The proliferation of Web 2.0 has brought about all kinds of digital artifacts: documents, people, concepts, vocabulary, tasks, activities, and more. However, there is no easy way to combine these assets in a way that provides meaningful user experience. IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks provides a unified API that helps in creating solutions for these types of multidimensional networks (people, documents, tasks, etc.) and provides an integrated platform for combining social computing, semantic processing, and activity-centered computing for enhanced user experience. Take IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks for a test drive to fully asess its capabilities!


 

 

 

 

 

 

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IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks provides a unified API that helps in creating solutions for these types of multidimensional networks (people, documents, tasks, etc.) and provides an integrated platform for combining social computing, semantic processing, and activity-centered computing for enhanced user experience. Multidimensional Socio-Semantic Networks: Socio-Semantic Networks are social networks that are based on a social model of the network (people). In this type of networks, people have a wide variety of interests. For example, we have friends, and co-workers. However, in any social network, there is also the second dimension of the network (documents). For example, Facebook is a social network where people interact with each other and this activity is a result of their interests and values. However, you can have a wide variety of people, but only a small set of documents, as in the case of personal blogs. Language-based Socio-Semantic Networks: Multidimensional Socio-Semantic Networks also exist that are based on the understanding of language. In these networks, people and documents share the same space and exist in the same social network. Meaningful User Experience: In this type of networks, the ability to combine and learn from people’s work, and their projects, in a language-based, socio-semantic network is essential. IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API (Application Programming Interface) is a unified API that can be used to create solutions for this type of networks. In order to get started with IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API, visit the IBM Knowledge Center at IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API: The IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks API is a unified API that can be used to create solutions for this type of networks. For more information on the API, click For more information on IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks, click



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IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks integrates information analysis and synthesis technologies to enable Web sites, Web services, and other content-enabled applications to deliver online information that can be readily understood and used by humans. This integration enables the following: * Socially-enabled content analytics to understand, mine, and model social, behavioral, and socio-economic information, including user profiles, user interactions, and knowledge/opinion distribution, to provide context-sensitive, collaborative, and intelligent information processing. * Socio-semantic networks with integrated semantics for data-driven content mining, analytics, and synthesis of social and data content for enhanced experience. * User-centric social content mining with IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks as a unified platform for content analysis and synthesis. Users of IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks: * Specialized domain professionals who design, analyze, and mine social, behavioral, and socio-economic data to design, produce, and model content-driven applications. * Content managers who design and publish application-enabled content that incorporates social and data analytics. * Web developers who mine user data and extract socio-economic data from other online information sources. How it Works: The components of IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks are integrated to help you create solutions that enable people to interact with information in an intuitive manner, and to understand the context of any content so that they can make better decisions. The following key components are integrated in IBM LanguageWare Miner for Multidimensional Socio-Semantic Networks: * Socio-semantic networks: includes two modules, sociosemantic network and Socio-semantic network engine; users can use sociosemantic network to directly access sociosemantic network capabilities. * Social mining: includes four modules, social mining, social mining engine, social mining data analyst, and social mining data synthesizer; users can use social mining to directly access the social mining data analyst and social mining data synthesizer capabilities. * Socio-semantic networks: provides a unified API that enables users to access a wide range of capabilities including: a) Context-sensitive, collaborative, and intelligent Web content management; b) Socially-enabled analytics to understand, mine, and model social, behavioral, and socio-economic information; c) User-centric social content 2edc1e01e8



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In today’s world, the act of sharing information is not uncommon. Not only the Internet, but social networks such as Facebook and Twitter, also enable sharing of information. People are increasingly using these social networks to share their opinions on many topics and issues. This has resulted in the explosion of social networks such as “micro-blogs” such as Twitter and “micro-status” such as Facebook, which are continuously updated. However, a common problem with this trend is that the information that people share on social networks is fragmented and is hard to understand and correlate. This problem becomes significant when people share the information in a heterogeneous manner, on different platforms, using different terms and naming conventions. In addition to the issue of fragmented information, there are many people who are constantly searching for ideas, patterns, and discussions on a wide variety of topics. These searches often involve people searching for topics to discuss, people to talk to, and people who have similar interests. There is currently no easy way to find the right people, right conversations, right topics and right discussions. While there are many people and organizations that aggregate the information on social networks, they often do not know which topics to collect, and which content to syndicate to users. There are many organizations which aggregate content from social networks and from a wide range of other sources, including but not limited to web sites, blogs, instant messaging and chat systems, news and media organizations, wikis, and news groups. Currently, there are many systems which aggregate content from social networks and from a wide range of other sources, such as through search, and then match content from these sources based on some similarity. However, these existing systems are not able to relate fragmented content from different sources. Some systems have attempted to “understand” the concepts underlying the social media. For example, U.S. Patent Application Publication No. 2010/0193763 by Lu et al. entitled “Knowledge base for categorization of tweets,” incorporated by reference herein in its entirety, describes methods and apparatus for creating a knowledge base of tweets to provide the ability to classify tweets. U.S. Patent Application Publication No. 2010/0193763 discloses a method of creating a knowledge base of tweets which includes obtaining a set of tweets that are manually created and indexed, processing the manually created tweets and the manually indexed tweets into an indexed tweet set, wherein each indexed tweet in the indexed tweet set includes a tweet identifier and a



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LanguageWare Miner is a service that combines social computing, semantic processing, and activity-centered computing in a way that provides enhanced user experience. LanguageWare Miner uses a mix of open source technology, including Apache Hadoop and Apache Solr to provide high performance capabilities. LanguageWare Miner builds an integrated platform for combining social computing, semantic processing, and activity-centered computing for enhanced user experience. LanguageWare Miner supports the following activities: 1. People. LanguageWare Miner helps users discover and access relevant people on the Web and find out more about their relationship to them. LanguageWare Miner provides a discovery engine for people-based queries. The discovery engine exploits social connections and provides multiple related results. 2. Documents. LanguageWare Miner helps users discover and access relevant documents on the Web and find out more about their relationship to them. LanguageWare Miner provides a discovery engine for documents-based queries. The discovery engine exploits social connections and provides multiple related results. 3. Activities. LanguageWare Miner helps users discover and access relevant activities on the Web and find out more about their relationship to them. LanguageWare Miner provides a discovery engine for activities-based queries. The discovery engine exploits social connections and provides multiple related results. 4. Tasks. LanguageWare Miner helps users discover and access relevant tasks on the Web and find out more about their relationship to them. LanguageWare Miner provides a discovery engine for tasks-based queries. The discovery engine exploits social connections and provides multiple related results. LanguageWare Miner for Multidimensional Socio-Semantic Networks LanguageWare Miner provides the ability to provide increased relevance for users of a search. The query processor in LanguageWare Miner can be trained to make the results more relevant and personal to the user. LanguageWare Miner supports a number of modes of query processing to provide high performance and high-quality results. • Solr Filter Mode – LanguageWare Miner can be trained to provide improved results using features like faceted search and document-level relevancy. • Solr QueryMode – LanguageWare Miner can be trained to provide improved results using features like faceted search and document-level relevancy. 4. Query A and Query B – LanguageWare Miner can be trained to improve query A and query B by using the results of each other’s result sets. 5. Activities and Tasks – LanguageWare Miner can be trained to improve the user’s ability to find activities and tasks of relevance to the user. Developers can use standard Java, Perl, Python, C#, and other technologies to implement their own custom scripts. Key Features: • MapReduce and Hadoop for processing large volumes of data and information • Cognitive interface to understand human semantics for finding people, documents, and other activities • Solr for indexing and querying social and semantic content


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This plugin was designed for the Macintosh platform. It should work with the Macintosh version of Eudora 4.1 or later. If it does not work with your version of Eudora, email the author and request a refund. Eudora 4.0 and earlier The Eudora 4.0 engine was not designed to work with the Visual Installer. If you purchased Eudora 4.0 from Blue Orange Software, please download and install the Mac version of the Visual Installer, and then use the E



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