Group items tagged

Semantic Cloud is the API that powers semantic search engine SenseBot and contextual linking tool for bloggers LinkSensor. The API supports SOAP and REST protocols (HTTP GET). The idea is to empower semantic startups or any ventures that are looking to utilize an affordable high-quality semantic solution to build their applications. Semantic API features include: * extraction of semantic concepts from a page or document; * creating a "semantic cloud" of concepts describing a group of documents; * generating a multi-document summary of a set of pages; * generating an essay on a topic based on a set of documents. Multiple parameters allow the client to control the type and format of results.

Last week, I participated in a WWW2007 panel called "Multimedia Metadata Standards in a Semantic Web 3.0", where I took the opportunity to declare the Semantic Web dead. As you can imagine, such a declaration in front of a crowd of semantic web researchers provoked many responses. While I believe panels should be provocative and entertaining, I also have specific reasons for why I went as far as calling the Semantic Web "dead". Let me explain what I mean.

Semantic technologies are playing an an increasingly important role in capturing and modeling biological knowledge. Semantic systems biology can complement the bottom-up approach with data-driven generation of hypotheses. Therefore, Semantic Systems Biology (SSB) is a systems biology approach that uses semantic description of knowledge about biological systems to facilitate integrated data analysis.

A Semantic Map is kind of like a dictionary, in that it's a representation of Cognition's ability to define things. Cognition claims that its Semantic Map has over 10 million semantic connections; over 4 million semantic contexts (word meanings that create contexts for specific meanings of other related words); over 536,000 word senses (word and phrase meanings); 75,000 concept classes (or synonym classes of word meanings); 7,500 nodes in the technology's ontology or classification scheme; and 506,000 word stems (roots of words) for the English language.

KHarvester (Informal Knowledge Harvester) is a SOA layer which collects RDF data from web pages. It provides REST based Web Services for managing data available on Social Semantic Information Sources (SSIS): semantic blogs, semantic wikis, and JeromeDL (the Social Semantic Digital Library). These Web Services allow saving harvested data in the informal knowledge repository, and providing them in a form of informal Learning Objects (LOs) that are described accroding to LOM (Learning Object Metadata) standard. Also, IKHarvester is an extension to Didaskon system. Didaskon (διδάσκω - gr. teach) delivers a framework for composing an on-demand curriculum from existing Learning Objects provided by e-Learning services (formal learning). Moreover, the system derives from SSIS which provide informal knowledge. Then, the selection and work-flow scheduling of Learning Objects is based on the semantically annotated specification of the user's current skills/knowledge (pre-conditions), anticipated resulting skills/knowledge (goal) and technical details of the clients platform.

The goal of this twine is to gather relevant information and people around the project of establishing a new adressing layer in cyberspace : semantic space. Semantic space is designed to augment : - semantic interoperability - distributed social semantic computing - exchange of personal and collective interpretation of vast amouts of data - collective intelligence reflexivity

The semantic mapping of the English language is the key to making Natural Language Processing (NLP) effective. Cognition's unique Semantic Map, which it built over the past 24 years, is the most comprehensive and complete map of the English language available today. It can be used in support of the Semantic Web for semantic search, search tools, business analytics, machine translation, document search, context search, and much more.

MOAT (Meaning Of A Tag) provides a Semantic Web framework to publish semantically-annotated content from free-tagging. While tags are widely used in Web 2.0 services, their lack of machine-understandable meaning can be a problem for information retrieval, especially when people use tags that can have different meanings depending on the context. MOAT aims to solve this by providing a way for users to define meaning(s) of their tag(s) using URIs of Semantic Web resources (such as URIs from dbpedia, geonames … or any knowledge base), and then annotate content with those URIs rather than free-text tags, leveraging content into Semantic Web, by linking data together. Moreover, tag meanings can be shared between people, providing an architecture of participation to define and exchange potential meanings of tags within a community of users. To achieve this goal, MOAT relies on an architecture that can be deployed for any organisation or community and that involves a lightweight ontology, a MOAT server, and some third-party clients .

welcome to gnowsis, the Semantic Desktop environment published by the Knowledge Management Lab of the DFKI. Gnowsis is a reference implementation of parts of the Nepomuk Semantic Desktop framework. The Nepomuk project provides a standardized, conceptual framework for Semantic Desktops.Gnowsis can be used in research projects or by interested individuals to benefit from Semantic Web technologies.

Semantic Exchange is a collaborative industry news, research, and education initiative about all things web 3.0 and semantic web, and sponsored by industry leading semantic technology providers.

The project KiWi is concerned with knowledge management in Semantic Wikis and funded by the European Commission under the Project Number 211932 in the EU Seventh Framework Programme (FP7). KiWi's objective is to investigate how knowledge management in highly dynamic environments can be supported using Semantic Wiki technologies, and how Semantic Wikis can be improved to satisfy the requirements of knowledge management. For this purpose, KiWi will * implement an advanced knowledge management system based on the Semantic Wiki IkeWiki and extend it by improved, rule-based reasoning support, information extraction, personalisation, and advanced visualisations and editors * verify the system on two use cases in the areas of project knowledge management and software knowledge management, with flexible workflow models and specific support for the respective application areas.

The mistake is that semantic search engines present us with Google-like search box and allow us to enter free form queries. So we type the things that we are used to asking - primitive queries. It never occurs to us to type in What actor starred in both Pulp Fiction and Saturday Night Fever? or What two US Senators received donations from a foreign entity? We type simple questions, but this is not where the power of semantic search lies. Lets look at the spectrum of semantic technologies from Google, to SearchMonkey, to Powerset, and Freebase to understand what is going on.

KIM is a software platform for: * Semantic annotation of text At more length: automatic ontology population and open-domain dynamic semantic annotation of unstructured and semi-structured content for Semantic Web and KM applications * Indexing and retrieval (semantically-enabled and IE-enhanced search technology) * Query and exploration of formal knowledge * Co-occurrence tracking and ranking of entities * Entity popularity timelines analysis

The SMW+ User Forum is an online community for users and developers of Semantic MediaWiki+ in a commercial or production environment with the purpose to improve and extend Semantic Media Wiki+. SMW+ Forum fuses the social networking aspect of Web 2.0 with semantic technologies.

"The semantic web proposes to inject machine meaningful data into the existing human language oriented web. As part of this effort, on the semantic web, URIs are used to identify entities. But there is currently no standard way to specify what it is that any given URI is to identify, or to whom, or when. Recent work in linguistics offers ideas for a solution to this lack. It focuses on the pragmatics of actual language use among ensembles of people. Also, the World Wide Web provides a set of technologies, in the form of socially constructed web sites, that could be employed to provide a solution. In this paper, I suggest how such socially constructed web sites could be used to address the problem of establishing common ground among a community of machines of the referent of a URI used on the semantic web. The result is a proposal to automate social meaning by creating societies of machines that share knowledge representations identified by URIs."

What tagging does point to convincingly is the social aspect of naming. In a given natural language, many sorts of identifiers, such as common words, are socially centralized. Other sorts of identifiers, such as proper names, are socially decentralized, varying from local context to local context. Black has noticed a correspondence between this socially grounded identification process and the use of socially constructed Web sites.

I hope that "Smarter" is going to be a key tag for the Web 3.0, and yet I think "More Open, More Ubiquitous, with even More Information (Overload) and a little Smarter" is what it's really going to be. We'll have to wait till "Web 4.0" for a web that really is stepwise more intelligent, one that could really be called semantic and hold the hidden promises of a "Semantic Web". And the reason I believe this is that the community is focused on linking more stuff together in new ways and breaking down data siloes, much more than it is focused on creating new, smarter filters for all the data that's going to be made accessible that way.

In our latest podcast I talk to Mark Greaves, Director of Knowledge Systems Research at Vulcan. We talk about Mark's background, Vulcan's interest in the Semantic Web, and explore the opportunities to build a business on top of semantic technologies.

Want to create your own semantic search engine, but just don't have the PhDs? Semantic search engine Hakia is opening up APIs to let anyone build their own semantic search application on top of its technology. Hakia looks at the meaning of Web pages and matches search queries using its own quality index of trusted, relevant sites.

There's a long-time debate between those who advocate for semantic markup, and those who believe that machine learning will eventually get us to the holy grail of a Semantic Web, one in which computer programs actually understand the meaning of what they see and read.

s Richard MacManus recently predicted, in 2008 we'll witness the rise of semantic web services. From the native support for Microformats in Firefox 3, to the New York Times' utilization of rich headers metadata, to this week's release of the Social Graph API by Google, semantics are starting to slip onto the web. The impact is being felt because large companies are really starting to focus on structured information. In the same vein, last week Reuters - an international business and financial news giant - launched an API called Open Calais. The API does a semantic markup on unstructured HTML documents - recognizing people, places, companies, and events. This technology is the next generation of the Clear Forest offering, which Reuters acquired last year. We have profiled Clear Forest on ReadWriteWeb and in this post we will look at what Reuters opened up and why.