DOCSLIB.ORG

The Rise of KNOWLEDGE GRAPHS

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Rise of KNOWLEDGE GRAPHS THOUGHT LEADERSHIP SERIES The Rise of KNOWLEDGE GRAPHS 2019 | MAR THOUGHT LEADERSHIP SERIES | March 2019 2 MAKING NEW CONNECTIONS WITH KNOWLEDGE GRAPHS In today’s business world, time-to-insight and time-to-action are critical competitive differentiators. The demand for quick, easy access to information is growing. However, the challenge of combining data into meaningful information is also mounting—alongside the proliferation of data sources and types. While companies nowadays are investing heavily in initiatives to increase the amount of data at their disposal, most are spending more time finding than analyzing data. Data silos remain a huge problem at many enterprises, and legacy data management technologies and processes are having trouble keeping up with the speed, scalability, and flexibility requirements of new workloads and use cases. Data volumes are exploding in will be driven by specific applications Graph databases, a type of NoSQL organizations and the pressure to exploit and increasing expertise. “NoSQL” data database that employs structures that data for faster time to insight is management approaches, features, and with nodes, edges, and properties to increasing. Along with this store data and places data growth, there is a new a heavy emphasis wave of tools to expand The ability for knowledge graphs to gather on relationships, are what is possible with the growing in use. More proliferation of data and information, relationships, and insights—and recently, the term data types. connect those facts—allows organizations to “knowledge graph” While relational databases in particular has are still both the biggest discern context in data, which is important for gained popularity with source of transactional as well as complying with the introduction of data in most organizations extracting value several high-profile and the greatest source of increasingly stringent data privacy regulations. implementations by growth for transactional tech giants. data, according to a recent The ability for Unisphere Research study (“Emerging functionality vary widely and in many knowledge graphs to gather information, Alternatives for Data Management” situations, can provide an attractive relationships, and insights—and connect sponsored by AWS), the use of NoSQL is and effective solution for non-relational those facts—allows organizations to an emerging trend—the growth of which workloads. discern context in data, which is THOUGHT LEADERSHIP SERIES | March 2019 3 important for extracting value as well as complying with increasingly stringent data privacy regulations. Moreover, The use of knowledge graphs spans fields such as with the use of AI expanding across healthcare, life sciences, financial services, intelligence, enterprises in all industries, knowledge graphs offer the potential to improve telecommunications, and other fields to support fraud real-time insights and support machine learning. detection, recommendation engines, master data management and Customer 360 views, identity and THE RISE OF KNOWLEDGE GRAPHS There are many factors contributing to access management, and data compliance for the expansion of technologies that allow regulatory standards, and other initiatives. people and machines to better understand connections in their datasets so decisions can be made faster. In particular, the ability to combine semantic and graph technologies that process data with contextual and conceptual intelligence is valued since it can enable predictive analytics that can help support better, real- explains analyst Amy Stapleton in an advisor, Radiant Advisors, discusses the time decisions. Opus Research article. “An IA [intelligent transformation underway as companies The concept of the enterprise knowledge assistant] that taps into an EKG can infer increasingly realize the power of data, and graph is made possible by machine learning the context and intent of questions, generate the tools and technologies helping them to and big data technologies, including direct answers, make recommendations, and expand what is possible. “I think in the next automated text analytics and graph engines, automatically expand its understanding as year [2019], we will see more sophistication the knowledge graph adds new content,” around data lake management. An exciting she noted. area that we are keeping an eye on is The use of knowledge graphs the role of the graph databases to apply spans fields such as healthcare, management to the data. We see some early life sciences, financial traction there with the use of the property services, intelligence, graph or the knowledge graph to relate telecommunications, everything to the data lake.” and more to improve Gartner also recently identified the usability of data knowledge graphs as a key new technology lakes, and support in both its Hype Cycle for Artificial fraud detection, Intelligence and Hype Cycle for Emerging recommendation Technologies. Gartner’s Hype Cycle for engines, master data Artificial Intelligence, 2018 states, “The management and rising role of content and context for Customer 360 views, delivering insights with AI technologies, as identity and access well as recent knowledge graph offerings management, and for AI applications have pulled knowledge data compliance for graphs to the surface.” regulatory standards and other initiatives. KNOWLEDGE GRAPH MILESTONES The rise of knowledge Google’s launch in 2012 of its own graphs is not going knowledge graph powered in part by its unnoticed by industry acquisition of Freebase, is viewed by many experts. In a recent interview as having helped focus attention on graph with Big Data Quarterly, John technology. The knowledge graph is being O’Brien, CEO and principal pursued behind the scenes to enhance THOUGHT LEADERSHIP SERIES | March 2019 4 search. Described as “the next frontier in search,” the Google knowledge graph collects information about people, places, With the growing range of data sources, and and things, enhances the value of search results by gather information from sources with the increasing speed of data flowing into across the web, including the CIA World organizations, the use of knowledge graphs can Factbook, which provides information on the history, people, government, be expected to play a key role in enterprise data economy, geography, communications, transportation, military, and transnational management. In today’s fast-paced business world, issues for 267 world entities; Wikidata, a free and open knowledge base that can the speed of information access can mean the be read and edited by both humans and machines; Wikipedia, the free online difference between timely action and lost opportunity. encyclopedia, and other sources. The goal of Google’s knowledge graph was to not only give users a more complete picture of a topic they were searching, but also to improve the accuracy and speed at which they could find relevant information. Within 7 months of launching, it grew Amazon Web Servicesis also semantic nature of knowledge graphs makes to more than 18 billion facts and is still enabling knowledge graphs using them well-suited for managing and storing growing today as the foundation of systems the metaphactory platform backed data from diverse sources with context and that support AI capabilities in newer robots by Amazon Neptune. A January 2019 relevance. n and smartphones. Amazon blog post by Kunal Sengupta In another key milestone for knowledge points out that “knowledge graphs are graphs, Franz, an innovator in AI and gaining prominence in enterprise data supplier of the semantic graph database management because they offer advantages technology, AllegroGraph, announced for data integration. They also help build in 2018 that it is joining forces with smarter applications that use machine Semantic Web Company, developer of the learning and artificial intelligence (AI) PoolParty Semantic Suite, to pioneer the methods.” It goes on to explain that first knowledge graph for a public figure. Neptune supports open source and The Noam Chomsky Knowledge Graph open-standard API operations semantically links books, interviews, and allows users to take advantage movies, TV programs, and written work of existing information resources from Chomsky, an American linguist, to build their knowledge graphs philosopher, cognitive scientist, historian, and host them on a fully political activist, and social critic, who is managed service. sometimes called “the father of modern linguistics.” The content is being made SPEED OF INSIGHT available by searching the knowledge With the growing range of data graph for specific titles, related topics, sources, and with the increasing and concepts, and since the project is speed of data flowing into based on the latest and most advanced organizations, the use of knowledge technologies, the data is also being made graphs can be expected to play a key available as machine-readable information role in enterprise data management. in order to be fed into smart applications, In today’s fast-paced business world, intelligent chatbots, and question/ the speed of information access can answering machines, as well as other AI and mean the difference between timely data systems. action and lost opportunity. The flexible, sponsored content THOUGHT LEADERSHIP SERIES | March 2019 5 AI Knowledge Graph Solutions: Franz Inc. Gartner recently identified Knowledge and predictive analytics from highly “Triple attributes in AllegroGraph add Graphs as a key new technology in both complex, distributed data
Load more

Recommended publications
  • Semantic Search
    Semantic Search Philippe Cudre-Mauroux Definition ter grasp the semantics (i.e., meaning) and the context of the user query and/or Semantic Search regroups a set of of the indexed content in order to re- techniques designed to improve tra- trieve more meaningful results. ditional document or knowledge base Semantic Search techniques can search. Semantic Search aims at better be broadly categorized into two main grasping the context and the semantics groups depending on the target content: of the user query and/or of the indexed • techniques improving the relevance content by leveraging Natural Language of classical search engines where the Processing, Semantic Web and Machine query consists of natural language Learning techniques to retrieve more text (e.g., a list of keywords) and relevant results from a search engine. results are a ranked list of documents (e.g., webpages); • techniques retrieving semi-structured Overview data (e.g., entities or RDF triples) from a knowledge base (e.g., a knowledge graph or an ontology) Semantic Search is an umbrella term re- given a user query formulated either grouping various techniques for retriev- as natural language text or using ing more relevant content from a search a declarative query language like engine. Traditional search techniques fo- SPARQL. cus on ranking documents based on a set of keywords appearing both in the user’s Those two groups are described in query and in the indexed content. Se- more detail in the following section. For mantic Search, instead, attempts to bet- each group, a wide variety of techniques 1 2 Philippe Cudre-Mauroux have been proposed, ranging from Natu- matical tags (such as noun, conjunction ral Language Processing (to better grasp or verb) to individual words.
    [Show full text]
  • SEKI@Home, Or Crowdsourcing an Open Knowledge Graph
    SEKI@home, or Crowdsourcing an Open Knowledge Graph Thomas Steiner1? and Stefan Mirea2 1 Universitat Politècnica de Catalunya – Department LSI, Barcelona, Spain [email protected] 2 Computer Science, Jacobs University Bremen, Germany [email protected] Abstract. In May 2012, the Web search engine Google has introduced the so-called Knowledge Graph, a graph that understands real-world en- tities and their relationships to one another. It currently contains more than 500 million objects, as well as more than 3.5 billion facts about and relationships between these different objects. Soon after its announce- ment, people started to ask for a programmatic method to access the data in the Knowledge Graph, however, as of today, Google does not provide one. With SEKI@home, which stands for Search for Embedded Knowledge Items,weproposeabrowserextension-basedapproachtocrowdsource the task of populating a data store to build an Open Knowledge Graph. As people with the extension installed search on Google.com, the ex- tension sends extracted anonymous Knowledge Graph facts from Search Engine Results Pages (SERPs) to a centralized, publicly accessible triple store, and thus over time creates a SPARQL-queryable Open Knowledge Graph. We have implemented and made available a prototype browser extension tailored to the Google Knowledge Graph, however, note that the concept of SEKI@home is generalizable for other knowledge bases. 1Introduction 1.1 The Google Knowledge Graph With the introduction of the Knowledge Graph, the search engine Google has made a significant paradigm shift towards “things, not strings” [7], as a post on the official Google blog states. Entities covered by the Knowledge Graph include landmarks, celebrities, cities, sports teams, buildings, movies, celestial objects, works of art, and more.
    [Show full text]
  • Seo Report for Brands July 2019
    VOICE ASSISTANT SEO REPORT FOR BRANDS JULY 2019 GIVING VOICE TO A REVOLUTION Table of Contents About Voicebot About Magic + Co An Introduction to Voice Search Today // 3 Voicebot produces the leading independent Magic + Co. is a next-gen creative services firm research, online publication, newsletter and podcast whose core expertise is in designing, producing, and Consumer Adoption Trends // 10 focused on the voice and AI industries. Thousands executing on conversational strategy, the technology of entrepreneurs, developers, investors, analysts behind it, and associated marketing campaigns. Voice Search Results Analysis // 16 and other industry leaders look to Voicebot each Grading the Experts // 32 week for the latest news, data, analysis and insights Facts defining the trajectory of the next great computing • 25 people, technologists, strategists, and Practical Voice Assistant SEO Strategies // 36 platform. At Voicebot, we give voice to a revolution. creatives with focus on conversational design and technologies. Additional Resources // 39 • 1 Webby Award Nominee and 3 Honorees. • 3 years old and global leader. Methodology • We’ve worked primarily with CPG, but our practice extends into utilities, governments, Consumer survey data was collected online during banking, entertainment, healthcare and is the first week of January 2019 and included 1,038 international in scope. U.S. adults age 18 or older that were representative of U.S. Census demographic averages. Findings were compared to earlier survey data collected in Magic + Co. the first week of September 2018 that included responses from 1,040 U.S. adults. Voice search data results from Amazon Echo Smart Speakers with Alexa, Apple HomePod with Siri, Google Home and a Smartphone with Google Assistant, and Samsung Galaxy S10 with Bixby, were collected in Q2 2019.
    [Show full text]
  • Arxiv:1812.02903V1 [Cs.LG] 7 Dec 2018 Ized Server
    APPLIED FEDERATED LEARNING: IMPROVING GOOGLE KEYBOARD QUERY SUGGESTIONS Timothy Yang*, Galen Andrew*, Hubert Eichner* Haicheng Sun, Wei Li, Nicholas Kong, Daniel Ramage, Franc¸oise Beaufays Google LLC, Mountain View, CA, U.S.A. ftimyang, galenandrew, huberte haicsun, liweithu, kongn, dramage, [email protected] ABSTRACT timely suggestions are necessary in order to maintain rele- vance. On-device inference and training through FL enable Federated learning is a distributed form of machine learn- us to both minimize latency and maximize privacy. ing where both the training data and model training are decen- In this paper, we use FL in a commercial, global-scale tralized. In this paper, we use federated learning in a commer- setting to train and deploy a model to production for infer- cial, global-scale setting to train, evaluate and deploy a model ence – all without access to the underlying user data. Our to improve virtual keyboard search suggestion quality with- use case is search query suggestions [4]: when a user enters out direct access to the underlying user data. We describe our text, Gboard uses a baseline model to determine and possibly observations in federated training, compare metrics to live de- surface search suggestions relevant to the input. For instance, ployments, and present resulting quality increases. In whole, typing “Let’s eat at Charlie’s” may display a web query sug- we demonstrate how federated learning can be applied end- gestion to search for nearby restaurants of that name; other to-end to both improve user experiences and enhance user types of suggestions include GIFs and Stickers. Here, we privacy.
    [Show full text]
  • Knolwedge Acquisi$On from Music Digital Libraries
    Knolwedge Acquision from Music Digital Libraries Sergio Oramas, Mohamed Sordo Mo0vaon • Musicological Knowledge is hidden between the lines • Machines don’t know how to read Why Knowledge Acquisi0on? • Obtain knowledge automacally • Make complex ques0ons • Visualize the informaon • Improve navigaon • Share knowledge 4 Musical Libraries Musical Libraries digital recording, scan Musical Libraries OCR, manual transcrip0on digital recording, scan Musical Libraries Informaon Extrac0on, seman0c annotaon OCR, manual transcrip0on digital recording, scan Musical Libraries Informaon Extrac0on, seman0c annotaon Current DL OCR, manual transcrip0on digital recording, scan Musical Libraries Web search Informaon Extrac0on, seman0c annotaon Current DL OCR, manual transcrip0on digital recording, scan Musical Libraries Web search Informaon Extrac0on, seman0c annotaon Current DL OCR, manual transcrip0on digital recording, scan Seman0c Web • The Semanc Web aims at conver0ng the current web, dominated by unstructured and semi-structured documents into a web of linked data. • Achievements useful for Digital Libraries – Common framework for data representaon and interconnec0on (RDF, ontologies) – Seman0c technologies to annotate texts (En0ty Linking) – Language for complex queries (SPARQL) Wikipedia and DBpedia - Digital Encyclopedia - Knowledge Base - Unstructured - Structured - Keyword search - Query search 13 14 15 DBpedia • Dbpedia example queries – Composers born in Vienna in XVIII Century – American jazz musicians that have wri\en songs recorded by RCA
    [Show full text]
  • Google's Knowledge Graph
    Google’s Knowledge Graph Google has just announced that it will completely reshape the presentation of its search results. Instead of listing websites where people can find the answer to their queries, it will present data, links, pictures, etc. from its own databases, some 500 million “items”. The redesign is said to represent the biggest change in search for the last five years, not just conceptually but also in terms of business impact. The goal, of course, is to make sure that web surfers remain on Google properties instead of clicking away to another site for answers. Internet companies all over the world already complain and regulators can add the move to their list of Google features to investigate from an antitrust perspective. The announcement is also a subtle tactical move, just one day before Facebook’s IPO. It reinforces Google’s positioning as the owner of the “knowledge network” as opposed to the “social network”. It is hard to anticipate what the new ‘product’ will really be but there are a few important pointers. First, it will make connections between the different information elements. At the moment, search provides a list of sources but does not really categorize them. Knowledge Graph will and may even link the sources to one another. People (not least Tim Berners-Lee) have always argued for the, so called ‘semantic web’ to replace the traditional web. Google’s Knowledge Graph is a fine approximation of the idea. Another aspect of the service will, hopefully, have to do with formatting. We may get a more standardized presentation of the information, which is quite important for rapid processing.
    [Show full text]
  • Summarized by © Lakhasly.Com Real-Time Data Feeds Although It
    Real-time Data Feeds Although it doesn’t promote itself as such, Google is actually a collection of data and a set of tools for working with it. It has progressed from an index of web pages to a central hub for real-time data feeds on just about anything that can be measured such as weather reports, travel reports, stock market and shares, shopping suggestions, travel suggestions, and several other things. Sorting Tools Big Data analysis which implies utilizing tools intended to deal with and comprehend this massive data becomes an integral factor whenever users carry out a search query. The Google’s algorithms run complex calculations intended to match the questions that user entered with all the available data. It will try to determine whether the user is searching for news, people, facts or statistics, and retrieve the data from the appropriate feed. Knowledge Graph Pages Google Knowledge Graph is a tool or database which collects all the data and facts about people, places and things along with proper differentiation and relationship between them. It is then later used by Google in solving our queries with useful answers. Google knowledge graph is user-centric and it provides them with useful relevant information quickly and easily. Literal & Semantic search The main aim of the literal search engine is to find the root of your search phrase by looking for a match for some of the word or entire phrase. The root of the phrase is then examined and explored upon to display better search results. While semantic search engine tries to understand the context of the phrase by analyzing the terms and language in knowledge graph database to directly answer a question with specific information.
    [Show full text]
  • Messageontap: a Suggestive Interface to Facilitate Messaging-Related Tasks
    MessageOnTap: A Suggestive Interface to Facilitate Messaging-related Tasks Fanglin Chen Kewei Xia Human-Computer Interaction Institute Electronic Information School Carnegie Mellon University Wuhan University Pittsburgh, PA, USA Wuhan, China [email protected] [email protected] Karan Dhabalia Jason I. Hong Electrical and Computer Engineering Human-Computer Interaction Institute Carnegie Mellon University Carnegie Mellon University Pittsburgh, PA, USA Pittsburgh, PA, USA [email protected] [email protected] ABSTRACT KEYWORDS Text messages are sometimes prompts that lead to infor- Messaging/Communication, Information Seeking & Search; mation related tasks, e.g. checking one’s schedule, creating Contextual Computing; Personal Data/Tracking; User Expe- reminders, or sharing content. We introduce MessageOnTap, rience Design; Text/Speech/Language; Productivity a suggestive interface for smartphones that uses the text in ACM Reference Format: a conversation to suggest task shortcuts that can stream- Fanglin Chen, Kewei Xia, Karan Dhabalia, and Jason I. Hong. 2019. line likely next actions. When activated, MessageOnTap MessageOnTap: A Suggestive Interface to Facilitate Messaging- uses word embeddings to rank relevant external apps, and related Tasks. In CHI Conference on Human Factors in Computing parameterizes associated task shortcuts using key phrases Systems Proceedings (CHI 2019), May 4–9, 2019, Glasgow, Scotland mentioned in the conversation, such as times, persons, or Uk. ACM, New York, NY, USA, 14 pages. https://doi.org/10.1145/ events. MessageOnTap also tailors the auto-complete dictio- 3290605.3300805 nary based on text in the conversation, to streamline any 1 INTRODUCTION text input. We first conducted a month-long study of mes- Messaging apps are among the most popular apps for smart- saging behaviors (N=22) that informed our design.
    [Show full text]
  • Adding Realtime Coverage to the Google Knowledge Graph
    Adding Realtime Coverage to the Google Knowledge Graph Thomas Steiner1?, Ruben Verborgh2, Raphaël Troncy3, Joaquim Gabarro1, and Rik Van de Walle2 1 Universitat Politècnica de Catalunya – Department lsi, Spain {tsteiner,gabarro}@lsi.upc.edu 2 Ghent University – IBBT, ELIS – Multimedia Lab, Belgium {ruben.verborgh,rik.vandewalle}@ugent.be 3 EURECOM, Sophia Antipolis, France [email protected] Abstract. In May 2012, the Web search engine Google has introduced the so-called Knowledge Graph, a graph that understands real-world entities and their relationships to one another. Entities covered by the Knowledge Graph include landmarks, celebrities, cities, sports teams, buildings, movies, celestial objects, works of art, and more. The graph enhances Google search in three main ways: by disambiguation of search queries, by search log-based summarization of key facts, and by explo- rative search suggestions. With this paper, we suggest a fourth way of enhancing Web search: through the addition of realtime coverage of what people say about real-world entities on social networks. We report on a browser extension that seamlessly adds relevant microposts from the social networking sites Google+, Facebook, and Twitter in form of a panel to Knowledge Graph entities. In a true Linked Data fashion, we inter- link detected concepts in microposts with Freebase entities, and evaluate our approach for both relevancy and usefulness. The extension is freely available, we invite the reader to reconstruct the examples of this paper to see how realtime opinions may have changed since time of writing. 1 Introduction With the introduction of the Knowledge Graph, the search engine Google has made a significant paradigm shift towards “things, not strings” [3], as a post on the official Google blog states.
    [Show full text]
  • Enhancing Profile and Context Aware Relevant Food Search Through
    proceedings Article Enhancing Profile and Context Aware Relevant Food Search through Knowledge Graphs † Unai Zulaika *,‡, Asier Gutiérrez ‡ and Diego López-de-Ipiña ‡ DeustoTech—University of Deusto, Avda. Universidades 24, 48007 Bilbao, Spain; [email protected] (A.G.); [email protected] (D.L.-d.-I.) * Correspondence: [email protected] † Presented at the 12th International Conference on Ubiquitous Computing and Ambient Intelligence (UCAmI 2018), Punta Cana, Dominican Republic, 4–7 December 2018. ‡ These authors contributed equally to this work. Published: 25 October 2018 Abstract: Foodbar is a Cloud-based gastroevaluation solution, leveraging IBM Watson cognitive services. It brings together machine and human intelligence to enable cognitive gastroevaluation of “tapas” or “pintxos” , i.e., small miniature bites or dishes. Foodbar matchmakes users’ profiles, preferences and context against an elaborated knowledge graph based model of user and machine generated information about food items. This paper reasons about the suitability of this novel way of modelling heterogeneous, with diverse degree of veracity, information to offer more stakeholder satisfying knowledge exploitation solutions, i.e., those offering more relevant and elaborated, directly usable, information to those that want to take decisions regarding food in miniature. An evaluation of the information modelling power of such approach is performed highlighting why such model can offer better more relevant and enriched answers to natural language questions posed by users. Keywords: knowledge graphs; relevant search; recommendation systems; data models; inference; software architectures 1. Introduction Currently, many of us follow social opinion when choosing where to go for food. Particularly, when we go to a unknown environment. Google Places (https://developers.google.com/places/web- service/intro) and TripAdvisor (https://www.tripadvisor.com/) are two widely used services for this purpose.
    [Show full text]
  • Chapter 1 What Is a Knowledge Graph?
    Chapter 1 What Is a Knowledge Graph? 1.1 Introduction In recent years, knowledge graphs (KGs) have emerged as a major area in Artificial Intelligence (AI) [139]. Graphs have always been pervasive in the broader AI literature, but with the advent of large quantities of data on the Web (‘Big Data’) and in the broader commercial sphere, there emerged a need to enable machines to ‘understand’ and make use of this data in some productive analytical way. The inability of machines to truly understand English, and other ‘natural’ languages like it, with all their irregularities and nuances, has also been largely evident in the (unsuccessful) quest to achieve general AI and commonsense reasoning. Although much progress has been made in all of these domains, it is still very much the case that machines have an easier time processing structured data in the form of graphs, dictionaries and tables than in natural language. In modern history, Google was among the first big companies to recognize and couple this ability with that of providing richer search capabilities on the Web. In fact, the use of the term ‘Knowledge Graph’ in recent Computer Science articles, papers and posts, can be traced back to the Google Knowledge Graph, which was described in an influential blog post in the early 2010s. The basic motto behind the Google Knowledge Graph was to make search about things not strings [164]. In other words, it would allow search to evolve from simple string searching (with all its bells and whistles), to one that involved reasoning about entities, attributes and relationships.
    [Show full text]
  • “OK Google, How Do I...?” the Complete Manual
    Google Home The Complete Manual “OK Google, 9001 how do I...?” Introducing Google Home Learn all about your new smart device and how the Google Assistant can help you Setup guide Control your smart home Set up and configure your Google Home or Use Google Home as the hub of your connected Google Home Mini home and control lights, heating and more Your My Day briefing Add more functions Teach your Google Home what content you Use IFTTT functionality to create and automate want to hear updates about every day command routines Cast media Explore apps Take advantage of Google Cast and get your Explore some of the wealth of apps and games Home to send media to your TV and speakers available for Google Home 9000 NEWGoogle Home The Complete Manual The independent handbook for Google Home users Master essential commands & more 100% UNOFFICIAL100% Digital Edition FIRST EDITION GoWelcomeog to le Home The Complete Manual Our homes are getting smarter. And who better to help us take advantage of this than the search engine we turn to with all of our queries? The Google Home is a revolutionary product in the same vein as the Amazon Echo and Apple HomePod. Where it differs is in its ability to leverage Google’s awesome powers of search, making it easy for it to answer any query you have, from news to weather to traffic and travel, in real time. In addition to that, it can take advantage of Google Cast technology to serve up music and video to your TV or stereo, function as the connected hub of your smart home, play audio- based games with you, and much more.
    [Show full text]