Adding Realtime Coverage to the Google Knowledge Graph
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A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage
heritage Article A Comparative Evaluation of Geospatial Semantic Web Frameworks for Cultural Heritage Ikrom Nishanbaev 1,* , Erik Champion 1,2,3 and David A. McMeekin 4,5 1 School of Media, Creative Arts, and Social Inquiry, Curtin University, Perth, WA 6845, Australia; [email protected] 2 Honorary Research Professor, CDHR, Sir Roland Wilson Building, 120 McCoy Circuit, Acton 2601, Australia 3 Honorary Research Fellow, School of Social Sciences, FABLE, University of Western Australia, 35 Stirling Highway, Perth, WA 6907, Australia 4 School of Earth and Planetary Sciences, Curtin University, Perth, WA 6845, Australia; [email protected] 5 School of Electrical Engineering, Computing and Mathematical Sciences, Curtin University, Perth, WA 6845, Australia * Correspondence: [email protected] Received: 14 July 2020; Accepted: 4 August 2020; Published: 12 August 2020 Abstract: Recently, many Resource Description Framework (RDF) data generation tools have been developed to convert geospatial and non-geospatial data into RDF data. Furthermore, there are several interlinking frameworks that find semantically equivalent geospatial resources in related RDF data sources. However, many existing Linked Open Data sources are currently sparsely interlinked. Also, many RDF generation and interlinking frameworks require a solid knowledge of Semantic Web and Geospatial Semantic Web concepts to successfully deploy them. This article comparatively evaluates features and functionality of the current state-of-the-art geospatial RDF generation tools and interlinking frameworks. This evaluation is specifically performed for cultural heritage researchers and professionals who have limited expertise in computer programming. Hence, a set of criteria has been defined to facilitate the selection of tools and frameworks. -
One Knowledge Graph to Rule Them All? Analyzing the Differences Between Dbpedia, YAGO, Wikidata & Co
One Knowledge Graph to Rule them All? Analyzing the Differences between DBpedia, YAGO, Wikidata & co. Daniel Ringler and Heiko Paulheim University of Mannheim, Data and Web Science Group Abstract. Public Knowledge Graphs (KGs) on the Web are consid- ered a valuable asset for developing intelligent applications. They contain general knowledge which can be used, e.g., for improving data analyt- ics tools, text processing pipelines, or recommender systems. While the large players, e.g., DBpedia, YAGO, or Wikidata, are often considered similar in nature and coverage, there are, in fact, quite a few differences. In this paper, we quantify those differences, and identify the overlapping and the complementary parts of public KGs. From those considerations, we can conclude that the KGs are hardly interchangeable, and that each of them has its strenghts and weaknesses when it comes to applications in different domains. 1 Knowledge Graphs on the Web The term \Knowledge Graph" was coined by Google when they introduced their knowledge graph as a backbone of a new Web search strategy in 2012, i.e., moving from pure text processing to a more symbolic representation of knowledge, using the slogan \things, not strings"1. Various public knowledge graphs are available on the Web, including DB- pedia [3] and YAGO [9], both of which are created by extracting information from Wikipedia (the latter exploiting WordNet on top), the community edited Wikidata [10], which imports other datasets, e.g., from national libraries2, as well as from the discontinued Freebase [7], the expert curated OpenCyc [4], and NELL [1], which exploits pattern-based knowledge extraction from a large Web corpus. -
Wikipedia Knowledge Graph with Deepdive
The Workshops of the Tenth International AAAI Conference on Web and Social Media Wiki: Technical Report WS-16-17 Wikipedia Knowledge Graph with DeepDive Thomas Palomares Youssef Ahres [email protected] [email protected] Juhana Kangaspunta Christopher Re´ [email protected] [email protected] Abstract This paper is organized as follows: first, we review the related work and give a general overview of DeepDive. Sec- Despite the tremendous amount of information on Wikipedia, ond, starting from the data preprocessing, we detail the gen- only a very small amount is structured. Most of the informa- eral methodology used. Then, we detail two applications tion is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline that follow this pipeline along with their specific challenges built on top of DeepDive to successfully extract meaningful and solutions. Finally, we report the results of these applica- relations from the Wikipedia text corpus. We evaluated the tions and discuss the next steps to continue populating Wiki- system by extracting company-founders and family relations data and improve the current system to extract more relations from the text. As a result, we extracted more than 140,000 with a high precision. distinct relations with an average precision above 90%. Background & Related Work Introduction Until recently, populating the large knowledge bases relied on direct contributions from human volunteers as well With the perpetual growth of web usage, the amount as integration of existing repositories such as Wikipedia of unstructured data grows exponentially. Extract- info boxes. These methods are limited by the available ing facts and assertions to store them in a struc- structured data and by human power. -
The State of the News: Texas
THE STATE OF THE NEWS: TEXAS GOOGLE’S NEGATIVE IMPACT ON THE JOURNALISM INDUSTRY #SaveJournalism #SaveJournalism EXECUTIVE SUMMARY Antitrust investigators are finally focusing on the anticompetitive practices of Google. Both the Department of Justice and a coalition of attorneys general from 48 states and the District of Columbia and Puerto Rico now have the tech behemoth squarely in their sights. Yet, while Google’s dominance of the digital advertising marketplace is certainly on the agenda of investigators, it is not clear that the needs of one of the primary victims of that dominance—the journalism industry—are being considered. That must change and change quickly because Google is destroying the business model of the journalism industry. As Google has come to dominate the digital advertising marketplace, it has siphoned off advertising revenue that used to go to news publishers. The numbers are staggering. News publishers’ advertising revenue is down by nearly 50 percent over $120B the last seven years, to $14.3 billion, $100B while Google’s has nearly tripled $80B to $116.3 billion. If ad revenue for $60B news publishers declines in the $40B next seven years at the same rate $20B as the last seven, there will be $0B practically no ad revenue left and the journalism industry will likely 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 disappear along with it. The revenue crisis has forced more than 1,700 newspapers to close or merge, the end of daily news coverage in 2,000 counties across the country, and the loss of nearly 40,000 jobs in America’s newsrooms. -
Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications. -
Android Turn Off Google News Notifications
Android Turn Off Google News Notifications Renegotiable Constantine rethinking: he interlocks his freshmanship so-so and wherein. Paul catapult thrillingly while agrarian Thomas don phrenetically or jugulate moreover. Ignescent Orbadiah stilettoing, his Balaamite maintains exiles precious. If you click Remove instead, this means the website will be able to ask you about its notifications again, usually the next time you visit its homepage, so keep that in mind. Thank you for the replies! But turn it has set up again to android turn off google news notifications for. It safe mode advocate, android turn off google news notifications that cannot delete your android devices. Find the turn off the idea of android turn off google news notifications, which is go to use here you when you are clogging things online reputation and personalization company, defamatory term that. This will take you to the preferences in Firefox. Is not in compliance with a court order. Not another Windows interface! Go to the homepage sidebar. From there on he worked hard and featured in a series of plays, television shows, and movies. Refreshing will bring back the hidden story. And shortly after the Senate convened on Saturday morning, Rep. News, stories, photos, videos and more. Looking for the settings in the desktop version? But it gets worse. Your forum is set to use the same javascript directory for all your themes. Seite mit dem benutzer cookies associated press j to android have the bell will often be surveilled by app, android turn off google news notifications? This issue before becoming the android turn off google news notifications of android enthusiasts stack exchange is granted permission for its notification how to turn off google analytics and its algorithms. -
The Future of Voice and the Implications for News (Report)
DIGITAL NEWS PROJECT NOVEMBER 2018 The Future of Voice and the Implications for News Nic Newman Contents About the Author 4 Acknowledgements 4 Executive Summary 5 1. Methodology and Approach 8 2. What is Voice? 10 3. How Voice is Being Used Today 14 4. News Usage in Detail 23 5. Publisher Strategies and Monetisation 32 6. Future Developments and Conclusions 40 References 43 Appendix: List of Interviewees 44 THE REUTERS INSTITUTE FOR THE STUDY OF JOURNALISM About the Author Nic Newman is Senior Research Associate at the Reuters Institute and lead author of the Digital News Report, as well as an annual study looking at trends in technology and journalism. He is also a consultant on digital media, working actively with news companies on product, audience, and business strategies for digital transition. Acknowledgements The author is particularly grateful to media companies and experts for giving their time to share insights for this report in such an enthusiastic and open way. Particular thanks, also, to Peter Stewart for his early encouragement and for his extremely informative daily Alexa ‘flash briefings’ on the ever changing voice scene. The author is also grateful to Differentology and YouGov for the professionalism with which they carried out the qualitative and quantitative research respectively and for the flexibility in accommodating our complex and often changing requirements. The research team at the Reuters Institute provided valuable advice on methodology and content and the author is grateful to Lucas Graves and Rasmus Kleis Nielsen for their constructive and thoughtful comments on the manuscript. Also thanks to Alex Reid at the Reuters Institute for keeping the publication on track at all times. -
Personalized News Recommendation Based on Click Behavior Jiahui Liu, Peter Dolan, Elin Rønby Pedersen Google Inc
Personalized News Recommendation Based on Click Behavior Jiahui Liu, Peter Dolan, Elin Rønby Pedersen Google Inc. 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA {jiahui, peterdolan, elinp}@google.com ABSTRACT to thousands of sources via the internet. News aggregation Online news reading has become very popular as the web websites, like Google News and Yahoo! News, collect news provides access to news articles from millions of sources from various sources and provide an aggregate view of around the world. A key challenge of news websites is to news from around the world. A critical problem with news help users find the articles that are interesting to read. In service websites is that the volumes of articles can be this paper, we present our research on developing overwhelming to the users. The challenge is to help users personalized news recommendation system in Google find news articles that are interesting to read. News. For users who are logged in and have explicitly enabled web history, the recommendation system builds Information filtering is a technology in response to this profiles of users’ news interests based on their past click challenge of information overload in general. Based on a behavior. To understand how users’ news interest change profile of user interests and preferences, systems over time, we first conducted a large-scale analysis of recommend items that may be of interest or value to the anonymized Google News users click logs. Based on the user. Information filtering plays a central role in log analysis, we developed a Bayesian framework for recommender systems, as it is able to recommend predicting users’ current news interests from the activities information that has not been rated before and of that particular user and the news trends demonstrated in accommodates the individual differences between users [3, the activity of all users. -
Knowledge Graph Identification
Knowledge Graph Identification Jay Pujara1, Hui Miao1, Lise Getoor1, and William Cohen2 1 Dept of Computer Science, University of Maryland, College Park, MD 20742 fjay,hui,[email protected] 2 Machine Learning Dept, Carnegie Mellon University, Pittsburgh, PA 15213 [email protected] Abstract. Large-scale information processing systems are able to ex- tract massive collections of interrelated facts, but unfortunately trans- forming these candidate facts into useful knowledge is a formidable chal- lenge. In this paper, we show how uncertain extractions about entities and their relations can be transformed into a knowledge graph. The ex- tractions form an extraction graph and we refer to the task of removing noise, inferring missing information, and determining which candidate facts should be included into a knowledge graph as knowledge graph identification. In order to perform this task, we must reason jointly about candidate facts and their associated extraction confidences, identify co- referent entities, and incorporate ontological constraints. Our proposed approach uses probabilistic soft logic (PSL), a recently introduced prob- abilistic modeling framework which easily scales to millions of facts. We demonstrate the power of our method on a synthetic Linked Data corpus derived from the MusicBrainz music community and a real-world set of extractions from the NELL project containing over 1M extractions and 70K ontological relations. We show that compared to existing methods, our approach is able to achieve improved AUC and F1 with significantly lower running time. 1 Introduction The web is a vast repository of knowledge, but automatically extracting that knowledge at scale has proven to be a formidable challenge. -
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. -
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. -
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.