Jay Shah of Federal Times recently wrote, “In January 2014 a survey of more than 200 nonprofit and public-sector professionals found that three out of every four grant-seeking organizations is dependent on federal funds. With competition this fierce, federal grant managers are tasked with a heavy evaluation load. At the same time, Semantic Web and linked-data technologies are changing the way we access and interact with complex data environments, allowing for faster, more cost-efficient analysis and a shorter road to discovering substantive correlations.” Read more
Professor Dr. Christian Bizer of the University of Mannheim, Germany, has announced the release of DBpedia 2014. DBpedia is described at dbpedia.org as “… a crowd-sourced community effort to extract structured information from Wikipedia and make this information available on the Web. DBpedia allows you to ask sophisticated queries against Wikipedia, and to link the different data sets on the Web to Wikipedia data. We hope that this work will make it easier for the huge amount of information in Wikipedia to be used in some new interesting ways. Furthermore, it might inspire new mechanisms for navigating, linking, and improving the encyclopedia itself.”
The full announcement on the new release is reprinted below with Bizer’s permission.
DBpedia Version 2014 released
1. the new release is based on updated Wikipedia dumps dating from April / May 2014 (the 3.9 release was based on dumps from March / April 2013), leading to an overall increase of the number of things described in the English edition from 4.26 to 4.58 million things.
2. the DBpedia ontology is enlarged and the number of infobox to ontology mappings has risen, leading to richer and cleaner data.
The English version of the DBpedia knowledge base currently describes 4.58 million things, out of which 4.22 million are classified in a consistent ontology (http://wiki.dbpedia.org/Ontology2014), including 1,445,000 persons, 735,000 places (including 478,000 populated places), 411,000 creative works (including 123,000 music albums, 87,000 films and 19,000 video games), 241,000 organizations (including 58,000 companies and 49,000 educational institutions), 251,000 species and 6,000 diseases. Read more
Semantic data integration vendor TopQuadrant’s TopBraid Suite 4.5 just hit the street, a major release that CMO and VP of Professional Services Robert Coyne says “provides a large number of new and enhanced capabilities driven primarily by customers who are using our TopBraid Enterprise Vocabulary Net solution for vocabulary and/or metadata management, or using TopBraid Live to create a custom, model-driven solution.”
The latest version, he says, features more business user and enterprise readiness-motivated improvements than any past major release since Release 4.0, when the current generation of the TopBraid EVN product was first introduced. Many of the enhancements were inspired by requests coming from different customers using TopBraid in different contexts, he notes.
New capabilities in EVN range from improved configurability for the EVN Ontology Editor, via a form builder that allows browser window management and enables users to open multiple view forms, tree and chart windows, to an improved search form that makes it possible to search on cardinalities, regular expressions, aggregates in the search counts and chart results.
Also part of the upgrade is increased support for business stakeholders who need to collaborate on defining and linking enterprise vocabularies, taxonomies and metadata used for information sharing, data integration and search. Features like that reflect the fact that a growing number of enterprise customers and business users are looking to leverage products such as TopBraid EVN, Coyne says.
On his personal website, Frederick Giasson reports, “We just released a new UMBEL web service endpoint and online tool: the Concept Tagger Plain. This plain tagger uses UMBEL reference concepts to tag an input text. The OBIE (Ontology-Based Information Extraction) method is used, driven by the UMBEL reference concept ontology. By plain we mean that the words (tokens) of the input text are matched to either the preferred labels or alternative labels of the reference concepts. The simple tagger is merely making string matches to the possible UMBEL reference concepts.” Read more
LightReading reports, “Ontology Systems, the semantic search company for enterprise application data, today announces the launch of Intelligent 360 for Network Operators (i360-NetOps), a product to help operators gain a reliable, fast and holistic view of their network across all layers, technologies and vendors. i360-NetOps helps organisations to carry out network troubleshooting, navigate their infrastructure and the customers that depend on it, handle their change management and track the alignment and quality of the data that describes the network and its services.” Read more
Cognitum’s year got off to a good start, with an investment from the Giza Polish Ventures Fund, and it plans to apply some of that funding to building its sales and development teams, demonstrating the approaches to and benefits of semantic knowledge engineering, and focusing on big implementations for recognizable customers. The company’s products include Fluent Editor 2 for editing and manipulating complex ontologies via controlled natural language (CNL) tools, and its NLP-fronted Ontorion Distributed Knowledge Management System for managing large ontologies in a distributed fashion (both systems are discussed in more detail in our story here). “The idea here is to open up semantic technologies more widely,” says CEO Pawel Zarzycki.
To whom? Zarzycki says the company currently has pilot projects underway in the banking sector, which see opportunities to leverage ontologies and semantic management frameworks that provide a more natural way for sharing and reusing knowledge and expressing business rules for purposes such as lead generation and market intelligence. In the telco sector, another pilot project is underway to support asset management and impact assessment efforts, and in the legal arena, the Poland-based company is working with the Polish branch of international legal company Eversheds on applying semantics to legal self-assessment issues. Having a semantic knowledge base can make it possible to automate the tasks behind assessing a legal issue, he says, and so it opens the door to outsourcing this job directly to the entity pursuing the case, with the lawyer stepping in mostly at the review stage. That saves a lot of time and money.
Ontologies are getting a thumbs up to serve as the basis for the Office of Financial Research’s Instruments database. Last week, the Data & Technology Subcommittee of the OFR Financial Research Advisory Committee (FRAC) recommended that the OFR “adopt the goal of developing and validating a comprehensive ontology for financial instruments as part of its overall effort to meet its statutory requirement to ‘prepare and publish’ a financial instrument reference database.”
The Instruments database will define the official meaning of financial instruments for the financial system — derivatives, securities, and so on. The recommendation by the subcommittee is that the OFR conduct its own evaluation of private sector initiatives in this area, including the Financial Industry Business Ontology (FIBO), to assess whether and how ontology can support transparency and financial stability analysis.
FIBO, which The Semantic Web Blog discussed in detail most recently here, is designed to improve visibility to the financial industry and the regulatory community by standardizing the language used to precisely define the terms, conditions, and characteristics of financial instruments; the legal and relationship structure of business entities; the content and time dimensions of market data; and more. The effort is spearheaded by the Object Management Group and the Enterprise Data Management (EDM) Council.
Everyone knows The Clapper for turning electric equipment on and off, right? Sing along: “Clap-on, clap-off….The Clapper.”
Things have come a long way since then, with security, energy management, and more coming along to help turn the average house into a smarter home. Now comes a chance to take things to another level, with semantic-based resource discovery and orchestration in home and building automation. Research led by Michele Ruta, assistant professor at Technical University of Bari, takes on the challenge of bringing together the worlds of semantic web and automation in order to improve what Ruta says are very poor user interaction scenarios.
How? In home automation solutions today, he says, the user is limited to very basic scenarios and very static interaction that requires pre-programming the capabilities the home can assume. “It should be possible to have dynamic interaction, more intelligent interaction with the user, and decisions should be done according to user interest, to a user’s profile,” Ruta says. Semantic technology can be called upon to annotate users’ profiles, interests, and needs against home automation profile options, and make the match between them, he says.
Look, up in the sky! It’s a bird, it’s a plane, no – it’s an Amazon drone!
Admittedly, Amazon Prime Air’s unmanned aerial vehicles in commercial use are still a little ways off. But such technology – along with other recent innovations, such as the use of unmanned aircraft in crop-dusting or even Department of Homeland Security border applications, or future capabilities to extend the notion of auto-piloting in passenger airplanes using autonomous machine logic to control airspace and spacing between planes –needs to be accounted for in terms of its impact on the air space. The Next-Generation Air Transportation System is taking on the change in the management and operation of the national air transportation system.
And semantic technology, natural language processing, and machine learning, too, will have a hand in helping out, by fostering collaboration among the agencies that will be working together to develop the system, including the Federal Aviation Administration, the U.S. Air Force, U.S. Navy, and the National Aeronautics and Space Administration, under the coordination of the Joint Planning and Development Office. These agencies will need to leverage each other’s knowledge and research, as well as ensure – as necessary – data privacy.
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