Techniques for organizing knowledge about a dataset storing data from or about multiple sources may be provided. For example, the data can be accessed from the multiple sources and categorized based on the data type. For each data type, a triple extraction technique specific to that data type may be invoked. One set of techniques can allow the extraction of triples from the data based on natural language-based rules. Another set of techniques can allow a similar extraction based on logical or structural-based rules. A triple may store a relationship between elements of the data. The extracted triples can be stored with corresponding identifiers in a list. Further, dictionaries storing associations between elements of the data and the triples can be updated. The list and the dictionaries can be used to return triples in response to a query that specifies one or more elements.

Abstract: Techniques are disclosed for using natural language processing techniques to define, manipulate, and interact with consumer segmentations. In such embodiments a content consumption analytics engine can be configured to receive and process a natural language segmentation query. The query may comprise, for example, a command that defines a new segmentation, a command that manipulates existing segmentations, or a command that solicits information relating to existing consumer segmentations. The query is parsed to identify individual grammatical tokens which are then correlated with specific segment token types through the use of a token repository. A custom thesaurus is used to identify synonymous terms for grammatical tokens which may not exist in the token repository. User feedback enables the custom thesaurus to learn additional synonyms for future use.

Abstract: A computer-implemented method for providing performance indicators of destination documents includes identifying a referral document to a destination document, where the referral document comprising a source of at least one visitor to the destination document. The method also includes extracting referral keywords from content of the referral document, the referral keywords corresponding to a referral context of the referral document. The method further includes determining a degree of correlation between the referral document and the destination document based on a comparison between the referral keywords and destination keywords, the destination keywords corresponding to destination context of the destination document.

Abstract: User data and a plurality of micro-segment definitions are received. Each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign. Further, a micro-segment parser parses each micro-segment definition from the plurality of micro-segment definitions into a plurality of parsed expression segments that indicate a plurality of micro-segment condition rules. In addition, a compiler compiles the plurality of parsed expression segments into an executable object that indicates a plurality of instructions to determine if the user data matches the plurality of micro-segment definitions. Each micro-segment definition is also serially processed, with a sequential evaluation engine, to apply the plurality of micro-segment condition rules to the user data to determine a match of a user belonging to a micro-segment. Further, the sequential evaluation engine assigns a score to indicate the strength of each match.

Abstract: A computer environment includes an editor application enabling generation of a specification (e.g. a graphic description) defining attributes of a navigable semantic network. An example navigable semantic network as defined by the specification can include multiple resource nodes and corresponding links defining relationships amongst the resource nodes. In addition to defining nodes and relationships, the specification can define display rules, navigation rules, etc. associated with the navigable semantic network. The computer environment can further include a compiler, a view instantiation engine, and a presentation engine. The compiler compiles the specification into a semantic network model. The view instantiation engine instantiates different portions of a semantic network as defined by the semantic network model. The presentation engine initiates display of the instantiated portion of the semantic network.

Abstract: A method, apparatus and computer program product for performing targeted advertising in documents is presented. A document is identified as having advertisements associated therewith. A statistical ranking of terms derived from said document is received and at least one term is selected from the results to use as a keyword for associating at least one advertisement with the document. The at least one term is stored with the document. When the document is viewed, the document is identified as being enabled to have advertisements associated therewith. The at least one stored term is retrieved from the document and is submitted to an advertisement aggregator. At least one advertisement is received from the advertisement aggregator and is rendered with the document.

Abstract: A contextual analysis engine systematically extracts, analyzes and organizes digital content stored in an electronic file such as a webpage. Content can be extracted using a text extraction module which is capable of separating the content which is to be analyzed from less meaningful content such as format specifications and programming scripts. The resulting unstructured corpus of plain text can then be passed to a text analytics module capable of generating a structured categorization of topics included within the content. This structured categorization can be organized based on a content topic ontology which may have been previously defined or which may be developed in real-time. The systems disclosed herein optionally include an input/output interface capable of managing workflows of the text extraction module and the text analytics module, administering a cache of previously generated results, and interfacing with other applications that leverage the disclosed contextual analysis services.

Abstract: A contextual analysis engine systematically extracts, analyzes and organizes digital content stored in an electronic file such as a webpage. Content can be extracted using a text extraction module which is capable of separating the content which is to be analyzed from less meaningful content such as format specifications and programming scripts. The resulting unstructured corpus of plain text can then be passed to a text analytics module capable of generating a structured categorization of topics included within the content. This structured categorization can be organized based on a content topic ontology which may have been previously defined or which may be developed in real-time. The systems disclosed herein optionally include an input/output interface capable of managing workflows of the text extraction module and the text analytics module, administering a cache of previously generated results, and interfacing with other applications that leverage the disclosed contextual analysis services.

Abstract: A contextual analysis engine systematically extracts, analyzes and organizes digital content stored in an electronic file such as a webpage. Content can be extracted using a text extraction module which is capable of separating the content which is to be analyzed from less meaningful content such as format specifications and programming scripts. The resulting unstructured corpus of plain text can then be passed to a text analytics module capable of generating a structured categorization of topics included within the content. This structured categorization can be organized based on a content topic ontology which may have been previously defined or which may be developed in real-time. The systems disclosed herein optionally include an input/output interface capable of managing workflows of the text extraction module and the text analytics module, administering a cache of previously generated results, and interfacing with other applications that leverage the disclosed contextual analysis services.

Abstract: A computer environment includes an editor application enabling generation of a specification (e.g. a graphic description) defining attributes of a navigable semantic network. An example navigable semantic network as defined by the specification can include multiple resource nodes and corresponding links defining relationships amongst the resource nodes. In addition to defining nodes and relationships, the specification can define display rules, navigation rules, etc. associated with the navigable semantic network. The computer environment can further include a compiler, a view instantiation engine, and a presentation engine. The compiler compiles the specification into a semantic network model. The view instantiation engine instantiates different portions of a semantic network as defined by the semantic network model. The presentation engine initiates display of the instantiated portion of the semantic network.

Abstract: A method, apparatus and computer program product for performing targeted advertising in documents is presented. A document is identified as having advertisements associated therewith. A statistical ranking of terms derived from said document is received and at least one term is selected from the results to use as a keyword for associating at least one advertisement with the document. The at least one term is stored with the document. When the document is viewed, the document is identified as being enabled to have advertisements associated therewith. The at least one stored term is retrieved from the document and is submitted to an advertisement aggregator. At least one advertisement is received from the advertisement aggregator and is rendered with the document.

Abstract: Systems and methods are discussed to automatically create a domain ontology that is a combination of ontologies. Some embodiments include systems and methods for developing a combined ontology for a website that includes extracting collocations for each webpage within the website, creating first and second ontologies from the collocations, and then aggregating the ontologies into a combined ontology. Some embodiments of the invention include unique ways to calculate collocations, to develop a smaller yet meaningful document sample from a large sample, to determine webpages of interest to users interacting with a website, and to determine topics of interest of users interacting with a website. Various other embodiments of the invention are disclosed.

Abstract: A selection of one or more segment attributes from an offer provider campaign is received at a graphical user interface. The one or more segment attributes define one or more segments that correspond to one or more offers in the offer provider campaign. Further, an expression graph based on the one or more segment attributes is generated. In addition, a portable micro-segment object is generated based on the expression tree such that the portable micro-segment object lacks dependence on the offer provider campaign.

Abstract: A method includes identifying a content entity in content data, categorizing the content entity into at least one content entity category of a plurality of content entity categories, and identifying a plurality of searchable information resources associated with the at least one content entity category.

Abstract: User data and a plurality of micro-segment definitions such that each micro-segment definition in the plurality of micro-segment definitions corresponds to one or more offers in an offer provider campaign are received. Further, a dispatcher dispatches a first subset of the user data and a first subset of the plurality of the micro-segment definitions to a first node in a network. In addition, the dispatcher dispatches a second subset of the user data and a second subset of the plurality of the micro-segment definitions to a second node in the network. Parsing and compiling are performed at each node. Further, parallel processing is performed at a scalable evaluation engine at each node to apply micro-segment condition rules to user data to determine matches to micro-segments. Computation of micro-segments occurs in parallel and resulting micro-segment assignments are collected, filtered to remove duplicates, then ranked to produce a final set of micro-segments that can be used to find offers.

Abstract: An extensible offer inventory database of offers in a domain is established. Further, an offer ontology is generated based on the extensible offer inventory database. The offer ontology provides an extensible vocabulary that correlates to categories in the offer inventory database. In addition, offers are automatically located. The offers are also semantically analyzed to generate semantic analysis data. Further, user data is obtained. In addition, an optimal offer match is automatically determined based upon the semantic analysis data and the user data.

Abstract: A campaign is received, at a micro-segmentation system, from an offer provider. The micro-segmentation system is a third-party system. The campaign indicates a set of target attributes and one or more offers corresponding to the set of target attributes. A set of user attributes pertaining to each of the plurality of users is received at the micro-segmentation system. The set of attributes is defined by an attribute knowledge structure. Permission is received at the micro-segmentation system from each of the plurality of users to receive an offer from the micro-segmentation system. Data associated with the micro-segment classification is provided from the micro-segmentation system without user information to the offer provider.