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As computers and computer networks become more sophisticated, a huge amount of information, such as that found in Web documents, has been accumulated and circulated. Such information gives people a framework for organizing their daily lives. A well-functioning society needs technology that can be used to manage this wealth of information and, in particular, investigate its credibility. This technology would be able to handle a wide range of tasks: extracting credible information related to a given topic, organizing this information, detecting its provenance, clarifying background, facts, and various related opinions and the distribution of them, and so on. Especially, as the Web is becoming a major source of information nowadays, it is necessary to provide efficient and reliable methods for evaluation of Web content's trustworthiness. The aim of this workshop is to provide a forum for discussion on issues related to information credibility criteria and the process of its evaluation.

The W3C OWL 2 Web Ontology Language (OWL) is a Semantic Web language designed to represent ontologies - information about how individuals are grouped and fit together in a particular domain. OWL can represent rich and complex information about classes of individuals and their properties. OWL is a logical language, where every construct has a well-defined meaning, meanings that fit together to support exact and useful representation of many different kinds of information. OWL groups information into ontologies in the form of documents that can be stored and transmitted across the World Wide Web in the same way that data and other kinds of information are and that can be completely and effectively processed by tools that extract the information implicit in an ontology.

The DBpedia community uses a flexible and extensible framework to extract different kinds of structured information from Wikipedia. The DBpedia information extraction framework is written using PHP 5. The framework is available from the DBpedia SVN (GNU GPL License).

Melita is an ontology-based text annotation tool. It implements a methodology with the intent to manage the whole annotation process for the users. It was noticed that several steps in the process, which till now are done manually can be easily automated and handled all by the system. The main competencies of Melita can be summarised into four groups, i.e. the Managing task, the Extraction, the Learning and the Information Tagging Autonomously. This is performed thanks to the use of a smart interface together with a powerfull Information extraction algorithm. Melita has now been replaced by AKTive Media , please click here to download AKTive Media, Melita is no longer used or available for download

Piggy Bank is a Firefox extension that turns your browser into a mashup platform, by allowing you to extract data from different web sites and mix them together. Piggy Bank also allows you to store this extracted information locally for you to search later and to exchange at need the collected information with others.

GATE is... * the Eclipse of Natural Language Engineering, the Lucene of Information Extraction, a leading toolkit for Text Mining * used worldwide by thousands of scientists, companies, teachers and students * comprised of an architecture, a free open source framework (or SDK) and graphical development environment * used for all sorts of language processing tasks, including Information Extraction in many languages * funded by the EPSRC, BBSRC, AHRC, the EU and commercial users * 100% Java reference implementation of ISO TC37/SC4 and used with XCES in the ANC * 10 years old in 2005, used in many research projects and compatible with IBM's UIMA * based on MVC, mobile code, continuous integration, and test-driven development, with code hosted on SourceForge

understanding this content and making decisions based on it (especially in mission-critical situations) is not just a simple matter of consuming information. To effectively "make sense" of large, heterogeneous, and often unstructured content collections requires: - efficient, accurate, and context-based ways of extracting, filtering, and summarizing information; - better and more meaningful ways of organizing, visualizing, and interacting with the information; - faster, more objective methods for investigating hypotheses, detecting trends or patterns across multiple sources, and otherwise analyzing or interpreting information.

A software engine that pulls together facts by combing through more than 500 million Web pages has been developed by researchers at the University of Washington. The tool extracts information from billions of lines of text by analyzing basic relationships between words.

DBpedia is a community effort to extract structured information from Wikipedia and to make this information available on the Web. DBpedia allows you to make sophisticated queries against Wikipedia, and to link other data sets on the Web to Wikipedia data.

Triplify is based on the definition of relational database queries for a specific Web application in order to retrieve valuable information and to convert the results of these queries into RDF, JSON and Linked Data. Experiences showed that for most web-applications a relatively small number of queries (mostly between 3-7) is sufficient to extract the important information. After generating such database views the Triplify software can be used to convert the view into an RDF, JSON or Linked Data representation, which can be shared and accessed on the (Semantic) Web.

Our goal in creating this site is to provide the basis for a thriving community of UIMA developers who can announce, discuss, design, share, and critique UIMA-compliant components, resources and solutions. The Unstructured Information Management Architecture (UIMA) is a software framework that supports rapid development and deployment of multimodal analytics - applications which provide value by processing human-readable text, audio and/or video in order to extract information, answer questions, summarize documents, etc.

The scholarly literature forms a vast network of academic papers connected to one another by citations in bibliographies and footnotes [1]. The structure of this network reflects millions of decisions by individual scholars about which papers are important and relevant to their own work. Therefore within the structure of this network is a wealth of information about the relative influence of individual journals, and also about the patterns of relations among academic disciplines. Our aim at eigenfactor.org is develop ways of extracting this information.

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.

Faviki is a social bookmarking tool which allows you to tag webpages you want to remember with Wikipedia terms. This means that everybody uses the same names for tags from the world's largest collection of knowledge. Thanks to DBpedia, which extracts structured information from Wikipedia and represents it in a flexible data model, these tags are reference to objects which are categorized automatically, keeping your and your friend's bookmarks and interests well organized.

Alchemy is a software package providing a series of algorithms for statistical relational learning and probabilistic logic inference, based on the Markov logic representation. Alchemy allows you to easily develop a wide range of AI applications, including: * Collective classification * Link prediction * Entity resolution * Social network modeling * Information extraction

OntoMat-Annotizer is a user-friendly interactive webpage annotation tool. It supports the user with the task of creating and maintaining ontology-based OWL-markups i.e. creating of OWL-instances, attributes and relationships. It include an ontology browser for the exploration of the ontology and instances and a HTML browser that will display the annotated parts of the text. It is Java-based and provide a plugin interface for extensions. The intended user is the individual annotator i.e., people that want to enrich their web pages with OWL-meta data. Instead of manually annotating the page with a text editor, say, emacs, OntoMat allows the annotator to highlight relevant parts of the web page and create new instances via drag?n?drop interactions. It supports the meta-data creation phase of the lifecycle. It is planned that a future version will contain an information extraction plugin, that offers a wizard which suggest which parts of the text are relevant for annotation. That aspect will help to ease the time-consuming annotation task.

MnM is an annotation tool which provides both automated and semi-automated support for annotating web pages with semantic contents. MnM integrates a web browser with an ontology editor and provides open APIs to link to ontology servers and for integrating information extraction tools.

SCRIBO - Semi-automatic and Collaborative Retrieval of Information Based on Ontologies - aims at algorithms and collaborative free software for the automatic extraction of knowledge from texts and images, and for the semi-automatic annotation of digital documents.

In this paper an application of methodologies to automatically acquire semantic word classes and to use them in text information retrieval systems is described.

Built on top of Calais and scalably hosted on Amazon's EC2 service, the new site at SemanticProxy.com enters public beta today, and enables anyone to easily generate rich semantic metadata for pages on the open web, simply by passing the URL to SemanticProxy.