Semantic Web Technologies Public Knowledge Graphs
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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. -
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. -
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. -
Google Knowledge Graph, Bing Satori and Wolfram Alpha * Farouk Musa Aliyu and Yusuf Isah Yahaya
International Journal of Scientific & Engineering Research Volume 12, Issue 1, January-2021 11 ISSN 2229-5518 An Investigation of the Accuracy of Knowledge Graph-base Search Engines: Google knowledge Graph, Bing Satori and Wolfram Alpha * Farouk Musa Aliyu and Yusuf Isah Yahaya Abstract— In this paper, we carried out an investigation on the accuracy of two knowledge graph driven search engines (Google knowledge Graph and Bing’s Satori) and a computational knowledge system (Wolfram Alpha). We used a dataset consisting of list of books and their correct authors and constructed queries that will retrieve the author(s) of a book given the book’s name. We evaluate the result from each search engine and measure their precision, recall and F1 score. We also compared the result of these two search engines to the result from the computation knowledge engine (Wolfram Alpha). Our result shows that Google performs better than Bing. While both Google and Bing performs better than Wolfram Alpha. Keywords — Knowledge Graphs, Evaluation, Information Retrieval, Semantic Search Engines.. —————————— —————————— 1 INTRODUCTION earch engines have played a significant role in helping leveraging knowledge graphs or how reliable are the result S web users find their search needs. Most traditional outputted by the semantic search engines? search engines answer their users by presenting a list 2. How are the accuracies of semantic search engines as of ranked documents which they believe are the most relevant compare with computational knowledge engines? to their -
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. -
Towards a Knowledge Graph for Science
Towards a Knowledge Graph for Science Invited Article∗ Sören Auer Viktor Kovtun Manuel Prinz TIB Leibniz Information Centre for L3S Research Centre, Leibniz TIB Leibniz Information Centre for Science and Technology and L3S University of Hannover Science and Technology Research Centre at University of Hannover, Germany Hannover, Germany Hannover [email protected] [email protected] Hannover, Germany [email protected] Anna Kasprzik Markus Stocker TIB Leibniz Information Centre for TIB Leibniz Information Centre for Science and Technology Science and Technology Hannover, Germany Hannover, Germany [email protected] [email protected] ABSTRACT KEYWORDS The document-centric workflows in science have reached (or al- Knowledge Graph, Science and Technology, Research Infrastructure, ready exceeded) the limits of adequacy. This is emphasized by Libraries, Information Science recent discussions on the increasing proliferation of scientific lit- ACM Reference Format: erature and the reproducibility crisis. This presents an opportu- Sören Auer, Viktor Kovtun, Manuel Prinz, Anna Kasprzik, and Markus nity to rethink the dominant paradigm of document-centric schol- Stocker. 2018. Towards a Knowledge Graph for Science: Invited Article. arly information communication and transform it into knowledge- In WIMS ’18: 8th International Conference on Web Intelligence, Mining and based information flows by representing and expressing informa- Semantics, June 25–27, 2018, Novi Sad, Serbia. ACM, New York, NY, USA, tion through semantically rich, interlinked knowledge graphs. At 6 pages. https://doi.org/10.1145/3227609.3227689 the core of knowledge-based information flows is the creation and evolution of information models that establish a common under- 1 INTRODUCTION standing of information communicated between stakeholders as The communication of scholarly information is document-centric. -
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. -
New Generation of Social Networks Based on Semantic Web Technologies: the Importance of Social Data Portability
Workshop on Adaptation and Personalization for Web 2.0, UMAP'09, June 22-26, 2009 New Generation of Social Networks Based on Semantic Web Technologies: the Importance of Social Data Portability Liana Razmerita1, Martynas Jusevičius2, Rokas Firantas2 Copenhagen Business School, Denmark1 IT University of Copenhagen, Copenhagen, Denmark2 [email protected], [email protected], [email protected] Abstract. This article investigates several well-known social network applications such as Last.fm, Flickr and identifies social data portability as one of the main technical issues that need to be addressed in the future. We argue that this issue can be addressed by building social networks as Semantic Web applications with FOAF, SIOC, and Linked Data technologies, and prove it by implementing a prototype application using Java and core Semantic Web standards. Furthermore, the developed prototype shows how features from semantic websites such as Freebase and DBpedia can be reused in social applications and lead to more relevant content and stronger social connections. 1 Introduction Social networking sites are developing at a very fast pace, attracting more and more users. Their application domain can be music sharing, photos sharing, videos sharing, bookmarks sharing, professional networks, and others. Despite their tremendous success social networking websites have a number of limitations that are identified and discussed within this article. Most of the social networking applications are “walled websites” and the “online communities are like islands in a sea”[1]. Lack of interoperability between data in different social networks applications limits access to relevant content available on different social networking sites, and limits the integration and reuse of available data and information.