Buddhist yogacara writings, forum, network, teachings, and more.
Twango, a great way to connect with family and friends and share your photos, videos and other media in the way you choose.
7 steps of the research process, from Cornell University.
A Microformat extension for Dreamweaver 8, although should work for MX and above, implements a few simple Insert Bar Objects to help Dreamweaver users to add hCalendar, hCard, rel-license, rel-tag and XFN data to their documents. After installing, you’ll find a new Microformats category on your Insert Bar. Support for more formats is to follow, so check back.
Microformat Base Interface exploration for microformats, Microformat Base crawls the web for structured data, creates a local copy of any data it finds, and offers a variety of tools for working with microformats.
Google hCalendar Google hCalendar, a Firefox Greasemonkey script, automatically identifies hCalendar microformat events and inserts buttons to add such events to Google Calendar.
he Music Ontology Specification provides main concepts and properties fo describing music (i.e. artists, albums and tracks) on the Semantic Web. This document contains a detailed description of the Music Ontology.
Some small teams of sharp, self-directed people are quickly creating sleeker stuff that works far better than the overly complex webapps produced by huge teams and gargantuan cost - with microscopic usability on behalf of clients with hazy goals.
Fantastic reviews in exactly ten words ~ no more, no less.
The main task of the GenIELex project is the development of a biochemistry specific lexicon as well as of an annotated corpus for the evaluation of the system. The need for the construction of such a lexicon is illustrated by the following figures, based on a corpus of full papers extracted from the Journal of Biological Chemistry. In biochemistry and related fields there is much more effort spent on data generation than on data analysis. This is reflected in the huge amount of publications that are annually produced, e.g around 450000 Medline (a database of publications in the field of medicine, biology, chemistry, biochemistry,etc.) publications per year. This massive amount of information remains "hidden" from the field experts who do not have the capacity to keep up with the number of publications. Natural language processing (especially information extraction or retrieval) can be used to help the scientist to extract, and thus analyse, relevant information for their research projects.
NICK DOUGLAS -- "Oh my god Web 2.0? More like Bubble 2.0!" Okay, good start. But to really intimidate non-geeks and show how you're so over Web 2.0 (as proved by the five parody logos you uploaded on Flickr and auto-inserted into your blog), you need to break out these advanced tactics...
Palestinians, Israelis, and international supporters mobilizing against violent extremism. One Voice principles. Conflict resolution software.
Stuck in her thumb, and pulled out a plum...of widget resources, websites, tools, and...of course...widgets! For Windows and Mac.
Data mining (DM), also called Knowledge-Discovery in Databases (KDD) or Knowledge-Discovery and Data Mining, is the process of automatically searching large volumes of data for patterns using tools such as classification, association rule mining, clustering, etc.. Data mining is a complex topic and has links with multiple core fields such as computer science and adds value to rich seminal computational techniques from statistics, information retrieval, machine learning and pattern recognition.
Audio Medica News, Lectures on Anatomy & Physiology, 2nd Annual Scientific Congress on Cardiology, Absolute Science...
TeSSI® (Terminology Supported Semantic Indexing) is a state-of-the-art tool that improves upon the existing search and retrieval tools by extracting the meaning out of medical free text and placing the resulting medical ‘concepts’ in the document index instead of terms. This creates a very powerful environment allowing healthcare knowledge seekers to query content stores in natural language and retrieve highly relevant information with great accuracy. TeSSI® performs automated information extraction of medical facts out of free text medical documents and feeds them automatically into predefined templates or rules engines for further processing and analysis- a clear market differentiator. TeSSI® also has the ability to automatically categorize and cluster documents base on its understanding of their content. TeSSI® represents a set of software components that perform semantic indexing, semantic searching, coding and information extraction. Instead of creating indexes containing words, TeSSI® creates indexes containing concepts, which are individual units of meaning that remain independent of language. The medical intelligence that TeSSI® needs to perform these activities is present in L&C’s LinKBase® medical ontology. The complete TeSSI® semantic indexing and search process can be implemented as an automated mechanism without the use of a manual intervention, or it can be deployed in an auto-assist mode depending on the needs of the customer. TeSSI® Indexing Engine TeSSI® Extraction Engine TeSSI® Search Engine
Sundown Lounge: laid back (and often explicit) weekly podzine of music, spoken word, progressive politics, weird science, and west coast open mic vignettes; KEXP: eclectic mix of full length songs; The Beat Oracle: experimental, electronic, and conscious hip-hop; EDMCast: unreleased trance, house, techno, & breaks; Insomnia Radio Seattle: unsigned, under-the-radar bands;
FreePharma is a software plug-in that analyzes drug prescription information expressed in free natural language (written or spoken) and structures it automatically for integration in host applications. FreePharma can derive the dose, route, frequency etc. from natural language & common language descriptions...it will automatically capture, structure, and link prescription info to drug databases, reducing medication errors and making drug data available for analysis & research.
Annotea is a W3C Semantic Web Advanced Development project that provides a framework for rich communication about Web pages through shared RDF metadata. An RDF model of bookmark classification permits multiple classification systems to be related to each other; for example, users's informal bookmark folders could be associated with formal ontologies via additional RDF properties. Furthermore, bookmark classification systems in RDF need not be limited to strict hierarchies but can be full graphs.In addition, the Annotea infrastructure makes it simple to add other RDF properties to bookmarks, share individual bookmarks with other users, share individual bookmarks between browsers, and query bookmark data in new ways not supported by current bookmark systems. As bookmarks are RDF metadata it is also easy to define semantic relationships that permit tools to interpret other data, such as RSS newsfeeds to be presented as bookmarks.
Text mining and web scraping involves chunk parsing and recognition of named entities (institutions, dates, titles)...The extraction of named entities is mostly based on a strategy that combines look up in gazetteers (lists of companies, cities, etc.) with the definition of regular expression patterns. Named entity recognition can be included as part of the linguistic chunking procedure. So for example, the following sentence fragment: ... the secretary-general of the United Nations, Kofi Annan, ... will be annotated as a nominal phrase, including two named entities: United Nations with named entity class: organization, and Kofi Annan with named entity class: person.
Poetry Chaikana (sacred poetry from Sufi, Hindu, Buddhist, Jewish, Muslim, Christian, and other traditions); Sakya Monastary Vlogs: Tibetan Buddhism's practice of Chenrezig; Zencast (seeds of happiness); Audio Dharma...
LinKBase: World's largest formal medical ontology, i.e. a conceptual computer-understandable representation of medicine. Due to its magnitude, formal structure and the fact that it is machine-readable, LinKBase is the only medical knowledge base capable of producing the results needed in automated processes that work with medical unstructured texts...It breaks down medical language to a common denominator set of medical concepts that can be expressed both by standardized terminologies and by natural language expressions.
As human language is a primary mode of knowledge transfer, a growing integration of language technology tools into semantic web applications is to be expected. Language technology tools will be essential in scaling up the semantic web by providing automatic knowledge markup support and facilities for ontology monitoring and adaptation.
This Special Issue of JIME will feature nine papers by invited, internationally renowned authors who have previously written about the effect of technology on education, learning and scholarship. Their interests and writing span distance education, higher education and lifelong learning.
Neatly organized on a pleasingly uncluttered page, find podcasts by topic with ease. Many academic, as well as popular, categories. Society and Culture. Science and Medicine. Religion and Spirituality. Government. Politics. Education. Arts. Technology. Comedy. Film. Music.
Natural Language Processing (NLP) and Natural Language Understanding (NLU) are technologies that can extract data and information from free text documents for further processing. Language and Computing (L&C) is unique in delivering this level of understanding through its integration of the world’s largest medical ontology with sophisticated linguistic processing algorithms. L&C's, mission is to create the tools that allow disparate categories of information to be treated as an integrated knowledge store. These include: Mapping and modeling disparate controlled medical vocabularies (CMVs); Populating clinical data warehouses with elements captured in free text documents; Supporting the billing and reimbursement process; Improve patient safety and decision support applications; and Implementing the Continuity of Care Record (CCR) elements in HL7 CDA representation.
After analyzing a large amount of social annotations, we found that tags are usually semantically related to each other if they are used to tag the same or related resources for many times. Users may have similar interests if their annotations share many semantically related tags. Resources are usually semantically related if they are tagged by many users with similar interests. This domino effect on semantic relatedness also can be observed from other perspectives. For example, tags are semantically related if they are heavily used by users with similar interests. Related resources are usually tagged many times by semantically related tags and finally users may have similar interests if they share many resources in their annotations. This chain of semantic relatedness is embodied in the different frequencies of co-occurrences among users, resources and tags in the social annotations. These frequencies of co-occurrences give expression to the implicit semantics embedded in them.
The semantic web must "explain the meaning of words" to computers. Some semantic technologies use a "bottom up" by embedding semantic annotations (metadata) into web content. "Top down" technologies analyze information without metadata using some form of natural language processing. ClearForest, using the top-down approach, has created Gnosis, a Firefox extension: "With a single click, Gnosis will identify the people, companies, organizations, geographies and products on the page you are viewing. Using the built-in navigation sidebar you can gain immediate understanding of the page’s contents."
After analyzing a large amount of social annotations, we found that tags are usually semantically related to each other if they are used to tag the same or related resources for many times. Users may have similar interests if their annotations share many semantically related tags. Resources are usually semantically related if they are tagged by many users with similar interests. This domino effect on semantic relatedness also can be observed from other perspectives. For example, tags are semantically related if they are heavily used by users with similar interests. Related resources are usually tagged many times by semantically related tags and finally users may have similar interests if they share many resources in their annotations. This chain of semantic relatedness is embodied in the different frequencies of co-occurrences among users, resources and tags in the social annotations. These frequencies of co-occurrences give expression to the implicit semantics embedded in them.
This paper describes an extension to be integrated in Wikipedia, that enhances it with Semantic Web [6] technologies.
If everyone would create good metadata for the purposes of describing their goods, services and information, it would be a trivial matter to search the Internet for highly qualified, context-sensitive results: a fan could find all the downloadable music in a given genre, a manufacturer could efficiently discover suppliers, travelers could easily choose a hotel room for an upcoming trip. A world of exhaustive, reliable metadata would be a utopia. It's also a pipe-dream, founded on self-delusion, nerd hubris and hysterically inflated market opportunities.
It is important to differentiate between text data mining and information access (or information retrieval, as it is more widely known)... the goal of data mining is to discover or derive new information from data, finding patterns across datasets, and/or separating signal from noise. The fact that an information retrieval system can return a document that contains the information a user requested does not imply that a new discovery has been made: the information had to have already been known to the author of the text; otherwise the author could not have written it down. Another way to view text data mining is as a process of exploratory data analysis that leads to the discovery of heretofore unknown information, or to answers to questions for which the answer is not currently known. For example, when investigating causes of migraine headaches, he extracted various pieces of evidence from titles of articles in the biomedical literature. Some of these clues can be paraphrased as follows: stress is associated with migraines stress can lead to loss of magnesium calcium channel blockers prevent some migraines magnesium is a natural calcium channel blocker spreading cortical depression (SCD) is implicated in some migraines high leveles of magnesium inhibit SCD migraine patients have high platelet aggregability magnesium can suppress platelet aggregability These clues suggest that magnesium deficiency may play a role in some kinds of migraine headache; a hypothesis which did not exist in the literature at the time Swanson found these links. The hypothesis has to be tested via non-textual means, but the important point is that a new, potentially plausible medical hypothesis was derived from a combination of text fragments and the explorer's medical expertise. (According to swanson91, subsequent study found support for the magnesium-migraine hypothesis [Ramadan et al.1989].)
Tagging is great...But can tagging be better? Yes. For example, how do you specify Paris (the city) as opposed to Paris (the person)? By using contextual tags (e.g.; celebrity:paris or city:paris) to give "thing" tags meaning. words are no longer stripped of their syntactical sense, lost to wander in the desert.
This paper describes Seeker, a platform for large-scale text analytics, and SemTag, an application written on the platform to perform automated semantic tagging of large corpora. We apply SemTag to a collection of approximately 264 million web pages, and generate approximately 434 million automatically disambiguated semantic tags, published to the web as a label bureau providing metadata regarding the 434 million annotations. To our knowledge, this is the largest scale semantic tagging effort to date.We describe the Seeker platform, discuss the architecture of the SemTag application, describe a new disambiguation algorithm specialized to support ontological disambiguation of large-scale data, evaluate the algorithm, and present our final results with information about acquiring and making use of the semantic tags. We argue that automated large-scale semantic tagging of ambiguous content can bootstrap and accelerate the creation of the semantic web.
Ever notice that before you can really do a good web search, you have to actually know something about your search topic? Let's say you want to learn more about jazz. But you don't know any of the "keywords," the musicians, the composers, the talent. So you type general terms into Google...and pretty soon, it's a jazz tsunami. You're flooded with information that'll take hours to sift through. The mSpace software framework lets you wrap iTunes-like browsers around any kind of information domain and associate any kind of media with that information and explore it just about any way you'd like.
Lexical ambiguity is a fundamental problem in Information Retrieval (IR), especially in the medical domain. Many systems use a subset of the words contained in the document to represent the content, but they are faced with the problem of ambiguity. In this paper, we propose a method for disambiguation based on existing medical terminological resources on the one hand, and statistical tools for linguistic annotation on the other, in order to develop more satisfactory indexing techniques for patient reports. The main hypothesises guiding this method are that: (i) Syntax can help to distinguate meanings of words that are polyfunctional. (ii) Syntactic analysis can be done by a probabilistic tagger...
You maintain a blog at, say, livejournal.com (but this can be anything) and you stay logged in there usually. You go to leave a comment at someblog.com (perhaps it's Movable Type, or Wordpress, or DeadJournal, ...) and you don't have an account there, so there's otherwise no way to leave an authenticated comment. But if their blog-system has OpenID support,
OpenID starts with the concept that anyone can identify themselves on the Internet the same way websites do-with a URI (also called a URL or web address). Since URIs are at the very core of Web architecture, they provide a solid foundation for user-centric identity. OpenID is an open, decentralized, free framework for user-centric digital identity.
A great jump towards the advent of the Semantic Web will take place when a critical mass of web resources is available for use in a semantic way. This goal can be reached by the creation of semantic meta-data in the publication workflow, or by the development of systems and applications able to associate semantics to resources (i.e., annotating them) automatically. Those applications should analyze the content of a web page and should be able to associate some ontology classes to it...by exploiting lexical networks like WordNet, which contain syntactic information connected through semantic relationships.
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