Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Semantic search helps business people find answers to pressing questions by wading through oceans of information to find nuggets of meaningful information. In this presentation we’ll discuss how semantic search and content analysis technologies are starting to appear in the marketplace today. We’ll provide a recap of what semantic search is and what the key benefits are, then we’ll answer the following questions:
• Is semantic search a feature, an application, or enterprise system?
• How can I add semantic search to my existing work processes?
• Will I need to replace my existing content technologies?
• What will I need to do to prepare my content for semantic search?
• Is semantic search just for documents or can I search my data too?
• Can I use semantic search to find information on the internet and other public data sources?
• Are there standards to consider?
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions?
In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set.
Webinar with Joe Depeau, Neo4j, April 15, 2020
Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...Connected Data World
Borislav Popov's slides from his lightning talk at Connected Data London. Borislav - a Director of Business Development at Ontotext presented Ontotext's approach to tackling the Panama Papers leak. Using a technology that is a mix between semantic web and graph databases.
Applications of Semantic Technology in the Real World TodayAmit Sheth
Amit Sheth, "Applications of Semantic Technology in the Real World Today," talk given at Semantic Technology Conference, San Jose, CA, March 2005.
This talk reviews real-world applications mainly deployed in financial services industry developed over Semagix Freedom platform described in http://knoesis.org/library/resource.php?id=810 . Technology is based on this patent: "Semantic web and its applications in browsing, searching, profiling, personalization and advertising", http://knoesis.org/library/resource.php?id=843 .
Amit Sheth founded Taalee in 1999, which merged with Voquette in 2002, and then with Semagix in 2004.
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Paris Sud University
Today, we are experiencing an unprecedented production of resources, published as Linked Open Data (LOD, for short). This is leading to the creation of knowledge graphs (KGs) containing billions of RDF (Resource Description Framework) triples, such as DBpedia, YAGO and Wikidata on the academic side, and the Google Knowledge Graph or Microsoft’s Satori graph on the commercial side. These KGs contain millions of entities (such as people, proteins, or books), and millions of facts about them. This knowledge is typically expressed in RDF (Resource Description Framework), i.e., as triples of the form ⟨Macron, presidentOf, France⟩. Some KGs provide an ontology expressed in OWL2 (Web Ontology Language), which describes the vocabulary (the classes and properties) for the RDF facts. However, to exploit and take benefits from the richness of this available data and knowledge, several problems have to be faced, namely, data linking, data fusion and knowledge discovery, when data is of big volume, heterogeneous and evolving. In this tutorial we will first give an overview of exiting data linking and key discovery approaches. Then, we will discuss the problem of identity crisis caused by the misuse of owl:sameAs predicate and give some possible solutions. We will finish by highlighting some current challenges in this research area.
Basic explanation about graph mining for social network analysis (SNA). I tried to describe some metrics and benefit from SNA (focusing on telecommunication field). Basic spark with graphx script to analyse the graph also in the slide
Methods for Intrinsic Evaluation of Links in the Web of DataCristina Sarasua
The current Web of Data contains a large amount of interlinked data. However, there is still a limited understanding about the quality of the links connecting entities of different and distributed data sets. Our goal is to provide a collection of indicators that help assess existing interlinking. In this paper, we present a framework for the intrinsic evaluation of RDF links, based on core principles of Web data integration and foundations of Information Retrieval. We measure the extent to which links facilitate the discovery of an extended description of entities, and the discovery of other entities in other data sets. We also measure the use of different vocabularies. We analysed links extracted from a set of data sets from the Linked Data Crawl 2014 using these measures.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Employees, Business Partners and Bad Guys: What Web Data Reveals About Person...Connotate
This presentation will discuss how to collect Web data with precision, transform it and then apply next-generation text analytics to reveal insights about the past activities of persons of interest and/or predict future outcomes. Featured guest speaker Claire Schmidt will discuss results of a project which proved the potential of using automated Web data collection and advanced analytics to identify potential child victims of exploitation.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
Christopher Gutteridge's slides form Connected Data London. Christopher, who is an Open Data Architect at the Univeristy of Southhampton presented why and how people should employ an Open Data strategy at their organisation.
Knowledge graphs ilaria maresi the hyve 23apr2020Pistoia Alliance
Data for drug discovery and healthcare is often trapped in silos which hampers effective interpretation and reuse. To remedy this, such data needs to be linked both internally and to external sources to make a FAIR data landscape which can power semantic models and knowledge graphs.
SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITYAmit Sheth
Amit Sheth, SEMANTIC CONTENT MANAGEMENT FOR ENTERPRISES AND NATIONAL SECURITY, Keynote at:
CONTENT- AND SEMANTIC-BASED INFORMATION RETRIEVAL @ SCI 2002.
How Graph Algorithms Answer your Business Questions in Banking and BeyondNeo4j
Graph algorithms are powerful tools, and there’s a lot of excitement about their applications for data science. It can sometimes be difficult, however - especially for those of us who aren’t data scientists - to know how they might be applied to a particular data set or a specific business problem. There are graph algorithms for centrality and importance measurement, community detection, similarity comparison, pathfinding, and link prediction. Which ones should you use on your data, and which ones might be most useful in answering your business questions?
In this presentation, we’ll look at a few examples of Neo4j graph algorithms, and see how they can be applied to data and business problems from the banking industry. We’ll discuss what kinds of data are appropriate for different types of algorithms, show how to model and structure data to work with graph algorithms, and run through some real-world scenarios demonstrating the use of graph algorithms on a sample banking data set.
Webinar with Joe Depeau, Neo4j, April 15, 2020
Powerful Information Discovery with Big Knowledge Graphs –The Offshore Leaks ...Connected Data World
Borislav Popov's slides from his lightning talk at Connected Data London. Borislav - a Director of Business Development at Ontotext presented Ontotext's approach to tackling the Panama Papers leak. Using a technology that is a mix between semantic web and graph databases.
Applications of Semantic Technology in the Real World TodayAmit Sheth
Amit Sheth, "Applications of Semantic Technology in the Real World Today," talk given at Semantic Technology Conference, San Jose, CA, March 2005.
This talk reviews real-world applications mainly deployed in financial services industry developed over Semagix Freedom platform described in http://knoesis.org/library/resource.php?id=810 . Technology is based on this patent: "Semantic web and its applications in browsing, searching, profiling, personalization and advertising", http://knoesis.org/library/resource.php?id=843 .
Amit Sheth founded Taalee in 1999, which merged with Voquette in 2002, and then with Semagix in 2004.
Tutorial@BDA 2017 -- Knowledge Graph Expansion and Enrichment Paris Sud University
Today, we are experiencing an unprecedented production of resources, published as Linked Open Data (LOD, for short). This is leading to the creation of knowledge graphs (KGs) containing billions of RDF (Resource Description Framework) triples, such as DBpedia, YAGO and Wikidata on the academic side, and the Google Knowledge Graph or Microsoft’s Satori graph on the commercial side. These KGs contain millions of entities (such as people, proteins, or books), and millions of facts about them. This knowledge is typically expressed in RDF (Resource Description Framework), i.e., as triples of the form ⟨Macron, presidentOf, France⟩. Some KGs provide an ontology expressed in OWL2 (Web Ontology Language), which describes the vocabulary (the classes and properties) for the RDF facts. However, to exploit and take benefits from the richness of this available data and knowledge, several problems have to be faced, namely, data linking, data fusion and knowledge discovery, when data is of big volume, heterogeneous and evolving. In this tutorial we will first give an overview of exiting data linking and key discovery approaches. Then, we will discuss the problem of identity crisis caused by the misuse of owl:sameAs predicate and give some possible solutions. We will finish by highlighting some current challenges in this research area.
Basic explanation about graph mining for social network analysis (SNA). I tried to describe some metrics and benefit from SNA (focusing on telecommunication field). Basic spark with graphx script to analyse the graph also in the slide
Methods for Intrinsic Evaluation of Links in the Web of DataCristina Sarasua
The current Web of Data contains a large amount of interlinked data. However, there is still a limited understanding about the quality of the links connecting entities of different and distributed data sets. Our goal is to provide a collection of indicators that help assess existing interlinking. In this paper, we present a framework for the intrinsic evaluation of RDF links, based on core principles of Web data integration and foundations of Information Retrieval. We measure the extent to which links facilitate the discovery of an extended description of entities, and the discovery of other entities in other data sets. We also measure the use of different vocabularies. We analysed links extracted from a set of data sets from the Linked Data Crawl 2014 using these measures.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Employees, Business Partners and Bad Guys: What Web Data Reveals About Person...Connotate
This presentation will discuss how to collect Web data with precision, transform it and then apply next-generation text analytics to reveal insights about the past activities of persons of interest and/or predict future outcomes. Featured guest speaker Claire Schmidt will discuss results of a project which proved the potential of using automated Web data collection and advanced analytics to identify potential child victims of exploitation.
Deep Recommender Systems - PAPIs.io LATAM 2018Gabriel Moreira
In this talk, we provide an overview of the state on how Deep Learning techniques have been recently applied to Recommender Systems. Furthermore, I provide an brief view of my ongoing Phd. research on News Recommender Systems with Deep Learning
Christopher Gutteridge's slides form Connected Data London. Christopher, who is an Open Data Architect at the Univeristy of Southhampton presented why and how people should employ an Open Data strategy at their organisation.
Knowledge graphs ilaria maresi the hyve 23apr2020Pistoia Alliance
Data for drug discovery and healthcare is often trapped in silos which hampers effective interpretation and reuse. To remedy this, such data needs to be linked both internally and to external sources to make a FAIR data landscape which can power semantic models and knowledge graphs.
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
Profiling User Interests on the Social Semantic WebFabrizio Orlandi
Fabrizio Orlandi's PhD Viva @Insight NUI Galway (ex-DERI) - 31/03/2014.
Supervisors: Alexandre Passant and John G. Breslin.
Examiners: Fabien Gandon and Stefan Decker
February 18 2015 NISO Virtual Conference
Scientific Data Management: Caring for Your Institution and its Intellectual Wealth
Network Effects: RMap Project
Sheila M. Morrissey, Senior Researcher, ITHAKA
X api chinese cop monthly meeting feb.2016Jessie Chuang
Topics
XAPI Vocabulary spec. From ADL
Linked Data / Semantic web. / Web 3.0
Linked Data in education and content recommender
Semantic search and Google Knowledge Graph
APIs eat software (connect with partners and services)
How should we exploit data and build intelligence layer?
Case Study (Hong Ding Educational Technology)
Monetize your data and add value (intelligence)
The global need to securely derive (instant) insights, have motivated data architectures from distributed storage, to data lakes, data warehouses and lake-houses. In this talk we describe Tag.bio, a next generation data mesh platform that embeds vital elements such as domain centricity/ownership, Data as Products, Self-serve architecture, with a federated computational layer. Tag.bio data products combine data sets, smart APIs, statistical and machine learning algorithms into decentralized data products for users to discover insights using FAIR Principles. Researchers can use its point and click (no-code) system to instantly perform analysis and share versioned, reproducible results. The platform combines a dynamic cohort builder with analysis protocols and applications (low-code) to drive complex analysis workflows. Applications within data products are fully customizable via R and Python plugins (pro-code), and the platform supports notebook based developer environments with individual workspaces.
Join us for a talk/demo session on Tag.bio data mesh platform and learn how major pharma industries and university health systems are using this technology to promote value based healthcare, precision healthcare, find cures for disease, and promote collaboration (without explicitly moving data around). The talk also outlines Tag.bio secure data exchange features for real world evidence datasets, privacy centric data products (confidential computing) as well as integration with cloud services
Bringing Machine Learning and Knowledge Graphs Together
Six Core Aspects of Semantic AI:
- Hybrid Approach
- Data Quality
- Data as a Service
- Structured Data Meets Text
- No Black-box
- Towards Self-optimizing Machines
Presentation on an overview of LinkedIn data driven products and infrastructure given on 26 Oct 2012 in the big-data symposium given in honor of the retirement of my PhD advisor Dr Martin H. Schultz.
This presentation was provided by Tim McGeary of Duke University during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
SmartCities increase citizens’ quality of life and improve the efficiency and quality of the services provided by governing entities and business
“The city must become like the Internet, i.e. enabling creative development and easy deployment of applications which aim to empower the citizen” - THE APPS FOR SMART CITIES MANIFESTO
This view can be achieved by leveraging:
Available infrastructure such as Open Government Data and deployed sensor networks in cities
Citizens’ participation through apps in their smartphones
The IES CITIES project promotes user-centric mobile micro-services that exploit open data and generate user-supplied data
Hypothesis: Users may help on improving, extending and enriching the open data in which micro-services are based
Its platform aims to:
Facilitate the generation of citizen-centric apps that exploit urban data in different domains
Enable user supplied data to complement, enrich and enhance existing datasets about a city
Data Science: Expediting Use of Data by Business Users with Self-service Disc...Denodo
In this presentation, you will learn how Denodo expedites the use of data by business users through its new self-service discovery and search capabilities.
This presentation is part of the Fast Data Strategy Conference, and you can watch the video here goo.gl/uOPpGe.
Maintaining networks and servers availability while reducing downtimes to minimum are fundamental missions for IT managers and administrators. But with the growing complexity of infrastructures, the pressure from business strategists to deliver new services or the heterogeneity of data assets, managing networks is often a challenge.
Graph technologies like Linkurious offer an intuitive approach to model and investigate data by putting the connections between components at the forefront. Modeling the network into a flexible and unified overview is one of the keys to understand your architecture and reduce risks, costs and time spent on maintenance operations.
Cross discipline collaboration benefits from group think, a consolidation of soft system methodology and user focused design that all starts with design thinking that sees clients, designers, developers and information architects working together to address user problems and needs. As with any great adventure, design thinking starts with exploration and discovery.This presentation examines the high level tenants of system thinking, expands the scope of user thinking to include tools and devices that users employ to find out designs and delve into the specifics of design thinking, its methods and outcomes.
Nesta palestra no evento GDG DataFest, apresentei uma introdução prática sobre as principais técnicas de sistemas de recomendação, incluindo arquiteturas recentes baseadas em Deep Learning. Foram apresentados exemplos utilizando Python, TensorFlow e Google ML Engine, e fornecidos datasets para exercitarmos um cenário de recomendação de artigos e notícias.
Semantic Web & Information Brokering: Opportunities, Commercialization and Ch...Amit Sheth
Amit Sheth, "Semantic Web & Info. Brokering Opportunities, Commercialization and Challenges," Keynote talk at the workshop on Semantic Web: Models, Architecture and Management, September 21, 2000, Lisbon, Portugal.
This was the keynote given at probably the first international event with "Semantic Web" in title (and before the well known SciAm article). As in TBL's use of Semantic Web in his 1999 book, (semantic) metadata plays central role. The use of Worldmodel/Ontology is consistent with our use of ontology for (Web) information integration in 1994 CIKM paper. Summary of the talk by event organizers and other details are at: http://knoesis.org/library/resource.php?id=735
Prof. Sheth started a Semantic Web company Taalee, Inc. in 1999 (product was called MediaAnywhere A/V search engine- discussed in this paper in the context of one of its use by a customer Redband Broadcasting). The product included Semantic Web/populated Ontology based semantic (faceted) search, semantic browsing, semantic personalization, semantic targeting (advertisement), etc as is described in U.S. Patent #6311194, 30 Oct. 2001 (filed 2000). MediaAnywhere has about 25 ontologies in News/Business, Sports, Entertainment, etc.
Taalee merged to become Voquette in 2001 (product was called SCORE), Semagix in 2004 (product was called Semagix Freedom), and then Fortent in 2006 (products included Know Your Customers).
Supporting product development while reducing material and prototyping costs or centralizing product records is critical for PLM and PDM managers. However, the growing complexity and volume of cross-business data and processes can turn the management of a product lifecycle into a complex enterprise.
Graph technology like Linkurious offers an intuitive approach to model, search and understand data by putting the connections between components at the forefront. Modeling people, processes, business systems and products components into an interactive and unified network is one of the keys to escape the complexity of product development and find the insights your organization need to gain competitive advantage.
In this presentation, you will learn about:
- Challenges and risks of product development and data management,
- How businesses can use graph technology to model, visualize, optimize and monitor product lifecycles and related elements,
- How to conduct BOM and change management with Linkurious.
Similar to Web Intelligence 2013 - Characterizing concepts of interest leveraging Linked Data and the Social Web (20)
Semantic user profiling and Personalised filtering of the Twitter streamFabrizio Orlandi
Presentation at Kno.e.sis - Feb 2012.
The presentation describe my current PhD research at DERI and the work done in 5 weeks during a collaboration in Kno.e.sis with Pavan Kapanipathi, Prof. Amit Sheth, Prof. T. K. Prasad and the rest of the group.
- video: http://youtu.be/MmF5HxIVUwA
Project Serenity is an innovative initiative aimed at transforming urban environments into sustainable, self-sufficient communities. By integrating green architecture, renewable energy, smart technology, sustainable transportation, and urban farming, Project Serenity seeks to minimize the ecological footprint of cities while enhancing residents' quality of life. Key components include energy-efficient buildings, IoT-enabled resource management, electric and autonomous transportation options, green spaces, and robust waste management systems. Emphasizing community engagement and social equity, Project Serenity aspires to serve as a global model for creating eco-friendly, livable urban spaces that harmonize modern conveniences with environmental stewardship.
This tutorial presentation provides a step-by-step guide on how to use Facebook, the popular social media platform. In simple and easy-to-understand language, this presentation explains how to create a Facebook account, connect with friends and family, post updates, share photos and videos, join groups, and manage privacy settings. Whether you're new to Facebook or just need a refresher, this presentation will help you navigate the features and make the most of your Facebook experience.
Telegram is a messaging platform that ushers in a new era of communication. Available for Android, Windows, Mac, and Linux, Telegram offers simplicity, privacy, synchronization across devices, speed, and powerful features. It allows users to create their own stickers with a user-friendly editor. With robust encryption, Telegram ensures message security and even offers self-destructing messages. The platform is open, with an API and source code accessible to everyone, making it a secure and social environment where groups can accommodate up to 200,000 members. Customize your messenger experience with Telegram's expressive features.
Improving Workplace Safety Performance in Malaysian SMEs: The Role of Safety ...AJHSSR Journal
ABSTRACT: In the Malaysian context, small and medium enterprises (SMEs) experience a significant
burden of workplace accidents. A consensus among scholars attributes a substantial portion of these incidents to
human factors, particularly unsafe behaviors. This study, conducted in Malaysia's northern region, specifically
targeted Safety and Health/Human Resource professionals within the manufacturing sector of SMEs. We
gathered a robust dataset comprising 107 responses through a meticulously designed self-administered
questionnaire. Employing advanced partial least squares-structural equation modeling (PLS-SEM) techniques
with SmartPLS 3.2.9, we rigorously analyzed the data to scrutinize the intricate relationship between safety
behavior and safety performance. The research findings unequivocally underscore the palpable and
consequential impact of safety behavior variables, namely safety compliance and safety participation, on
improving safety performance indicators such as accidents, injuries, and property damages. These results
strongly validate research hypotheses. Consequently, this study highlights the pivotal significance of cultivating
safety behavior among employees, particularly in resource-constrained SME settings, as an essential step toward
enhancing workplace safety performance.
KEYWORDS :Safety compliance, safety participation, safety performance, SME
Surat Digital Marketing School is created to offer a complete course that is specifically designed as per the current industry trends. Years of experience has helped us identify and understand the graduate-employee skills gap in the industry. At our school, we keep up with the pace of the industry and impart a holistic education that encompasses all the latest concepts of the Digital world so that our graduates can effortlessly integrate into the assigned roles.
This is the place where you become a Digital Marketing Expert.
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Using Google Teams (G-Teams) is simple. Start by opening the Google Teams app on your phone or visiting the G-Teams website on your computer. Sign in with your Google account. To join a meeting, click on the link shared by the organizer or enter the meeting code in the "Join a Meeting" section. To start a meeting, click on "New Meeting" and share the link with others. You can use the chat feature to send messages and the video button to turn your camera on or off. G-Teams makes it easy to connect and collaborate with others!
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Web Intelligence 2013 - Characterizing concepts of interest leveraging Linked Data and the Social Web
1. INSIGHT Centre for Data Analytics
www.insight-centre.org
Characterising concepts of interest
leveraging Linked Data
and the Social Web
Fabrizio Orlandi, Pavan Kapanipathi,
Amit Sheth, Alexandre Passant
IEEE/WIC/ACM Web Intelligence
Atlanta, GA, USA
20th November 2013
Copyright 2013 INSIGHT Centre for Data Analytics. All rights reserved.
Semantic Web & Linked Data
Research Programme
2. Scenario:
Personalisation and User Profiling on the Social Web
INSIGHT Centre for Data Analytics
www.insight-centre.org
Semantic Web & Linked Data
Research Programme
http://www.flickr.com/photos/giladlotan/
3. INSIGHT Centre for Data Analytics
www.insight-centre.org
Semantic Web & Linked Data
Research Programme
4. INSIGHT Centre for Data Analytics
www.insight-centre.org
Semantic Web & Linked Data
Research Programme
5. Solution
INSIGHT Centre for Data Analytics
www.insight-centre.org
Interlink social websites
Integration
&
User Modelling
Merge and model user data
Personalise users’ experience
using their profile
User Profile
Recommendations
Adaptive Systems
Search Personalisation
[Orlandi et al., I-Semantics 2012]
Semantic Web & Linked Data
Research Programme
6. Problem
INSIGHT Centre for Data Analytics
www.insight-centre.org
Entity-based user profiles of interests:
Sport
CEV Volleyball Cup
Music
Heavy Metal
Mastodon
Atlanta
…
6
Semantic Web & Linked Data
Research Programme
7. Problem
INSIGHT Centre for Data Analytics
www.insight-centre.org
Entity-based user profiles of interests:
Semantics?
Pragmatics?
Sport
CEV Volleyball Cup
Music
Heavy Metal
Mastodon
Relevance?
Atlanta
…
7
Semantic Web & Linked Data
Research Programme
8. Linking Open Data
INSIGHT Centre for Data Analytics
8
www.insight-centre.org
The Semantics of the Web of Data
LOD Cloud by R. Cyganiak
and A. Jentzsch
Semantic Web & Linked Data
Research Programme
9. Example
INSIGHT Centre for Data Analytics
www.insight-centre.org
“Mastodon is the best heavy metal band from Atlanta…
Can’t wait to see them live again!”
“Trentino vs Lugano about to start - Diatec youngster to
impress again in CEV Champions League #volleyball”
“W3C Invites Implementations of five Candidate
Recommendations for RDF 1.1 #SemanticWeb”
Music
Heavy Metal
Mastodon
• Named entity recognition
and disambiguation
• Frequency + time-decay
weighting scheme
Atlanta
CEV Champions League
Volleyball
Semantic Web
RDF
9
Semantic Web & Linked Data
Research Programme
10. Example
INSIGHT Centre for Data Analytics
www.insight-centre.org
Are all the extracted entities useful for personalisation?
How are concepts/entities being used on the Social Web? (Pragmatics)
Music
Heavy Metal
Mastodon (band)
Atlanta (GA.)
CEV Champions League
Volleyball
Very abstract, very popular
Very popular
Specific and time-dependent on events, etc.
Specific, very popular and time-dependent
Specific and time-dependent on events, etc.
Abstract and popular
Semantic Web
RDF
10
Abstract and not popular
Specific and not popular
Semantic Web & Linked Data
Research Programme
11. The Dimensions of our
Characterisation
INSIGHT Centre for Data Analytics
Specificity
www.insight-centre.org
The level of abstraction that an entity has in a common
conceptual schema shared by humans
Popularity
How popular an entity is on the Social Web
– How frequently is it mentioned/used at that point of time?
Temporal Dynamics
The trend and evolution of the frequency of mentions of an
entity on the Social Web
– i.e. popularity over time
11
Semantic Web & Linked Data
Research Programme
12. Requirements
INSIGHT Centre for Data Analytics
www.insight-centre.org
Our use case: real-time personalisation of Social
Web streams
1.
(quasi-) Real-time computation of the dimensions
2.
Results constantly up to date with the real world
3.
Knowledge base and domain independent approach
12
Semantic Web & Linked Data
Research Programme
13. Popularity
INSIGHT Centre for Data Analytics
www.insight-centre.org
We chose the Twitter Search API
We search for an entity on the Twitter stream in a short recent time
frame.
Run entity disambiguation on the resulting tweets to filter out noisy
tweets.
Count the remaining tweets in a given timeframe.
The Popularity measure is the resulting value in tweets/second.
This is fast, simple, up-to-date, only for short recent timeframe.
e.g. “Music”~ 16.6 tw/s
“Heavy Metal”~ 0.09 tw/s
“Semantic Web”~ 0.0008 tw/s
13
Semantic Web & Linked Data
Research Programme
14. Temporal Dynamics
INSIGHT Centre for Data Analytics
www.insight-centre.org
We use Wikipedia page views
Entities are already mapped to DBpedia
MediaWiki API provides a long history of daily page views of
Wikipedia articles
We use Mean and Standard Deviation for the last 30 days of page
views to identify if the popularity of an entity is:
– Stable/Unstable
– Trendy/Non-Trendy
CEV_Champions_League
Typhoon_Haiyan (2013)
(Diagrams from: stats.grok.se)
Semantic Web & Linked Data
Research Programme
15. Specificity
INSIGHT Centre for Data Analytics
www.insight-centre.org
We use the Linking Open Data (LOD) cloud
Most of the available knowledge bases (e.g. DMOZ, Wordnet,
OpenCyc) are not up-to-date.
Wikipedia would be large, domain-independent, continuously
updated, but:
– entities are not organised hierarchically in a taxonomy
– We cannot use taxonomy-based methods (i.e. super/sub -type rel.)
– PLUS: expensive algorithms would not be good for real-time computation
LOD Links Structure!
15
Semantic Web & Linked Data
Research Programme
16. Graph based measures
INSIGHT Centre for Data Analytics
www.insight-centre.org
SOA graph based method:
indegree and outdegree
(here called Incoming/Outgoing Predicates – IP and OP)
We can use these methods with RDF triples
We introduce “distinct in/out-degree” (IDP and ODP )
s1
p1
p1
s2
p2
p3
m
o1
p4
o2
Values for “m”:
IP (indegree) = 3
OP (outdegree) = 2
IDP (distinct indegree) = 2
ODP (distinct outdegree) = 2
s3
16
Semantic Web & Linked Data
Research Programme
17. Our Specificity Measure
INSIGHT Centre for Data Analytics
www.insight-centre.org
DRR (Distinct Relations Ratio):
Incoming Distinct Predicates (IDP)
DRR =
Outgoing Distinct Predicates (ODP)
Compared with:
IP/OP, IP+OP, IP, IDP
Computed on Sindice SPARQL
endpoint in less than 1sec.
17
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Research Programme
18. Alternative SOA Method
INSIGHT Centre for Data Analytics
www.insight-centre.org
DMOZ (Open Directory Project) taxonomy
18
We use the hierarchical structure of DMOZ as an alternative method to
measure specificity.
We manually map entities to the DMOZ entities and compute the
distance from the root of the DMOZ tree.
Semantic Web & Linked Data
Research Programme
19. Generation of a Gold Standard
INSIGHT Centre for Data Analytics
www.insight-centre.org
Binary classification of entities
5 humans classified 160 entities in:
– Generic (38%)
– Specific (62%)
Substantial agreement (k=0.61)
Ranking of entities
5 humans rated the specificity of 160 entities in:
– 1 to 10 scale (1=very generic, 10=very specific)
Average Rate
7.03
Average Std. Dev.
1.45
AVG Top 30 High Std. Dev.
5.66
AVG Top 30 Low Std. Dev.
7.51
Abstract entities are harder
for humans to rate
19
Semantic Web & Linked Data
Research Programme
20. Evaluation: Classification
INSIGHT Centre for Data Analytics
www.insight-centre.org
We compared the different methods against the gold standard
created manually by the users
Agreement with gold std. in the binary classification task:
DMOZ
IP/OP
IP+OP
IP
random
83.9%
DRR
84.1%
70.0%
70.0%
72.5%
61.9%
The performance of the DRR measure for this classification task
is comparable to a manual classification done using the DMOZ
taxonomy and to human judgement.
20
Semantic Web & Linked Data
Research Programme
21. Evaluation: Ranking
INSIGHT Centre for Data Analytics
www.insight-centre.org
We rank the specificity of 50 randomly chosen entities using:
Gold standard (average of the 5 users’ rates for each entity)
DMOZ levels (integers, 0 to 9)
– We compute “DMOZ-” and “DMOZ+” as the worst and best possible rankings
compared to the gold standard ranking.
DRR, IP/OP, IP+OP, random, values (real numbers)
We compute NDCG (Normalized Discounted Cumulative Gain) at
different ranking positions “p”.
(DCGideal is the ranking of the gold std.)
Semantic Web & Linked Data
Research Programme
22. Evaluation: Ranking
INSIGHT Centre for Data Analytics
www.insight-centre.org
DRR: +5% for NDCG at 10 and 20
Semantic Web & Linked Data
Research Programme
23. Evaluation on User Profiles
INSIGHT Centre for Data Analytics
www.insight-centre.org
We evaluate the impact of the proposed measures on user
profiles of interests, a real use case
Interests extracted from users’ posts on Facebook and Twitter
with NLP tools (as described in our previous work [1])
Frequency-based + time decay weighting strategy
Each user rated his/her Top 30 list of interests generated (total
of 794 user ratings)
23
27 volunteers
Ratings on a “1 to 5” scale according to how relevant/interesting
is each entity of interest to the user (5 is highly relevant)
[1] Orlandi et al., I-Semantics 2012
Semantic Web & Linked Data
Research Programme
24. Evaluation on User Profiles
INSIGHT Centre for Data Analytics
www.insight-centre.org
Average score (1 to 5 scale) is computed according to groups of types of
entities
(+8%)
(17%)
(+12%)
24
Not-popular and generic entities better represent users’ perception of
their interests (but we have only 17% of them)
This behaviour might be different in other applications and use cases!
(e.g. news recommendations, etc.)
Semantic Web & Linked Data
Research Programme
25. Conclusions
INSIGHT Centre for Data Analytics
www.insight-centre.org
Introduced dimensions for characterisation of concepts of interest:
specificity, popularity and temporal dynamics.
Proposed methods for their computation satisfying requirements for
real-time personalisation of Social Web streams:
Introduced a novel measure (DRR) for specificity of concepts based
on the LOD cloud
Evaluated for two different tasks (classification and ranking) against SOA
methods (humans, DMOZ, graph measures)
Evaluated the impact of the measures on user profiles of interests
(27 users and ~800 ratings)
25
Real-time, domain independent, up to date.
Abstract and non-popular interests are preferred by users
Semantic Web & Linked Data
Research Programme
26. Future work
INSIGHT Centre for Data Analytics
www.insight-centre.org
Experiment the measures on user profiles used for different
personalisation tasks.
E.g. a tweets recommender system should give priority to trendy,
popular and specific entities instead.
Improve the simple popularity and trend detection methods.
Improve the DRR measure adding more “semantics”, i.e. considering
the different types of edges.
26
Semantic Web & Linked Data
Research Programme
27. Thanks!
INSIGHT Centre for Data Analytics
www.insight-centre.org
@badmotorf
fabrizio.orlandi@deri.org
@pavankaps
pavan@knoesis.org
@amit_p
amit@knoesis.org
@terraces
alex@seevl.net
Semantic Web & Linked Data
Research Programme