Enter the 21st century building's "digital twin." Think of it as a dynamic, virtual model of the physical structure, powered by the massive amounts of data that a single structure generates around the clock--everything from design specs to equipment parameters and live occupancy data. With IoT-enabled sensors tracking a building's "pulse" and feeding data back into next-generation systems such as Watson, facility owners and managers today are able to reconstruct every relevant metric from a physical structure in a digital environment. Every asset--from the HVAC system to the vending machines--can be monitored and analyzed remotely. But how do you manage those assets over time?

Although several RDF knowledge bases are available through the LOD initiative, the ontology schema of such linked datasets is not very rich. In particular, they lack object properties. The problem of finding new object properties (and their instances) between any two given classes has not been investigated in detail in the context of Linked Data. In this paper, we present DART (Detecting Arbitrary Relations for enriching T-Boxes of Linked Data) - an unsupervised solution to enrich the LOD cloud with new object properties between two given classes. DART exploits contextual similarity to identify text patterns from the web corpus that can potentially represent relations between individuals. These text patterns are then clustered by means of paraphrase detection to capture the object properties between the two given LOD classes. DART also performs fully automated mapping of the discovered relations to the properties in the linked dataset. This serves many purposes such as identification of completely new relations, elimination of irrelevant relations, and generation of prospective property axioms. We have empirically evaluated our approach on several pairs of classes and found that the system can indeed be used for enriching the linked datasets with new object properties and their instances. We compared DART with newOntExt system which is an offshoot of the NELL (Never-Ending Language Learning) effort. Our experiments reveal that DART gives better results than newOntExt with respect to both the correctness, as well as the number of relations.

Marc C. Close is the co-founder and CEO of Bespokify. Based in SE Asia, the Bespokify technology allows patterns to be made on-demand for brands, which helps reduce overhead and inventory challenges. There are quite a few companies with Artificial Intelligence (AI) projects on the go -- some look promising, others gimmicky. I am going to check out some of these projects and analyse what's been happening behind the scenes.Project Muze is an experimental collaboration between European eCommerce site Zalando and Google. It was one of the first "machine learning" projects to dabble in fashion design, and it shows.

As easy as it is to make purchases in the era of tap-to-pay services, it's about to get easier still. Alipay (which handles purchases for Chinese shopping giant Alibaba) has launched what it says is the first payment system that uses facial recognition to complete the sale. If you visit one of KFC's KPRO restaurants in Hangzhou, China, you can pay for your panini or salad by smiling at a camera-equipped kiosk -- you need to verify the purchase on your phone, but you don't have to punch in digits or bring your phone up to an NFC reader. The system (Smile to Pay) is purportedly resistant to spoofing with photos and other tricks. It relies on both depth-sensing cameras and a "likeness detection algorithm" to make sure it's really you.

Artificial intelligence (AI) may be the next big thing for communications service providers (CSPs), but it's not clear yet exactly how they will use it or where it will have the biggest impact on their business. "Let's break it down a bit – it can be misleading," Telefónica Global Group's CIO, Phil Jordan, told attendees at the Executive Summit during TM Forum Live!. "We see clear use cases and value in cognitive and machine learning. Any decision we take in a systemic way, I have asked for a plan for when and where does that become a machine-learned activity? "It's the next transformation wave that is going to hit all of us – converting decision-making into something that isn't static rule-based," he adds. "I don't think that's a technology problem – it's here or it's coming." But is Telefónica making extensive use of AI today? "We made no use of it in the transformation," Jordan emphasized in discussing the company's massive digital transformation. "AI isn't a magic trick," he says, and it won't be useful unless operators transform their existing IT systems first. Indeed, that's the message we've been hearing from many of our members: AI is promising but it isn't reality – yet. In November we will publish an extensive Trend Analysis Report on AI and machine learning, analyzing the results of our surveys of CSPs and suppliers (choose the right one for you). Take the survey and you'll be entered into a draw for a $250 Amazon gift voucher. Certainly, new virtualized network functionality and new operational and business support systems are needed to take advantage of AI and machine learning (a form of AI), in customer facing applications such as virtual agents and chatbots and for end-to-end network and service management. "You have to teach it; you have to give the machine context all the time," Jordan explains. "You have to have a business that is ready and able to understand outcomes and go back and feed it into machine learning.

TOCHIGI – A test of self-driving bus services organized by the transport ministry kicked off at a michi no eki roadside rest area in Tochigi Prefecture on Saturday. The ministry hopes to launch the autonomous bus services in fiscal 2020, aiming to provide a means of transportation for elderly people living in hilly and mountainous areas with dwindling populations. On Saturday, a ceremony to mark the launch of the test and a test-ride event were held in the city of Tochigi. Among the participants was transport minister Keiichi Ishii. According to the ministry, 80 percent of michi no eki rest areas in Japan are in hilly and mountainous areas.

Since its messaging app debuted in June 2011, Line Corp. has shaken up the online communications landscape in Japan and morphed into a player in smartphone communications infrastructure. So Line is planting the seeds of success for what it thinks will be the next big thing: voice-based "AI agents." While this artificial-intelligence quest will pit the smaller Line against IT powerhouses Google, Apple and Amazon, among others, Line CEO Takeshi Idezawa likes his chances. "We are taking on a new challenge because we believe we have the assets to win the battle," Idezawa told The Japan Times in a recent interview. This is quite a change for a firm that owes its success to a prescient bet on smartphones less than a decade ago.

With summer ending and students going back to school, new KDnuggets Cartoon looks at a possible future Machine Learning Class. Teacher: Robbie, stop misbehaving or I will send you back to data cleaning. This cartoon was ably drawn by Jon Carter. Here are other KDnuggets Big Data, Data Mining, and Data Science Cartoons. Cartoon: Machine Learning - What They Think I Do Cartoon: the distance between Espresso and Cappuccino Cartoon: Taxes, Artificial Intelligence, and Humans Cartoon: What Happens When AI Masters the March Madness Causation or Correlation: Explaining Hill Criteria using xkcd Cartoon: Perfect Valentine's Dates Found With Data Analysis Cartoon: When Self-Driving Car Machine Learning takes you too far ... A Funny Look at Big Data and Data Science Cartoon: Thanksgiving, Big Data, and Turkey Data Science.

NEW DELHI: The Commerce and Industry Ministry has constituted an 18-member task force to explore possibilities to leverage artificial intelligence (AI) for economic transformation. In a statement, Commerce and Industry Minister Nirmala Sitharaman said with rapid development in the fields of information technology and hardware, the world is about to witness a fourth industrial revolution. Artificial intelligence, machine learning to impact workplace practices in India: Adobe According to a global report by software major Adobe, over 50 per cent respondents did not feel concerned by artificial intelligence (AI) or machine learning. "Driven by the power of big data, high computing capacity, artificial intelligence and analytics, Industry 4.0 aims to digitise the manufacturing sector," she added. The panel comprised experts, academics, researchers and industry leaders.