Folksannotation: a Semantic Metadata Tool for Annotating Learning Resources Using Folksonomies and Domain Ontologies
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EVERYTHING YOU NEED to KNOW ABOUT SEMANTIC SEO by GENNARO CUOFANO, ANDREA VOLPINI, and MARIA SILVIA SANNA the Semantic Web Is Here
WHITE PAPER EVERYTHING YOU NEED TO KNOW ABOUT SEMANTIC SEO BY GENNARO CUOFANO, ANDREA VOLPINI, AND MARIA SILVIA SANNA The Semantic Web is here. Those that are taking advantage of Semantic Technologies to build a Semantic SEO strategy are benefiting from staggering results. From a research paper put together with the team atWordLift , presented at SEMANTiCS 2017, we documented that structured data is compelling from the digital marketing standpoint. For instance, on the analysis of the design-focused website freeyork.org, after three months of using structured data in their WordPress website we saw the following improvements: • +12.13% new users • +18.47% increase in organic traffic • +2.4 times increase in page views • +13.75% of sessions duration In other words, many still think of Semantic Technologies belonging to the future, when in reality quite a few players in the digital marketing space are taking advantage of them already. Semantic SEO is a new and powerful way to make your content strategy more effective. In this article/guide I will explain from scratch what Semantic SEO is and why it’s important. Why Semantic SEO? In a nutshell, search engines need context to understand a query properly and to fetch relevant results for it. Contexts are built using words, expressions, and other combinations of words and links as they appear in bodies of knowledge such as encyclopedias and large corpora of text. Semantic SEO is a marketing technique that improves the traffic of a website byproviding meaningful data that can unambiguously answer a specific search intent. It is also a way to create clusters of content that are semantically grouped into topics rather than keywords. -
Studying Social Tagging and Folksonomy: a Review and Framework
Studying Social Tagging and Folksonomy: A Review and Framework Item Type Journal Article (On-line/Unpaginated) Authors Trant, Jennifer Citation Studying Social Tagging and Folksonomy: A Review and Framework 2009-01, 10(1) Journal of Digital Information Journal Journal of Digital Information Download date 02/10/2021 03:25:18 Link to Item http://hdl.handle.net/10150/105375 Trant, Jennifer (2009) Studying Social Tagging and Folksonomy: A Review and Framework. Journal of Digital Information 10(1). Studying Social Tagging and Folksonomy: A Review and Framework J. Trant, University of Toronto / Archives & Museum Informatics 158 Lee Ave, Toronto, ON Canada M4E 2P3 jtrant [at] archimuse.com Abstract This paper reviews research into social tagging and folksonomy (as reflected in about 180 sources published through December 2007). Methods of researching the contribution of social tagging and folksonomy are described, and outstanding research questions are presented. This is a new area of research, where theoretical perspectives and relevant research methods are only now being defined. This paper provides a framework for the study of folksonomy, tagging and social tagging systems. Three broad approaches are identified, focusing first, on the folksonomy itself (and the role of tags in indexing and retrieval); secondly, on tagging (and the behaviour of users); and thirdly, on the nature of social tagging systems (as socio-technical frameworks). Keywords: Social tagging, folksonomy, tagging, literature review, research review 1. Introduction User-generated keywords – tags – have been suggested as a lightweight way of enhancing descriptions of on-line information resources, and improving their access through broader indexing. “Social Tagging” refers to the practice of publicly labeling or categorizing resources in a shared, on-line environment. -
On Using Product-Specific Schema.Org from Web Data Commons: an Empirical Set of Best Practices
On using Product-Specific Schema.org from Web Data Commons: An Empirical Set of Best Practices Ravi Kiran Selvam Mayank Kejriwal [email protected] [email protected] USC Information Sciences Institute USC Information Sciences Institute Marina del Rey, California Marina del Rey, California ABSTRACT both to advertise and find products on the Web, in no small part Schema.org has experienced high growth in recent years. Struc- due to the ability of search engines to make good use of this data. tured descriptions of products embedded in HTML pages are now There is also limited evidence that structured data plays a key role not uncommon, especially on e-commerce websites. The Web Data in populating Web-scale knowledge graphs such as the Google Commons (WDC) project has extracted schema.org data at scale Knowledge Graph that are essential to modern semantic search from webpages in the Common Crawl and made it available as an [17]. RDF ‘knowledge graph’ at scale. The portion of this data that specif- However, even though most e-commerce platforms have their ically describes products offers a golden opportunity for researchers own proprietary datasets, some resources do exist for smaller com- and small companies to leverage it for analytics and downstream panies, researchers and organizations. A particularly important applications. Yet, because of the broad and expansive scope of this source of data is schema.org markup in webpages. Launched in data, it is not evident whether the data is usable in its raw form. In the early 2010s by major search engines such as Google and Bing, this paper, we do a detailed empirical study on the product-specific schema.org was designed to facilitate structured (and even knowl- schema.org data made available by WDC. -
Instructions on the Annotation of Pdf Files
P-annotatePDF-v11b INSTRUCTIONS ON THE ANNOTATION OF PDF FILES To view, print and annotate your chapter you will need Adobe Reader version 9 (or higher). This program is freely available for a whole series of platforms that include PC, Mac, and UNIX and can be downloaded from http://get.adobe.com/reader/. The exact system requirements are given at the Adobe site: http://www.adobe.com/products/reader/tech-specs.html. Note: if you opt to annotate the file with software other than Adobe Reader then please also highlight the appropriate place in the PDF file. PDF ANNOTATIONS Adobe Reader version 9 Adobe Reader version X and XI When you open the PDF file using Adobe Reader, the To make annotations in the PDF file, open the PDF file using Commenting tool bar should be displayed automatically; if Adobe Reader XI, click on ‘Comment’. not, click on ‘Tools’, select ‘Comment & Markup’, then click If this option is not available in your Adobe Reader menus on ‘Show Comment & Markup tool bar’ (or ‘Show then it is possible that your Adobe Acrobat version is lower Commenting bar’ on the Mac). If these options are not than XI or the PDF has not been prepared properly. available in your Adobe Reader menus then it is possible that your Adobe Acrobat version is lower than 9 or the PDF has not been prepared properly. This opens a task pane and, below that, a list of all (Mac) Comments in the text. PDF ANNOTATIONS (Adobe Reader version 9) The default for the Commenting tool bar is set to ‘off’ in version 9. -
Implementation of Intelligent Semantic Web Search Engine
International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181 Vol. 4 Issue 04, April-2015 Implementation of Intelligent Semantic Web Search Engine Dinesh Jagtap*1, Nilesh Argade1, Shivaji Date1, Sainath Hole1, Mahendra Salunke2 1Department of Computer Engineering, 2Associate Professor, Department of Computer Engineering Sinhgad Institute of Technology and Science, Pune, Maharashtra, India 411041. II. BACKGROUND Abstract—Search engines are design for to search particular The idea of search engine and info retrieval from search information for a large database that is from World Wide engine is not a new concept. The interesting thing about Web. There are lots of search engines available. Google, traditional search engine is that different search engine will yahoo, Bing are the search engines which are most widely provide different result for the same query. While used search engines in today. The main objective of any search engines is to provide particular or required information was available in web, we have some fields of information with minimum time. The semantics web search problem in search engines. Information retrieval by engines are the next version of traditional search engines. The searching information on the web is not a fresh idea but has main problem of traditional search engines is that different challenges when it is compared to general information retrieval from the database is difficult or takes information retrieval. Different search engines return long time. Hence efficiency of search engines is reduced. To different search results due to the variation in indexing and overcome this intelligent semantic search engines are search process. Google, Yahoo, and Bing have been out introduced. -
Expert Knowledge Management Based on Ontology in a Digital Library
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by idUS. Depósito de Investigación Universidad de Sevilla EXPERT KNOWLEDGE MANAGEMENT BASED ON ONTOLOGY IN A DIGITAL LIBRARY Antonio Martín, Carlos León Departamento de Tecnología Electrónica, Seville University, Avda. Reina Mercedes S/N, Seville, Spain [email protected], [email protected] Keywords: Ontology, web services, Case-based reasoning, Digital Library, knowledge management, Semantic Web. Abstract: The architecture of the future Digital Libraries should be able to allow any users to access available knowledge resources from anywhere and at any time and efficient manner. Moreover to the individual user, there is a great deal of useless information in addition to the substantial amount of useful information. The goal is to investigate how to best combine Artificial Intelligent and Semantic Web technologies for semantic searching across largely distributed and heterogeneous digital libraries. The Artificial Intelligent and Semantic Web have provided both new possibilities and challenges to automatic information processing in search engine process. The major research tasks involved are to apply appropriate infrastructure for specific digital library system construction, to enrich metadata records with ontologies and enable semantic searching upon such intelligent system infrastructure. We study improving the efficiency of search methods to search a distributed data space like a Digital Library. This paper outlines the development of a Case- Based Reasoning prototype system based in an ontology for retrieval information of the Digital Library University of Seville. The results demonstrate that the used of expert system and the ontology into the retrieval process, the effectiveness of the information retrieval is enhanced. -
Type Annotations Specification
Type Annotations Specification (JSR 308) Michael D. Ernst [email protected] May 2, 2013 The JSR 308 webpage is http://types.cs.washington.edu/jsr308/. It contains the latest version of this document, along with other information such as a FAQ, the reference implementation, and sample annotation processors. Contents 1 Introduction 2 2 Java language syntax extensions 2 2.1 Source locations for annotations on types . 2 2.1.1 Implicit type uses are not annotatable . 5 2.1.2 Not all type names are annotatable . 5 2.2 Java language grammar changes . 6 2.2.1 Syntax of array annotations . 8 2.2.2 Syntax of annotations on qualified types . 9 2.3 Target meta-annotations for type annotations . 9 3 Class file format extensions 11 3.1 The type annotation structure . 12 3.2 The target type field: the type of annotated element . 13 3.3 The target info field: identifying a program element . 13 3.3.1 Type parameters . 13 3.3.2 Class supertypes: extends and implements clauses . 14 3.3.3 Type parameter bounds . 14 3.3.4 Method return type, receiver, and fields . 15 3.3.5 Method formal parameters . 15 3.3.6 throws clauses . 15 3.3.7 Local variables and resource variables . 15 3.3.8 Exception parameters (catch clauses) . 16 3.3.9 Type tests, object creation, and method/constructor references . 16 3.3.10 Casts and type arguments to constructor/method invocation/references . 16 3.4 The type path structure: Identifying part of a compound type . 17 A Examples of classfile structure 18 A.1 Examples of type parameter bounds . -
Supporting Source Code Annotations with Metadata-Aware Development Environment
Proceedings of the Federated Conference on DOI: 10.15439/2019F161 Computer Science and Information Systems pp. 411–420 ISSN 2300-5963 ACSIS, Vol. 18 Supporting Source Code Annotations with Metadata-Aware Development Environment Ján Juhár Department of Computers and Informatics Technical University of Košice Letná 9, 042 00 Košice, Slovakia Email: [email protected] Abstract—To augment source code with high-level metadata Recovering approaches use intrinsic metadata, which either with the intent to facilitate program comprehension, a program- define source code elements themselves or can be derived from mer can use annotations. There are several types of annotations: these elements. In contrast to them, preserving approaches either those put directly in the code or external ones. Each type comes with a unique workflow and inherent limitations. focus on extrinsic metadata, which complement the intrinsic In this paper, we present a tool providing uniform annotation ones by adding custom, high-level details explicitly recorded process, which also adds custom metadata-awareness for an by programmers. On one side, the more accurate preserved industrial IDE. We also report an experiment in which we sought knowledge may help to bridge the abstraction gap better than whether the created annotating support helps programmers to the lower-level recovered one. On the other side, recording annotate code with comments faster and more consistently. The experiment showed that with the tool the annotating consistency programmer’s mental model of the code brings in an additional was significantly higher but also that the increase in annotating cost: the programmer must spend extra time to record it. -
User Needs/Requirements: Social Reading/Text Annotation
NYU-UAG User Needs/Requirements: Social Reading/Text Annotation Immediate Needs ● Text-specific annotations (with ability to color-code or otherwise mark the author of the annotation) in following forms: ○ Text, including rich-text editor and to use mathematical notation (minimally, for underline, bold, italics; ideally, for embed of images, video, footnotes, etc.) ○ Link ○ Audio response/voiceover (both simultaneous narration and video stopped while comment plays) ○ Comment on annotations ● Collaborative Annotation/Social Reading ○ Ability to designate members who can see/post/share annotations ○ Ability to create private sub-groups ○ Ability for all sub-group members to post annotations (with annotations that can be set either to be visible only to sub-group members or beyond the sub-group) ○ Ability for a group member to post queries (annotations that ask for additional information/response from the group), with optional notification to other group members ○ Ability to thread responses and to easily open all responses in a thread at once ○ Ability for instructor to save annotations for future use (with student permission; option to anonymize) ○ Option to anonymize comments to students but reveal author to instructor ● Markers to show where annotations are placed ● Optional time-stamps on annotations ● Option to set deadline for submission of comments; ability to grade comments and send grade to gradebook; ability, when appropriate, to machine-grade annotations ● Annotation overlay on major document formats (pdf, google docs, Word) and -
Semantic Search on the Web
Semantic Web — Interoperability, Usability, Applicability 0 (0) 1 1 IOS Press Semantic Search on the Web Bettina Fazzinga a and Thomas Lukasiewicz b Semantic Web technology for interpreting Web search a Dipartimento di Elettronica, Informatica e queries and resources relative to one or more under- Sistemistica, Università della Calabria, Italy lying ontologies, describing some background domain E-mail: [email protected] knowledge, in particular, by connecting the Web re- b Computing Laboratory, University of Oxford, UK sources to semantic annotations, or by extracting se- E-mail: [email protected] mantic knowledge from Web resources. Such a search usually also aims at allowing for more complex Web search queries whose evaluation involves reasoning over the Web. Another common use of the notion of semantic search on the Web is the one as search in the Abstract. Web search is a key technology of the Web, since large datasets of the Semantic Web as a future substi- it is the primary way to access content on the Web. Cur- tute of the current Web. This second use is closely re- rent standard Web search is essentially based on a combi- nation of textual keyword search with an importance rank- lated to the first one, since the above semantic annota- ing of the documents depending on the link structure of the tion of Web resources, or alternatively the extraction of Web. For this reason, it has many limitations, and there are a semantic knowledge from Web resources, actually cor- plethora of research activities towards more intelligent forms responds to producing a knowledge base, which may of search on the Web, called semantic search on the Web, be encoded using Semantic Web technology. -
Schema Org Markup for Organization Logos
Schema Org Markup For Organization Logos Unspectacular Shelley bopping extempore while Devon always gurgled his xenon decongest sevenfold, he stupefied so coequally. stalagmometersRetroflexed and allodialpiecemeal. Michael never shog his celandine! Unreposeful and horned Darius pausing her cribbers ooze okay and Another example of information, or a local business markup for schema markup to get a huge source cmses, if your ecommerce store Schema markup is level of the strong potent forms of SEO however concern also happens to. Data said the off page showing the website social channels and logo. Why structured data and schema markup are no Flare. How both Add Schemaorg Markup to WordPress for Better SEO. SCHEMAORG Basic Markup SEO Agency Serpact. Healthcare Schema Markup and move It especially to Your SEO. What Google's Support of Schemaorg Logo Data iProspect. How people Add Schema Markup to WordPress and Kinsta. Schemaorg is a collaborative community activity with a mission to uphold maintain and. Schemaorg Tutorial w3resource. Use Schemaorg's organization markup to notice to Google the preferred logo Google has integrity they should honor that presumably in the absence of a Google. What and sending data types, seo plugin settings fields array output schema org markup for organization logos coming down on plugin settings page post type of, schema on your html tag. Org's Organization Markup in your SEO strategic plan Business Visibility on Google A squat of simple code on your website and you're logo will be. Wondering if you use html page. After all this search engines exist such as entities intertwine and it is a search on. -
Concept Based Semantic Search Engine
San Jose State University SJSU ScholarWorks Master's Projects Master's Theses and Graduate Research Spring 2014 Concept Based Semantic Search Engine Pradeep Roy San Jose State University Follow this and additional works at: https://scholarworks.sjsu.edu/etd_projects Part of the Computer Sciences Commons Recommended Citation Roy, Pradeep, "Concept Based Semantic Search Engine" (2014). Master's Projects. 351. DOI: https://doi.org/10.31979/etd.sjj6-fxju https://scholarworks.sjsu.edu/etd_projects/351 This Master's Project is brought to you for free and open access by the Master's Theses and Graduate Research at SJSU ScholarWorks. It has been accepted for inclusion in Master's Projects by an authorized administrator of SJSU ScholarWorks. For more information, please contact [email protected]. Concept Based Semantic Search Engine A Writing Project Presented to The Faculty of the Department of Computer Science San José State University In Partial Fulfillment Of the Requirements for the Degree Master of Science By Pradeep Roy May 2014 © 2014 Pradeep Roy ALL RIGHTS RESERVED The Designated Committee Approves the Writing Project Titled CONCEPT BASED SEMANTIC SEARCH ENGINE By Pradeep Roy APPROVED FOR THE DEPARTMENT OF COMPUTER SCIENCE SAN JOSÉ STATE UNIVERSITY May 2014 Dr. Tsau Young Lin Department of Computer Science Dr. Jon Pearce Department of Computer Science Mr. Amitaba Das IBM SVL Research Center ABSTRACT In the current day and age, search engines are the most relied on and critical ways to find out information on the World Wide Web (W3). With the ushering in of Big Data, traditional search engines are becoming inept and inadequate at dishing out relevant pages.