The World Wide Web changed the way we live our lives, most notably in the ways we now share, consume and find information. There are many more webpages now than there are people, and links connect these webpages to each other in a giant network that is accessible from your favorite browser.
A downside of this success is that now there’s too much information, so much in fact, that we need machines to intelligently read these webpages and answer our questions. The Semantic Web is a movement and research community that brings together experts from different areas, examples being natural language processing, ontologies, databases, social media, networks and logic, to realize the vision of making the Web machine-readable.
Why is this such a difficult problem? The main reason is that much of the Web, even today, is in a natural language like English or French. These languages are very ambiguous, but we humans have a knack for understanding them due to a variety of factors, not the least of which is our immense store of background knowledge and common sense. Machines are not yet capable of understanding English at the same level as an adult human being, though impressive progress is being made.
To overcome this problem, the Semantic Web presents a vision of the Web as an interlinked network of concepts, relationships and entities, rather than an interlinked network of ‘natural’ webpages. Intelligent systems, often called ‘agents’, can consume the Semantic Web and answer complex questions that now require human labor. The research in the Semantic Web also helps search; e.g. the Google Knowledge Graph, which uses Semantic Web technology, can help you to answer some of your questions without even clicking on a link!
The workshop was opened by David Halliwell, director of knowledge and innovation at interrnational firm Pinsent Masons, whose innovation team includes data scientists, knowledge and process engineers and machine learning experts. Uses for AI in law include process automation, document review – reviewing large volumes of similar documents to identify where a change in the law applies - as well as financial analytics and predicting litigation outcomes. In contrast to Pinsent Masons' home-grown solutions, magic circle firm Freshfields Bruckhaus Deringer's is taking a'holistic, commercial approach combining multiple commercial AI products, the firm's innovation architect Milos Kresojevic said. Its portfolio includes Kira Systems' contract analysis, Neota Logic's expert systems for smart (client facing) apps, blockchain and smart contracts, technology assisted review, and semantic web technologies.
Rychtyckyj, Nestor (AAAI) | Raman, Venkatesh (Ford Motor Company) | Sankaranarayanan, Baskaran (Indian Institute of Technology Madras) | Kuma, P. Sreenivasa (Indian Institute of Technology Madras) | Khemani, Deepak (Indian Institute of Technology Madras)
For over twenty-five years Ford Motor Company has been utilizing an AI-based system to manage process planning for vehicle assembly at its assembly plants around the world. The scope of the AI system, known originally as the Direct Labor Management System and now as the Global Study Process Allocation System (GSPAS), has increased over the years to include additional functionality on Ergonomics and Powertrain Assembly (Engines and Transmission plants). The knowledge about Ford's manufacturing processes is contained in an ontology originally developed using the KL-ONE representation language and methodology. In this article, we will discuss the process by which we re-engineered the existing GSPAS KL-ONE ontology and deployed semantic web technology in our application.
Google, Microsoft, and Yahoo have teamed up to encourage Web page operators to make the meaning of their pages understandable to search engines. The move may finally encourage widespread use of technology that makes online information as comprehensible to computers as it is to humans. By tagging information, Web page owners could improve the position of their site in search results--an important source of traffic. "They are saying you will get better results with semantic Web concepts," says Sporny, "and if they encourage more sites to embrace the semantic Web, that will help all kinds of other applications, too."
That's why many eyes will be on Twine, a Web organizer based on semantic technology that launches publicly today. Developed by Radar Networks, based in San Francisco, Twine is part bookmarking tool, part social network, and part recommendation engine, helping users collect, manage, and share online information related to any area of interest. After creating an account, a user adds a Twine bookmarklet to her browser's bookmarks, then adds items to her Twine page by clicking the bookmarklet as she surfs the Web. On the surface, Twine looks a lot like many other social-networking applications: users make connections, share, and discuss information, and the artificial intelligence, machine learning, and natural language processing built into the website is not immediately obvious.
Five years before, he'd agreed to lead a diverse group of researchers working on a project called the Semantic Web, which seeks to give computers the ability–the seeming intelligence–to understand content on the World Wide Web. In it, John Markoff defined Web 3.0 as a set of technologies that offer efficient new ways to help computers organize and draw conclusions from online data, and that definition has since dominated discussions at conferences, on blogs, and among entrepreneurs. Miller joined this community as a computer engineering student at Ohio State University, near the headquarters of a group called the Online Computer Library Center (OCLC). In early 1996, researchers at the MIT-based World Wide Web Consortium (W3C) asked Miller, then an Ohio State graduate student and OCLC researcher, for his opinion on a different type of metadata proposal.
But if anything, the terms "Semantic Web" or "Semantic Web technologies" are receiving less attention, points out Amit Sheth, educator, researcher, and entrepreneur whose roles include being the executive director of Kno.e.sis--the Ohio Center of Excellence in Knowledge-enabled Computing. Sheth notes that he's been on the same page for the last few years as it relates to his expectations about slow progress in broader adoption of Semantic Web standards and the technical challenges that hinder Linked Data usage: "One key challenge that continues to hinder more rapid adoption of Semantic Web and Linked Data is the lack of robust yet very easy-to-use tools when dealing with large and diverse data, [tools] that can do what tools like Weka did for Machine Learning," he says. "Indifferent quality, limited interlinking, and limited expressiveness of mappings between related data hinder broader adoption--while a few datasets that are extracted from actively maintained repositories (e.g., DBpedia from Wikipedia) and highly curated data continue to have the lion's share of applications," he says. "In other words, AI, with its much larger footprint in research and practice has realized that knowledge will propel machine understanding of (diverse) content," Sheth says.
I was eager to learn about the latest developments in the Semantic Web through the lens of a "new kind of semantics" as Abraham Bernstein et al. explored in their Viewpoint "A New Look at the Semantic Web" (Sept. If I understand it correctly, semantics is a mapping function that leads from manifest expressions to elements in a given arbitrary domain. Based on set theory, logicians have developed a framework to set up such mapping for formal languages like mathematics, provided one can fix an interpretation function. On the other hand, 20th-century logicians (notably Alfred Tarski) warned of the limits of the framework when applied to human languages. Now, to the extent it embraces a set-theoretic semantics (as in the W3C's Ontology Web Language), the Semantic Web seems to be facing exactly such limitations or experiencing, dealing with, and suffering them. Most Web content is expressed as natural language, and it is not easy for programmers to bring it into clean logical form; ...
Classes and Instances relate to the subjects, Relation and Descriptive Properties relate to the predicate, and Values relate to the object  in an RDF triple. First, many of AI's main sub-domains have a role to play with respect to data integration and interoperability: This places semantic Web technologies as a co-participant with natural language processing, knowledge mining, pattern recognizers, KR languages, reasoners, and machine learning as domains related to data interoperability. In the data integration context, master data models (and management, or MDM) attempt to provide common reference terms and objects to aid the integration effort. By standing back in this manner and asking these broader questions we can see a host of structures like reference concepts, reference attributes, reference places, reference identifiers, and the like, playing the roles of providing common groundings for integration and interoperation.
I gained access to this book free, via the sponsor of our non-profit's first year of operations, or I would not have bought it. The other books being read by our senior "working" technologist include: A New Ecology Perspective by Sven Jergensen et al (Elsevier, not on Amazon that I can find) Information Visualization: Beyond the Horizon Handbook of Data Visualization (Springer Handbooks of Computational Statistics) (Springer Handbooks of Computational Statistics) Most of what we are reading these days are research reports that are outrageously priced and really should be affordable books and also free online, but most authors are too willing to give away their intellectual property for a pittance at tis time. Personally, I am betting on humans linked with low cost information sharing and group sense-making tools, and I am NOT holding my breath for automated fusion, machine learning, artificial intelligence, or machine sense-making. See the image I have loaded under book cover for a sense of the nuances Earth Intelligence Network is exploring.