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Holly: AI-powered recruitment assistant for a seamless hiring process (hollyhires.ai).

Gpt Vetting: Expedite your tech recruitment process with Gpt-vetting, a swift and precise Artificial Intelligence assessment tool (micro1.ai).

ParallelGPT: Bulk processing ChatGPT on spreadsheets (parallelgpt.ai).

Semantic Wikis try to combine the strengths of Semantic Web (machine processable, data integration, complex queries) and Wiki (easy to use and contribute, strongly interconnected, collaborativeness) technologies.

The Semantic Web is a vision for extending the Web so that machines can more intelligently integrate and process the wealth of information that is available. Unlike HTML and ordinary XML, Semantic Web languages allow semantics (i.e., meaning) to be explicitly associated with the content. The semantics are formally specified in ontologies, which can be shared via the Internet and extended for local needs. The current standard for the Semantic Web is OWL, a W3C Recommendation. The Semantic Web and Agent Technologies (SWAT) lab is investigating many of the issues needed to realize the Semantic Web vision.

The YAGO-NAGA project started in 2006 with the goal of building a conveniently searchable, large-scale, highly accurate knowledge base of common facts in a machine-processible representation.

"The Open Relevance Project (ORP) is a small Apache LuceneTM sub-project aimed at making materials for doing relevance testing for Information Retrieval (IR), Machine Learning and Natural Language Processing (NLP) into open source."

The OWL Web Ontology Language is designed for use by applications that need to process the content of information instead of just presenting information to humans. OWL facilitates greater machine interpretability of Web content than that supported by XML, RDF, and RDF Schema (RDF-S) by providing additional expressive power along with a formal semantics. The group's work is complete with the publication of OWL 2 (Second Edition). There may be new Errata and discussion on public-owl-dev@w3.org.

In order to support data set owners to publish their datasets as Linked Data on the Web, as well as to support data consumers to access and integrate Linked Data from the Web, the LATC project has compiled a library of open source toolkits that cover all stages of the Linked Data publication (modeling, linking, serving) and consumption process (discovery, consolidation, application). By gathering high-quality open source tools in the form of a consistent library, we hope to lower the barriers to publishing Linked Data as well as to interacting with the Web of Data.

Semargl is a modular framework for crawling linked data from structured documents. The main goal of project is to provide lightweight and performant tool without excess dependencies.

The domain name registration facility provided to customers to solve their issues related to the process. A right host company can suggest you many ways of how to choose a perfect URL for your site as this about the online reputation.

This guest post includes important information regarding domain selection & registration process and tips to consider.

Green Turtle is an implementation of RDFa 1.1 for browsers. By simply including a bit of Javascript, the DOM is extended to include the RDFa API and an RDFa 1.1 processor is available to process any ancillary documents to harvest triples. For more information on the API, see RDFa API Working Draft at the W3C.

UBY is a large-scale lexical-semantic resource for natural language processing (NLP) based on the ISO standard Lexical Markup Framework (LMF). UBY combines a wide range of information from expert-constructed and collaboratively constructed resources for English and German. Currently, UBY holds structurally and semantically interoperable versions of nine resources in two languages:  English WordNet, Wiktionary, Wikipedia, FrameNet and VerbNet,  German Wikipedia, Wiktionary and GermaNet, and multilingual OmegaWiki. 

OntoWiktionary - Constructing an Ontology from the Collaborative Online Dictionary Wiktionary, chapter 6 in M. T. Pazienza and A. Stellato (Eds.): Semi-Automatic Ontology Development: Processes and Resources, p. 131-161, Hershey, PA: IGI Global, February 2012.

Our lab focuses on research and applications of Semantic Computing, Text Mining, Linked Data, Natural Language Processing (NLP), Intelligent Information Systems, and related technologies. We are committed to providing free, open source software and open research data to the community.

The goal of the Dicode project is to facilitate and augment collaboration and decision making in data-intensive and cognitively-complex settings. To do so, it will exploit and build on the most prominent high-performance computing paradigms and large data processing technologies - such as cloud computing, MapReduce, Hadoop, Mahout, and column databases - to meaningfully search, analyze and aggregate data existing in diverse, extremely large, and rapidly evolving sources. Building on current advancements, the solution foreseen in

lobid.org is the North Rhine-Westphalian Library Service Center's (hbz) Linked Open Data service. The akronym 'lobid' stands for "Linking Open Bibliographic Data". We support the process of creating Linked Open Bibliographic Data out of existing libraries and other associated data.