During a non-stop, two-hour keynote address at its annual I/O developers conference, Google unveiled a barrage of new products and updates. Here's a rundown of the most important things discussed: Google CEO Sundar Pichai kicked off the keynote by unveiling a new computer-vision system coming soon to Google Assistant. Apparently, as Pichai explained, you'll be able to point your phone's camera at something, and the phone will understand what it's seeing. Pichai gave examples of the system recognizing a flower, a series of restaurants on a street in New York (and automatically pulling in their ratings and information from Google), and the network name and password for a wifi router from the back of the router itself--the phone then automatically connecting to the network. Theoretically, in the future, you'll be searching the world not through text or your voice, but by pointing your camera at things.
This short paper is describing a demonstrator that is complementing the paper "Towards Cross-Media Feature Extraction" in these proceedings. The demo is exemplifying the use of textual resources, out of which semantic information can be extracted, for supporting the semantic annotation and indexing of associated video material in the soccer domain. Entities and events extracted from textual data are marked-up with semantic classes derived from an ontology modeling the soccer domain. We show further how extracted Audio-Video features by video analysis can be taken into account for additional annotation of specific soccer event types, and how those different types of annotation can be combined.
LG has placed its trust on Google Assistant and has given it the power to control its smart appliances. While it teamed up with Amazon earlier this year to give its refrigerators built-in access to Alexa, its partnership with Google is much bigger in scale. Now, you can control any of the company's 87 WiFi-connected smart home appliances by barking out orders through a Google Home speaker or through a compatible iOS or Android smartphone. Once you're done setting voice control up through LG's SmartThinQ app, you can use commands within a Home speaker's range or through a phone to tell your fridge to make more ice or to tell your AC to adjust the temperature. If you have an LG washing machine, you can ask Assistant how much time is still left before your load is done.
There is a growing need for scalable semantic web repositories which support inference and provide efficient queries. There is also a growing interest in representing uncertain knowledge in semantic web datasets and ontologies. In this paper, I present a bit vector schema specifically designed for RDF (Resource Description Framework) datasets. I propose a system for materializing and storing inferred knowledge using this schema. I show experimental results that demonstrate that this solution simplifies inference queries and drastically improves results. I also propose and describe a solution for materializing and persisting uncertain information and probabilities. Thresholds and bit vectors are used to provide efficient query access to this uncertain knowledge. My goal is to provide a semantic web repository that supports knowledge inference, uncertainty reasoning, and Bayesian networks, without sacrificing performance or scalability.
The Semantic Web language RDF was designed to unambiguously define and use ontologies to encode data and knowledge on the Web. Many people find it difficult, however, to write complex RDF statements and queries because doing so requires familiarity with the appropriate ontologies and the terms they define. We describe a system that suggests appropriate RDF terms given semantically related English words and general domain and context information. We use the Swoogle Semantic Web search engine to provide RDF term and namespace statistics, the WorldNet lexical ontology to find semantically related words, and a naïve Bayes classifier to suggest terms. A customized graph data structure of related namespaces is constructed from Swoogle's database to speed up the classifier model learning and prediction time.