Abstract: Discovering relevant concepts and context for content nodes to determine a user's intent includes identifying one or more concept candidates in a content node based at least in part on one or more statistical measures, and matching concepts in a concept association map against text in the content node. The concept association map represents concepts, concept metadata, and relationships between the concepts. The one or more concept candidates are ranked to create a ranked one or more concept candidates based at least in part on a measure of relevance. The ranked one or more concept candidates is expanded according to one or more cost functions. The expanded set of concepts is stored in association with the content node.

Abstract: Discovering relevant concepts and context for content nodes to determine a user's intent includes identifying one or more concept candidates in a content node based at least in part on one or more statistical measures, and matching concepts in a concept association map against text in the content node. The concept association map represents concepts, concept metadata, and relationships between the concepts. The one or more concept candidates are ranked to create a ranked one or more concept candidates based at least in part on a measure of relevance. The ranked one or more concept candidates is expanded according to one or more cost functions. The expanded set of concepts is stored in association with the content node.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Methods and apparatus for a new approach to the problem of matching relevant content to user queries. Instead of looking for the exact keyword, the invention expands it into groupings of concepts and phrases, where each such group represents one possible user intention (as implied by the query phrase or keyword). Each such grouping is analyzed to provide relevant content, including but not limited to, unstructured data like world wide web, categorized data and paid listings. The provided method can better capture user intentions even for cases where there is no click-through information.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Discovering relevant concepts and context for content nodes to determine a user's intent includes identifying one or more concept candidates in a content node based at least in part on one or more statistical measures, and matching concepts in a concept association map against text in the content node. The concept association map represents concepts, concept metadata, and relationships between the concepts. The one or more concept candidates are ranked to create a ranked one or more concept candidates based at least in part on a measure of relevance. The ranked one or more concept candidates is expanded according to one or more cost functions. The expanded set of concepts is stored in association with the content node.

Abstract: Methods and systems for extracting intents and intent profiles of users, as inferred from the different activities they execute and data they share on social media sites, and then (i) monetization of such intents via targeted advertisements, and (ii) enhancement of user experience via organization of their contact lists and conversations and posts based on their content and conceptual context.

Abstract: Systems and methods for the creation of hierarchical networks of overlapping informational web neighborhoods using percolation crawling. Each neighborhood comprises a set of closely linked pages that share a common set of concepts and intent and purpose. The neighborhoods represent web pages that share a common set of underlying concepts and semantic associations. Each such neighborhood can be semantically tagged.

Abstract: Systems and methods are described that facilitate exploration and mining of a corpus of documents using concepts and knowledge, rather than keywords and enable interactive visualization, management, monetization, and personalization of knowledge search results on a user interface. Underlying documents can be mapped into overlapping clusters of knowledge dimensions and the documents can be further partitioned into narrower knowledge dimensions, resulting in a hierarchical structure. Content of knowledge dimensions may be filtered using a knowledge dimension map. Knowledge may be personalized by a user and shared in collaborative and social networks. Knowledge-based advertisement systems and methods are described that associate products and services with knowledge dimensions and concepts. Advertisers can bid on concepts and KDs. Systems and methods are described that distribute a percentage of CPC revenue among content owners and provide revenue to content providers based on relevancy scores.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: Systems and methods are described for the creation of hierarchical networks of overlapping informational Web neighborhoods, where each neighborhood comprises a set of closely linked pages that share a common set of concepts and intent and purpose. A general description of a category of information can be used to generate a network of overlapping communities of web pages and objects, where the neighborhoods represent pages or objects that share a common set of underlying concepts and semantic associations. Each such neighborhood can be semantically tagged. Overlaps among neighborhoods and the hierarchical structure of the network capture complex relationships among the concepts that the corresponding informational neighborhoods represent. All informational neighborhoods of the web can be mapped. The systems and methods can be adapted for any digital content and constitute a hybrid network of contents and their relationships.

Abstract: A solution for identifying related concepts of URLs and domain names includes using structural parsing to extract information from user input comprising a URL or domain name. The information includes one or more of a protocol, a location, and a subdirectory. Semantic parsing of the information is used to identify a first one or more concepts represented by one or more tokens within the extracted information. A content association map is queried to retrieve a second one or more concepts related to the first one or more concepts. Each of the concepts represents a unit of thought, expressed by a term, letter, or symbol. The concept association map includes a representation of concepts, concept metadata, and relationships between the concepts. The first one or more concepts and the second one or more concepts are ranked, and the ranked concepts are stored for displaying to one or more users of the computer platform.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: Comprehensive methods and systems are described for creating, managing, searching, personalizing, and monetizing a knowledge system defined over a corpus of digital content. Systems and methods are described in which a user can initiate in-depth searches of subject matter and can browse, navigate, pinpoint, and select relevant contexts, concepts, and documents to gain knowledge. Systems and methods are described in which knowledge can be personalized through tagged, personalized context, and personalized context can be shared within social and professional networks, securely and confidentially and with the desired access control. Systems and methods are described in which products and services can be advertised in context and advertising can be selected through a bidding process. Systems and methods are described by which a user can navigate contexts and concepts to obtain relevant information, products and services.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: Methods and apparatus for a new approach to the problem of finding communities in complex networks relating to a social definition of communities and percolation are disclosed. Instead of partitioning the graph into separate subgraphs from top to bottom a local algorithm (communities of each vertex) allows overlapping of communities. The performance of an algorithm on synthetic, randomly-generated graphs and real-world networks is used to benchmark this method against others. An heuristic is provided to generate a list of communities for networks using a local community finding algorithm. Unlike diffusion based algorithms, The provided algorithm finds overlapping communities and provides a means to measure confidence in community structure. It features locality and low complexity for exploring the communities for a subset of network nodes, without the need for exploring the whole graph.

Abstract: A solution for identifying related concepts of URLs and domain names includes using structural parsing to extract information from user input comprising a URL or domain name. The information includes one or more of a protocol, a location, and a subdirectory. Semantic parsing of the information is used to identify a first one or more concepts represented by one or more tokens within the extracted information. A content association map is queried to retrieve a second one or more concepts related to the first one or more concepts. Each of the concepts represents a unit of thought, expressed by a term, letter, or symbol. The concept association map includes a representation of concepts, concept metadata, and relationships between the concepts. The first one or more concepts and the second one or more concepts are ranked, and the ranked concepts are stored for displaying to one or more users of the computer platform.