Posts Tagged ‘Ontologies’
News came the other week that Senzari had announced the MusicGraph knowledge engine for music. The Semantic Web Blog had a chance to learn a little bit more about it what’s underway thanks to a chat with Senzari’s COO Demian Bellumio.
MusicGraph used to go by the geekier name of Adaptable Music Parallel Processing Platform, or AMP3 for short, for helping users control their Internet radio. “We wanted to put more knowledge into our graph. The idea was we have really cool and interesting data that is ontologically connected in ways never done before,” says Bellumio. “We wanted to put it out in the world and let the world leverage it, and MusicGraph is a production of that vision.”
Since its announcement earlier this month about launching the consumer version on the Firefox OS platform that lets users make complex queries about music and learn and then listen to results, Senzari has submitted its technology to be offered for the iOS, Android, and Windows Mobile platforms. “You can ask anything you can think of in the music realm. We connect about 1 billion different points to respond to these queries,” he says. Its data covers more than twenty million songs, connected to millions of individual albums and artists across all genres, with extracted information on everything from keys to concept extractions derived from lyrics.
Sem Tech Solution For Materials Design And Development Wins Small Business Innovation Research Grant
A Small Business Innovation Research grant, sponsored by the U.S. Air Force through the Office of the Secretary of Defense, has gone to a semantic technology solution for materials design and development. The project is aligned with the Materials Genome Initiative that’s targeted to accelerate the pace of discovery and deployment of advanced material systems.
“This will help to showcase the semantic web technologies and methodologies in a large and meaningful domain and application area,” says Sam Chance, Innovation Evangelist and Special Programs Lead at iNovex Information Systems, which is the principal investigator and technical agent for development and integration for the project that will leverage W3C semantic standards. The project also includes as team members the University of Queensland, Penn State University, SRI International’s Materials Laboratory, and with Cambridge Semantics for access to its semantic tools to be bootstrapped onto the solution.
The University of Queensland brings to the effort a high-level domain ontology to represent structured knowledge about materials, their structure and properties and the processing steps involved in their composition and engineering, as well as certain case studies and data sources around jet fuels for hypersonic flight that are considered part of the materials design domain. The group at Penn State has been involved with the Materials Genome Initiative and also brings to the work particular case studies and data sources, this time for materials with nickel alloy as the base system. SRI has case studies and data sources for turbine blade coating.
RALEIGH, N.C.–(BUSINESS WIRE)– TopQuadrant™, a leading semantic data integration company, today announced the release of version 4.3 of TopBraid Enterprise Vocabulary Net (TopBraid EVN), a web-based solution that simplifies the development and management of interconnected vocabularies. With the latest release, TopBraid EVN can now be used to edit arbitrary RDFS/OWL ontologies and acts as a powerful platform for other semantic editing environments. Read more
Automation Technologies, Inc. is looking for a “Senior Java Developer with an Ontology background” in the Fort George Meade, Maryland area. The position requires an Active Top Secret/SCI (TS/SCI) with Polygraph Clearance. This is an additional position added to the ATI team.
While the main role is Java development, the person in this role “partners with senior customer leadership to provide strategic support to help organizations improve performance by using ontologies, triple stores and reasoning for analyzing situations, identifying gaps, and proposing solutions. Serves as a performance change agent; influences stakeholder decisions by “selling” strategic performance concepts. Uses a variety of performance strategies, tools, and interventions (i.e., organizational development, needs or trend analysis, needs assessment, training evaluation, etc.) to implement change, monitor solution implementation, and assure organizational success. Develops networks and alliances, and collaborates across boundaries with a wide range of stakeholders. Serves as an expert resource for customer executives. ”
Healthline Networks is seeking an Informatics Sr. Java Software Engineer in San Francisco, California. This position offers the opportunity to “Work with dynamic Engineering and Medical Informatics Teams to enhance Healthline’s medical ontology. Healthline is a web health technology company, employing tools for the future.” The Responsibilities include: “Design and implement algorithms for integrating medical standard dictionaries into Healthline ontology; Develop data mining processes to create our Semantic Network; Work on an authoring tool that allows Medical Informatics Specialists to successfully and easily edit our ontology; Read more
A favorite component of the Semantic Technology and Business Conference is the Lightning Round: an hour of five-minute talks given by excited executives and entrepreneurs fighting the clock to get across their ideas in just five minutes. If the participants went over the time limit (which was boldly displayed for all of us in the packed audience to monitor) they were very politely clapped off the stage. Surprisingly, many of the presenters had their speeches timed to the second, but others would have killed for fifteen more seconds.
At the end of the hour, the crowd of attendees left with brains stuffed to the brim with new ideas about a wide range of topics. The following are just a few highlights from the action-packed hour. Read more
[NOTE: This guest post is by Peter Haase, Lead Architect for Research and Development, fluid Operations.]
Industry engineers waste a significant amount of time searching for data that they require for their core tasks. When informed about potential problems, diagnosis engineers at Siemens Energy Services, an integrated business unit which runs service centers for power plants, need to access several terabytes of time-stamped sensor data and several gigabytes of event data, including both raw and processed data. These engineers have to respond to about 1,000 service requests per center per year, and end up spending 80% of their time on data gathering alone. What makes this problem even worse is that their data grows at a rate of 30 gigabytes per day. Similarly, at Statoil Exploration, geology and geographic experts spend between 30 and 70% of their time looking for and assessing the quality of some 1,000 terabytes of relational data using diverse schemata and spread over 2,000 tables and multiple individual databases . In such scenarios, it may take several days to formulate the queries that satisfy the information needs of the experts, typically involving the assistance of experienced IT experts who have been working with the database schemata for years.
Siemens and Statoil Exploration are hardly the only companies faced with time-wasting Big Data issues, but the root of these issues is not simply the “big” aspect of their data. The real challenge is finding a way to efficiently and effectively mine data for value and insight, regardless of its volume.
Inventorying and managing cultural heritage data turns out to be a pretty complicated undertaking. The construction of a famous site may have lasted across different time periods, and its present location may span multiple districts. Buildings may be associated not only with famous architects but also with well-known residents. Or structures may have been constructed atop pre-existing entities.
Helping sort it all out is the work of The Arches Project, collaboration between the Getty Conservation Institute (GCI) and World Monuments Fund (WMF). The Arches effort grew out of GCI’s and WMF’s work to develop MEGA-Jordan, a purpose-built geographic information system (GIS) to inventory and manage archaeology sites at a national level for that country. But for this more generic and open-source take at accommodating any country, region or other institution worldwide responsible for the protection of immovable cultural heritage, the focus expanded from the geo-spatial to the semantic.
“We became very familiar with the CIDOC Conceptual Reference Model ontology,” says Alison Dalgity, who manages the Arches project on GCI’s side. The CRM provides definitions and a formal structure for describing the implicit and explicit concepts and relationships used in cultural heritage documentation. “We realized we needed something like that. Now, the GIS piece is only part of this – it’s nice to know where something is, but all the other relationships – the who, how, what and when and so on – have to be represented, too.”
OWL, the Web Ontology Language has been standardized by W3C as a powerful language to represent knowledge (i.e. ontologies) on the Web. OWL has two functionalities. The first functionality is to express knowledge in an unambiguous way. This is accomplished by representing knowledge as set of concepts within a particular domain and the relationship between these concepts. If we only take into account this functionality, then the goal is very similar to that of UML or Entity-Relationship diagrams. The second functionality is to be able to draw conclusions from the knowledge that has been expressed. In other words, be able to infer implicit knowledge from the explicit knowledge. We call this reasoning and this is what distinguishes OWL from UML or other modeling languages.
OWL evolved from several proposals and became a standard in 2004. This was subsequently extended in 2008 by a second standard version, OWL 2. With OWL, you have the possibility of expressing all kinds of knowledge. The basic building blocks of an ontology are concepts (a.k.a classes) and the relationships between the classes (a.k.a properties). For example, if we were to create an ontology about a university, the classes would include Student, Professor, Courses while the properties would be isEnrolled, because a Student is enrolled in a Course, and isTaughtBy, because a Professor teaches a Course.
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