A Semantic Web Primer: The History and Vision of Linked Open Data and the Web 3.0
There is a transformational change coming to the world-wide-web that will fundamentally alter how its vast array of data is structured, and as a result greatly enhance the way humans and machines interact with this indispensable resource. Given the inertia of existing infrastructure, this segue will be evolutionary as opposed to revolutionary, and indeed has been envisioned since the inception of the web. Come join us for a layman's look at the nature of the Web 3.0, its historical underpinnings, and the opportunities it presents.
1. The document describes a study that aimed to develop an open government data (OGD) platform that integrates OGD and social media features to better stimulate value generation from OGD.
2. Researchers designed a prototype platform with features like data processing, feedback/collaboration, data quality ratings, and grouping/interaction capabilities.
3. An evaluation of the prototype found that users appreciated the novel social media-inspired features and found them useful for collaborating around OGD.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
The document discusses the concept of "Broad Data" which refers to the large amount of freely available but widely varied open data on the World Wide Web, including structured and semi-structured data. It provides examples such as the growing linked open data cloud and over 710,000 datasets available from governments around the world. Broad data poses new challenges for data search, modeling, integration and visualization of partially modeled datasets. International open government data search and linking government data to additional contexts are also discussed.
Towards Knowledge Graph based Representation, Augmentation and Exploration of...Sören Auer
This document discusses improving scholarly communication through knowledge graphs. It describes some current issues with scholarly communication like lack of structure, integration, and machine-readability. Knowledge graphs are proposed as a solution to represent scholarly concepts, publications, and data in a structured and linked manner. This would help address issues like reproducibility, duplication, and enable new ways of exploring and querying scholarly knowledge. The document outlines a ScienceGRAPH approach using cognitive knowledge graphs to represent scholarly knowledge at different levels of granularity and allow for intuitive exploration and question answering over semantic representations.
Semantics for Big Data Integration and AnalysisCraig Knoblock
Much of the focus on big data has been on the problem of processing very large sources. There is an equally hard problem of how to normalize, integrate, and transform the data from many sources into the format required to run large-scale anal- ysis and visualization tools. We have previously developed an approach to semi-automatically mapping diverse sources into a shared domain ontology so that they can be quickly com- bined. In this paper we describe our approach to building and executing integration and restructuring plans to support analysis and visualization tools on very large and diverse datasets.
How Semantics Solves Big Data ChallengesDATAVERSITY
Today, organizations want both IT simplicity and innovation, but reliance on traditional databases only leads to more complexity, longer development cycles, and more silos. In fact, organizations report that the #1 impediment to big data success is having too many silos. In this webinar, we will discuss how a new database technology, semantics, solves this problem by providing a new approach to modeling data that focuses on relationships and context, making it easier for data to be understood, searched, and shared. With semantics, world-leading organizations are integrating disparate data faster and easier and building smarter applications with richer analytic capabilities—benefits that we look forward to diving into during the webinar.
Notes for talk on 12th June 2013 to Open Innovation meeting, GlasgowPeterWinstanley1
The document discusses open innovation and TRIZ methodology for inventive problem solving. It notes that TRIZ asks designers to define the ideal solution without negative aspects, and looks for solutions across domains by removing domain-specific language. The document then discusses semantic interoperability of data through shared data models and vocabularies, and provides examples of existing linked open data sources and vocabularies that could be reused.
1. The document describes a study that aimed to develop an open government data (OGD) platform that integrates OGD and social media features to better stimulate value generation from OGD.
2. Researchers designed a prototype platform with features like data processing, feedback/collaboration, data quality ratings, and grouping/interaction capabilities.
3. An evaluation of the prototype found that users appreciated the novel social media-inspired features and found them useful for collaborating around OGD.
Big Data Mining - Classification, Techniques and IssuesKaran Deep Singh
The document discusses big data mining and provides an overview of related concepts and techniques. It describes how big data is characterized by large volume, variety, and velocity of data that is difficult to manage with traditional methods. Common techniques for big data mining discussed include NoSQL databases, MapReduce, and Hadoop. Some challenges of big data mining are also mentioned, such as dealing with high volumes of unstructured data and limitations of traditional databases in handling diverse and continuously growing data sources.
The document discusses the concept of "Broad Data" which refers to the large amount of freely available but widely varied open data on the World Wide Web, including structured and semi-structured data. It provides examples such as the growing linked open data cloud and over 710,000 datasets available from governments around the world. Broad data poses new challenges for data search, modeling, integration and visualization of partially modeled datasets. International open government data search and linking government data to additional contexts are also discussed.
Towards Knowledge Graph based Representation, Augmentation and Exploration of...Sören Auer
This document discusses improving scholarly communication through knowledge graphs. It describes some current issues with scholarly communication like lack of structure, integration, and machine-readability. Knowledge graphs are proposed as a solution to represent scholarly concepts, publications, and data in a structured and linked manner. This would help address issues like reproducibility, duplication, and enable new ways of exploring and querying scholarly knowledge. The document outlines a ScienceGRAPH approach using cognitive knowledge graphs to represent scholarly knowledge at different levels of granularity and allow for intuitive exploration and question answering over semantic representations.
Semantics for Big Data Integration and AnalysisCraig Knoblock
Much of the focus on big data has been on the problem of processing very large sources. There is an equally hard problem of how to normalize, integrate, and transform the data from many sources into the format required to run large-scale anal- ysis and visualization tools. We have previously developed an approach to semi-automatically mapping diverse sources into a shared domain ontology so that they can be quickly com- bined. In this paper we describe our approach to building and executing integration and restructuring plans to support analysis and visualization tools on very large and diverse datasets.
How Semantics Solves Big Data ChallengesDATAVERSITY
Today, organizations want both IT simplicity and innovation, but reliance on traditional databases only leads to more complexity, longer development cycles, and more silos. In fact, organizations report that the #1 impediment to big data success is having too many silos. In this webinar, we will discuss how a new database technology, semantics, solves this problem by providing a new approach to modeling data that focuses on relationships and context, making it easier for data to be understood, searched, and shared. With semantics, world-leading organizations are integrating disparate data faster and easier and building smarter applications with richer analytic capabilities—benefits that we look forward to diving into during the webinar.
Notes for talk on 12th June 2013 to Open Innovation meeting, GlasgowPeterWinstanley1
The document discusses open innovation and TRIZ methodology for inventive problem solving. It notes that TRIZ asks designers to define the ideal solution without negative aspects, and looks for solutions across domains by removing domain-specific language. The document then discusses semantic interoperability of data through shared data models and vocabularies, and provides examples of existing linked open data sources and vocabularies that could be reused.
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).
Open Linked Data as Part of a Government Enterprise ArchitectureJohann Höchtl
This document discusses open linked data and its role in public administration information management. It presents open linked data as a key element of transparency, participation, and collaboration in government. It outlines the benefits of open data for citizens, such as increased self-determination and better public services. However, it also notes potential drawbacks, such as loss of control and power for administrations. The document proposes an open government data architecture model based on a five-level saturation model, with data encoded in RDF and assigned persistent URIs to ensure reliability. Overall, the document argues that treating information outflow as a core part of information management can strengthen trust in government.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
Knowledge Graphs - The Power of Graph-Based SearchNeo4j
1) Knowledge graphs are graphs that are enriched with data over time, resulting in graphs that capture more detail and context about real world entities and their relationships. This allows the information in the graph to be meaningfully searched.
2) In Neo4j, knowledge graphs are built by connecting diverse data across an enterprise using nodes, relationships, and properties. Tools like natural language processing and graph algorithms further enrich the data.
3) Cypher is Neo4j's graph query language that allows users to search for graph patterns and return relevant data and paths. This reveals why certain information was returned based on the context and structure of the knowledge graph.
Extending Memory on the Web via Human-Centric Knowledge Exchange Network. Presented at W3C Workshop on Social Standards: The Future of Business, 7-8 August 2013, San Francisco, USA
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
Describing Scholarly Contributions semantically with the Open Research Knowle...Sören Auer
1) Prof. Dr. Sören Auer discusses challenges with current scholarly communication and proposes using knowledge graphs and the Open Research Knowledge Graph to better represent research contributions.
2) The presentation outlines how research contributions could be semantically captured and organized in the knowledge graph, including publications, data, and other artifacts.
3) Features like intuitive exploration, question answering, and automatic generation of comparisons are demonstrated as possible applications of the semantic representations in the knowledge graph.
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...Cloudera, Inc.
Federal, State and Local governments and the development community surrounding them are busy creating solutions leveraging the Apache Foundation Hadoop capabilities. This session will highlight the top five solutions selected by an all star panel of judges. Who will take home the coveted Hadoop Award for Government Excellence (Haggie) Nominations for Haggies are being accepted now at http://CTOlabs.com
This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Research in Intelligent Systems and Data Science at the Knowledge Media Insti...Enrico Motta
The document discusses research directions in intelligent systems and data science. It describes work on making sense of scholarly data through techniques like data mining, semantic technologies, and machine learning. It also discusses mapping and classifying computer science research areas using an automatically generated ontology with over 14,000 topics. Other topics discussed include predicting emerging research areas, applications in smart cities like the MK:Smart project, and potential roles for robots in smart cities like an autonomous health and safety inspector.
Towards an Open Research Knowledge GraphSören Auer
The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.
This document summarizes a presentation on linked open government data. It discusses how government data is being opened through initiatives like Data.gov and how linked data approaches can help address challenges in making open government data more interoperable, scalable, and able to maintain provenance. Key points discussed include the growth of open government data, challenges in working with raw open data, benefits of converting data to linked open formats, and open questions around improving interoperability, addressing scalability issues, and maintaining provenance as open government data continues to expand.
Slides of my talk at OSLCfest in Stockholm Nov 6, 2019
Video recording of the talk is available here:
https://www.facebook.com/oslcfest/videos/2261640397437958/
How to build and run a big data platform in the 21st centuryAli Dasdan
The document provides an overview of big data platform architectures that have been built by various companies and organizations. It discusses self-built platforms from companies like Airbnb, Netflix, Facebook, Slack, and Uber. It also covers cloud-built platforms on IBM Cloud, Microsoft Azure, Google Cloud, and Amazon AWS. Consulting-built platforms from Cloudera and ThoughtWorks are presented. Finally, it introduces the NIST Big Data Reference Architecture as a standard reference model and discusses generic batch vs streaming architectures like Lambda and Kappa.
DCMI Keynote: Bridging the Semantic Gaps and InteroperabilityMike Bergman
M. Bergman's presentation, 'Bridging the Gaps: Adaptive Approaches to Data Interoperabiity,' was a keynote at the DCMI's DC 2010 International Conference in Pittsburgh, PA, on October 22, 2010.
In the presentation, Bergman points to the Dublin Core Metadata Initiative as a unique and key player in plugging the semantics "gap" within the semantic Web. Some specific activities and roles are suggested.
Linked Data at the OU - the story so farEnrico Daga
The document discusses the Open University's use of linked open data and their data.open.ac.uk platform. It provides an overview of linked data principles and the data.open.ac.uk platform. Key services of the Open University rely on data.open.ac.uk to support users in various ways such as the student help center and OpenLearn platform. While linked data is useful for centralized data publishing, it does not replace traditional data management and requires developers to integrate it with existing workflows.
Tuning Personalized PageRank for Semantics-aware Recommendations based on Lin...Cataldo Musto
This document summarizes a research paper presented at the ESWC 2017 conference on using linked open data to improve graph-based recommender systems. The researchers created a tripartite graph representation that encodes user preferences and item descriptive features from DBpedia. They investigated how the DBpedia features impact the representation quality and whether all features are equally important. They aim to automatically select the most promising features.
Mars Generation One: Argubot Academy is an educational iPad game that teaches middle school students argumentation and reasoning skills. In the game, players take on the role of a student at Argubot Academy on Mars, where they must build arguments to make decisions about governing the city and settle disputes through robot battles. The game was developed in collaboration with NASA and the National Writing Project to align with Common Core standards and teach students how to construct logical arguments by matching evidence to claims.
This document provides an overview of remix practices and suggestions for remix to research projects. It includes examples of contemporary remixes like mashups and sampling. Remixing is presented as a lens for research where students can manipulate cultural works into new blends. The document then provides ideas for digital documentaries and persuasive videos students could create. It also includes details and instructions for a "13 Colonies" activity where students make promotional videos for their assigned colony. The rest of the document offers technical support and guidelines for students regarding fair use, video editing, and brainstorming ideas for history-based movie trailers using primary sources.
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).
Open Linked Data as Part of a Government Enterprise ArchitectureJohann Höchtl
This document discusses open linked data and its role in public administration information management. It presents open linked data as a key element of transparency, participation, and collaboration in government. It outlines the benefits of open data for citizens, such as increased self-determination and better public services. However, it also notes potential drawbacks, such as loss of control and power for administrations. The document proposes an open government data architecture model based on a five-level saturation model, with data encoded in RDF and assigned persistent URIs to ensure reliability. Overall, the document argues that treating information outflow as a core part of information management can strengthen trust in government.
This document discusses data mining techniques for big data. It defines big data as large, complex collections of data from various sources that contain both structured and unstructured data. Big data is growing rapidly due to data from sources like social media, sensors, and digital content. Data mining can extract useful insights from big data by discovering patterns and relationships. The document outlines common data mining techniques like classification, prediction, clustering and association rule mining that can be applied to big data. It also discusses challenges of big data like its huge volume, variety of data types, and rapid growth that require new data management approaches.
Knowledge Graphs - The Power of Graph-Based SearchNeo4j
1) Knowledge graphs are graphs that are enriched with data over time, resulting in graphs that capture more detail and context about real world entities and their relationships. This allows the information in the graph to be meaningfully searched.
2) In Neo4j, knowledge graphs are built by connecting diverse data across an enterprise using nodes, relationships, and properties. Tools like natural language processing and graph algorithms further enrich the data.
3) Cypher is Neo4j's graph query language that allows users to search for graph patterns and return relevant data and paths. This reveals why certain information was returned based on the context and structure of the knowledge graph.
Extending Memory on the Web via Human-Centric Knowledge Exchange Network. Presented at W3C Workshop on Social Standards: The Future of Business, 7-8 August 2013, San Francisco, USA
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
Describing Scholarly Contributions semantically with the Open Research Knowle...Sören Auer
1) Prof. Dr. Sören Auer discusses challenges with current scholarly communication and proposes using knowledge graphs and the Open Research Knowledge Graph to better represent research contributions.
2) The presentation outlines how research contributions could be semantically captured and organized in the knowledge graph, including publications, data, and other artifacts.
3) Features like intuitive exploration, question answering, and automatic generation of comparisons are demonstrated as possible applications of the semantic representations in the knowledge graph.
Hadoop World 2011: The Hadoop Award for Government Excellence - Bob Gourley -...Cloudera, Inc.
Federal, State and Local governments and the development community surrounding them are busy creating solutions leveraging the Apache Foundation Hadoop capabilities. This session will highlight the top five solutions selected by an all star panel of judges. Who will take home the coveted Hadoop Award for Government Excellence (Haggie) Nominations for Haggies are being accepted now at http://CTOlabs.com
This document discusses building knowledge graphs using DIG (Distributed Information Graphs) to integrate heterogeneous data sources. It describes the steps involved, including data acquisition, feature extraction, mapping to an ontology, entity resolution, graph construction, and deployment. As a use case, DIG has been used to build a knowledge graph from over 100 million web pages related to human trafficking to help law enforcement identify victims and prosecute traffickers.
3 pillars of big data : structured data, semi structured data and unstructure...PROWEBSCRAPER
There are 3 pillars of Big Data
1.Structured data
2.Unstructured data
3.Semi structured data
Businesses worldwide construct their empire on these three pillars and capitalize on their limitless potential.
Research in Intelligent Systems and Data Science at the Knowledge Media Insti...Enrico Motta
The document discusses research directions in intelligent systems and data science. It describes work on making sense of scholarly data through techniques like data mining, semantic technologies, and machine learning. It also discusses mapping and classifying computer science research areas using an automatically generated ontology with over 14,000 topics. Other topics discussed include predicting emerging research areas, applications in smart cities like the MK:Smart project, and potential roles for robots in smart cities like an autonomous health and safety inspector.
Towards an Open Research Knowledge GraphSören Auer
The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Now it is possible to rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the creation and evolution of information models for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This has the potential to revolutionize scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. Also research results become directly comparable and easier to reuse.
This document summarizes a presentation on linked open government data. It discusses how government data is being opened through initiatives like Data.gov and how linked data approaches can help address challenges in making open government data more interoperable, scalable, and able to maintain provenance. Key points discussed include the growth of open government data, challenges in working with raw open data, benefits of converting data to linked open formats, and open questions around improving interoperability, addressing scalability issues, and maintaining provenance as open government data continues to expand.
Slides of my talk at OSLCfest in Stockholm Nov 6, 2019
Video recording of the talk is available here:
https://www.facebook.com/oslcfest/videos/2261640397437958/
How to build and run a big data platform in the 21st centuryAli Dasdan
The document provides an overview of big data platform architectures that have been built by various companies and organizations. It discusses self-built platforms from companies like Airbnb, Netflix, Facebook, Slack, and Uber. It also covers cloud-built platforms on IBM Cloud, Microsoft Azure, Google Cloud, and Amazon AWS. Consulting-built platforms from Cloudera and ThoughtWorks are presented. Finally, it introduces the NIST Big Data Reference Architecture as a standard reference model and discusses generic batch vs streaming architectures like Lambda and Kappa.
DCMI Keynote: Bridging the Semantic Gaps and InteroperabilityMike Bergman
M. Bergman's presentation, 'Bridging the Gaps: Adaptive Approaches to Data Interoperabiity,' was a keynote at the DCMI's DC 2010 International Conference in Pittsburgh, PA, on October 22, 2010.
In the presentation, Bergman points to the Dublin Core Metadata Initiative as a unique and key player in plugging the semantics "gap" within the semantic Web. Some specific activities and roles are suggested.
Linked Data at the OU - the story so farEnrico Daga
The document discusses the Open University's use of linked open data and their data.open.ac.uk platform. It provides an overview of linked data principles and the data.open.ac.uk platform. Key services of the Open University rely on data.open.ac.uk to support users in various ways such as the student help center and OpenLearn platform. While linked data is useful for centralized data publishing, it does not replace traditional data management and requires developers to integrate it with existing workflows.
Tuning Personalized PageRank for Semantics-aware Recommendations based on Lin...Cataldo Musto
This document summarizes a research paper presented at the ESWC 2017 conference on using linked open data to improve graph-based recommender systems. The researchers created a tripartite graph representation that encodes user preferences and item descriptive features from DBpedia. They investigated how the DBpedia features impact the representation quality and whether all features are equally important. They aim to automatically select the most promising features.
Mars Generation One: Argubot Academy is an educational iPad game that teaches middle school students argumentation and reasoning skills. In the game, players take on the role of a student at Argubot Academy on Mars, where they must build arguments to make decisions about governing the city and settle disputes through robot battles. The game was developed in collaboration with NASA and the National Writing Project to align with Common Core standards and teach students how to construct logical arguments by matching evidence to claims.
This document provides an overview of remix practices and suggestions for remix to research projects. It includes examples of contemporary remixes like mashups and sampling. Remixing is presented as a lens for research where students can manipulate cultural works into new blends. The document then provides ideas for digital documentaries and persuasive videos students could create. It also includes details and instructions for a "13 Colonies" activity where students make promotional videos for their assigned colony. The rest of the document offers technical support and guidelines for students regarding fair use, video editing, and brainstorming ideas for history-based movie trailers using primary sources.
The document lists various events for an organization during the fall 2008 semester, including tabling, scavenger hunts, Ultimate Frisbee, barbecues, a farmers market, corporate presentations, mixers, guest speakers, a sustainability conference, fundraising for veterans, care package filling, social nights like bowling, a turkey bowl tournament, a brewery tour, volleyball, and end of semester awards.
An impact of radio frequency identityfication on supply.pptfodyy
RFID technology was developed in the 1940s and first used commercially in the 1980s for tracking applications. It uses radio signals to automatically identify objects equipped with RFID tags. RFID is now widely used in supply chain management to automate identification processes along the supply chain. This allows for more checkpoints to be established at lower cost. RFID provides benefits like improved efficiency, accuracy, and reduced manual data entry for supply chain operations. It also helps address issues like theft detection and decreases the "bullwhip effect" of demand variability propagating upstream.
An overview of the available digital eyewear, including Google Glass, Vuzix M100, Epson Moverio BT-200, and Optinvent ORA-1, and the SDK's and development environments available for each.
A tutorial on how to develop using the latest mobile AR SDK's. This is an updated version of the talk from Augmented World Expo 2013, that I gave at the Augmented World Expo New York in March, 2014.
Patrick O'Shaughnessey, Founder of Patched Reality Inc, gives an overview of AR SDK tutorial options, and tutorials using 4 of the most widely used SDK's in Unity 3D.
The current status of Linked Open Data (LOD) shows evidence of many datasets available on the Web in RDF. In the meantime, there are still many challenges to overcome by organizations in their journey of publishing five stars datasets on the Web. Those challenges are not only technical, but are also organizational. At this moment where connectionist AI is gaining a wave of popularity with many applications, LOD needs to go beyond the guarantee of FAIR principles. One direction is to build a sustainable LOD ecosystem with FAIR-S principles. In parallel, LOD should serve as a catalyzer for solving societal issues (LOD for Social Good) and personal empowerment through data (Social Linked Data).
Ever wonder how these concepts contrast with and yet complement each other in a next-generation system?
Enterprise semantics
Knowledge graphs
Model-driven development
Digital twins
Self-Sovereign Identity
Own your own data
Data deduplication
Autonomous agents
Large language systems
Data-Centric Architecture combines the major technologies behind each of these concepts. In fact, it’s essential to the real-world implementation of general AI, enabling the context that’s behind contextual computing, DARPA’s Third Phase of AI. To be able to deliver, DCA needs to simplify and scale data ecosystems using these pieces of the data ecosystem puzzle.
This talk will provide an overview of how these pieces of the data-centric puzzle are fitting together. It’s a best practice to see these pieces can fit together side-by-size in an enterprise context and envision next-gen systems from the viewpoint of some of the most demanding enterprise use cases.
It’s also best practice to study how one industry vertical is moving ahead and contrast that progress with your own industry. Remember, as the data-centric ecosystem emerges and the benefits of true digitization start to pay off, many more techniques can be borrowed from other verticals and used in your own vertical. This talk will summarize several powerful recent case studies and highlight the key takeaways.
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.
The document discusses the emergence of the semantic web, which aims to make data on the web more interconnected and machine-readable. It describes Tim Berners-Lee's vision of a "Giant Global Graph" that connects all web documents based on what they are about rather than just linking documents. This would allow user data and profiles to be seamlessly shared across different sites without having to re-enter the same information. The semantic web uses standards like RDF, RDFS and OWL to represent relationships between data in a graph structure and enable automated reasoning. Several companies are working to build applications that take advantage of this interconnected semantic data.
Understand the Idea of Big Data and in Present ScenarioAI Publications
Big data analytics and deep learning are two of data science's most promising areas of convergence. The importance of Big Data has grown recently as several organizations, both public and commercial, have been amassing large amounts of region-specific data that may provide useful information on topics like as national information, advanced security, blackmail area, development, and prosperity informatics. For Big Data Analytics, where data is often unstructured and unlabeled, Deep Learning's ability to analyze and learn from large amounts of data on its own is a crucial feature. In this review, we look at how Deep Learning can be used to solve some of the most pressing problems in Big Data Analytics, including model isolation from large data sets, semantic querying, data marking, smart data recovery, and the automation of discriminative tasks.
The web of data: how are we doing so farElena Simperl
The document summarizes the current state of open data and the web of data. It discusses how data is being shared online through datasets, digital traces, and algorithms. While there is a lot of annotated data available, especially about locations and businesses, uptake of linked data and vocabulary reuse is still low. The document also reviews guidelines for improving data organization, discoverability, documentation, and engagement. Finally, it discusses ongoing research on data search behavior, sensemaking practices, and the potential for generative AI to help with data understanding and reuse.
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
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BIAM 410 Final Paper - Beyond the Buzzwords: Big Data, Machine Learning, What...Thomas Rones
This document discusses big data, machine learning, and NoSQL databases. It defines big data as referring to large or complex datasets that require techniques like NoSQL, MapReduce, and machine learning for analysis. Machine learning is made possible by large amounts of publicly available unstructured data and advances in computing. NoSQL databases are used to store big data because they allow for more flexibility than structured SQL databases for applications that need to scale.
Linked Open Data and data-driven journalismPia Jøsendal
A keynote held at the Media 3.0 seminar in Bergen. It is an introductionary presentation of simple key elements of linked open data. It adresses media and journalists, what data driven journalism can look like and why they should care about what linked open data can offer.
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The document summarizes the topics discussed at an XAPI Chinese CoP meeting in February 2016. It covered the XAPI vocabulary specification, linked data/semantic web, linked data in education and content recommendation, semantic search and Google Knowledge Graph, monetizing data and adding intelligence. It also included a case study on Hong Ding Educational Technology using XAPI data and partnerships to provide differentiated learning paths. The document emphasized collaborating on standards for competency, user data, content metadata and xAPI statements to enable partnerships and monetizing data while ensuring security, regulation and collective decision making.
This document summarizes key points from a talk by Farida Vis on discussing and understanding Big Data. It outlines academic and industry definitions of Big Data, focusing on issues of objectivity, bias, and how data can only answer questions it was designed for. It also discusses how algorithms and data collection are not fully visible or understood, and proposes drawing on ideas from particle physics to make inferences about these "dark matters" and uncover their effects on visible data.
Chemical Semantics at Sopron CC Conference sopekmir
The document discusses applying Semantic Web technologies to computational chemistry data. It introduces the Chemical Semantics Portal, a testbed for exploring how to publish and retrieve computational chemistry data using Semantic Web standards. The rest of the document covers basics of the Semantic Web, challenges with current web technologies, core Semantic Web technologies like RDF and OWL, and examples of representing chemistry data and concepts as ontologies.
The document discusses applying Semantic Web technologies to computational chemistry data. It introduces the Chemical Semantics Portal, a testbed for exploring how to publish and retrieve computational chemistry data using the Semantic Web. The goal is to create a linked data network for computational chemistry results that allows for more effective discovery and analysis compared to existing isolated data silos. The document provides an overview of the Semantic Web and linked data principles and how they can help address challenges with today's large but fragmented web of scientific data.
Talk given by prof. Amit Sheth at the ICMSE-MGI Digital Data Workshop held at Kno.e.sis Center from November 13-14 2013.
workshop page: http://wiki.knoesis.org/index.php/ICMSE-MGI_Digital_Data_Workshop
This talk was given at SEMANTiCS 2014 in Leipzig. It gives an overview how to develop an enterprise linked data strategy around controlled vocabularies based on SKOS. It discusses how knowledge graphs based on SKOS can extended step by step due to the needs of the organization.
for getting the library resources fro the libraries entire world, the important tool is Library catalogues. every can browse all most all the world literature through WorldCat fro the INTERNET.
Knowledge Graphs and their central role in big data processing: Past, Present...Amit Sheth
Keynote at CODS-COMAD 2020, Hyderabad, India, 06 Jan 2020: https://cods-comad.in/keynotes.html
Abstract : Early use of knowledge graphs, before the start of this century, related to building a knowledge graph manually or semi-automatically and applying them for semantic applications, such as search, browsing, personalization, and advertisement. Taalee/Semagix Semantic Search in 2000 had a KG that covered many domains and supported search with an equivalent of today’s infobox. Along with the growth of big data, machine learning became the preferred technique for searching, analyzing and deriving insights from such data. We observed the complementary nature of bottom-up (machine learning-driven) and top-down (semantic, knowledge graph and planning based) techniques. Recently we have seen growing efforts involving the shallow use of a knowledge graph to improve the semantic and conceptual processing of data. The future promises deeper and congruent incorporation or integration of the knowledge graphs in the learning techniques (which we call knowledge-infused learning), where knowledge graphs combining statistical AI (bottom-up) and symbolic AI learning techniques (top-down) play a critical role in hybrid and integrated intelligent systems. Throughout this talk, we will provide real-world examples, products, and applications where the knowledge graph played a pivotal role.
Databases have been used for over 40 years to organize information in a variety of contexts like inventory, class schedules, and personal records. Relational databases remain popular today despite attempts to replace them with object-oriented databases. Cloud computing and big data have further transformed databases by allowing extremely large datasets to be analyzed for trends and patterns. Modern databases can provide targeted recommendations and offers by analyzing individual user information and behaviors.
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“An Outlook of the Ongoing and Future Relationship between Blockchain Technologies and Process-aware Information Systems.” Invited talk at the joint workshop on Blockchain for Information Systems (BC4IS) and Blockchain for Trusted Data Sharing (B4TDS), co-located with with the 36th International Conference on Advanced Information Systems Engineering (CAiSE), 3 June 2024, Limassol, Cyprus.
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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
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2. History & Perspective
“We might hope to see the
finances of the Union as clear
and intelligible as a
merchant's books, so that
every member of Congress
and every man of any mind
in the Union should be able
to comprehend them, to
investigate abuses, and
consequently to control
them.”
Thomas Jefferson, 1802
3. History & Perspective
Web 1.0
Read Only
Visual
Human Presentation
Web 2.0
Interactive
Social
Applications
Web 3.0
“Web of Data”
4. Graph Theory
Problem: Traverse each bridge once and only once in touring the city
Solution: Zero or two nodes of odd degree
Konigsburg, Prussia, 1735
5. Web 3.0 – The GGG
RelFinder SpaceTimeFacebook Graph Search Diseasome
Linked Life Data iDrove.itEdamam
TBL 2009
7. The Resource Description Framework
(RDF)
http://www.example.org/~joe/contact.rdf#joesmith
http://www.example.org/~joe/ “Object” resource (joe's home page)
“Subject” resource (joesmith in RDF)
Can be a literal...
http://xmlns.com/foaf/0.1/homepage “Predicate” resource (FOAF relationship)
URL URN
URI
8. Story in RDBMS
ID Name Type Legs Fur
001 Harrison Human 2 No
002 Layla Dog 4 Yes
003 MyBall Ball N/A N/A
Concept: Harrison has a dog friend named Layla whose favorite activity is chasing a ball
ObjectTable
ID1 ID2
001 002
002 001
FriendshipTable
Type1 Type2
Human Mammal
Dog Mammal
Ball Toy
ID1 ID2
002 003
SubclassTable
FavActivityTable
10. Story in OODBMS
Concept: Harrison has a dog friend named Layla whose favorite activity is chasing a ball
DogDog
HumanHuman
BallBall
ToyToy
001001 002002
HarrisonHarrison LaylaLayla22 44 TrueTrueFalseFalse
003003
MammalMammal
MyBallMyBall
foaf:friend
isA isA
ofType ofType
hasName
numLegs
hasFur hasName
numLegs
hasFur
hasName
ofType
isA
chases
Class
(Concept)
Sub-Class
Object
(Individual)
Attribute
(Property)
11. Atomic (axiomatic) Concepts
ID Name Type Legs Fur
001 Harrison Human 2 No
002 Layla Dog 4 Yes
003 MyBall Ball N/A N/A
ID1 ID2
002 003
Concept (dog) Instance (Layla)
Favorite Activity
Name
RDBMS
OODBMS
14. Quantitative
B-Tree Index: M x O(log2
N)
Graph: 1 x O(log2
N) – plus pointer traversalRef: Michel Domenjoud, blog.octo.com
15. Qualitative
Horizontal Scale (Complexity)
VerticalScale(Size)
Oliver, Andrew C. "Which Freaking Database Should I Use?" InfoWorld. InfoWorld, 02 Aug. 2012. Web. 07 Oct. 2013.
Gonzalez, Rob. "Two Kinds of Big Data." Semanticweb.com. Semanticweb.com, 06 Sept. 2011. Web. 07 Oct. 2013.
GGG
16. The “Ontology” (Vocabulary)
TaxonomyTaxonomy: Organizational hierarchy based upon similarities in properties
OntologyOntology: Set of representational primitives (classes, attributes & relationships)
with which to model a domain of knowledge or discourse*
HumanHuman
MammalMammal
Novel Stringtitle
Human Authorprofession
Novel Authorcreator NameName
NovelNovel
DocDoc
AuthorAuthor NameName
Human Stringname
Key pointKey point: The triple-store's construction enables the extraction of
meaning through inference (description logic), ie, “Smart Data”
*Encyclopedia of Database Systems, L. Liu et al, 2009
17. Summary
Aspect Web 2.0 Web 3.0
Unification Join Built-in
Retrieval Keyword indexing Pointer traversal
Query Keyword matching Inference
Citizens Documents Data
Polysemy & Synonymy Yes No
Construct Table Triple (how)
Descriptor URL - where URI = URL + URN (what)
Data Aggregated Connected
Flexibility Low High
Dynamic
Relationships
Meaning
24. Testimonials
This inference capability makes
both the journalist tagging and the
triple store powered SPARQL
queries simpler and indeed quicker
than a traditional SQL approach.
Dynamic aggregations based on
inferred statements increase the
quality and breadth of content
across the site. The RDF triple
approach also facilitates agile
modeling, whereas traditional
relational schema modeling is less
flexible and also increases query
complexity.
-BBC
Because there haven't been graph models in the
market-leading relational databases, the graph
approach has been mostly overlooked by
corporate users. But it shouldn't be ignored
anymore, he added: Graphs provide "a good
representation of the data structure with which
you're dealing."
-Robin Bloor, the Bloor Group, July 2013
We believe that Graph Search is part of a
trend that is much bigger than Facebook,
and more widespread than search.
Facebook is tapping into a fundamentally
new way to exploit the information that
exists in all the world’s databases. In this
post, we will look at the Facebook
announcement from a different angle, that
of connected data: a growing trend that is
on the verge of changing how companies
large and small understand their data.
-Andreas Kollegger, Jan 2013
The Web as we know it today will not be
the Web as we know it tomorrow. The
Web of today is oriented towards the
universal accessibility of files (e.g. web
pages, images). The Web of today can
be thought of as a large-scale,
distributed file system. The Web of
tomorrow will encode any datum (e.g.
strings, integers, dates). The Web of
tomorrow can be thought of as a
large-scale, distributed database.
-Marko Rodriguez, Los Alamos, 2009
The construction and structure of graphs or networks
is the key to understanding the complex world
around us. -Albert Laszlo, “Linked: The New Science
of Networks”
Today, most data is being managed
in traditional relational databases.
These databases have trouble scaling
to accommodate the increase in data
volume, variety, and complexity. It is
also difficult and expensive for these
relational databases to provide
answers to clinical questions in a
timely manner. These relational
databases are not a viable solution
for solving this new category of data
challenges. KnowMED web site.
While traditional, relational data warehousing and
federation approaches can scale well and are
effective for many core data storage and access
requirements, such approaches often fail when
facing the dynamic changes and the inherent
complexity of data integration requirements for
Healthcare / Life Sciences (HCLS) research.
Semantic integration methods assure coherence,
harmonize synonyms and different terminologies,
and provide an extensible, flexible data integration
platform and interactive knowledge base for
relevant network analysis.
-IO Informatics
25. Drivers for a Paradigm Shift
80% of data being generated is unstructured. *
*Dennis McCatherty, Baseline magazine
**CFO.com, http://www3.cfo.com/article/2012/2/analytics_big-data-business-intelligence
● Hobbled analytics
● Poor collaboration
● Opacity
● TCO
● Performance
11:1**
Silo
IoT
$$$$
28. Community & Regulatory
●
DTC – Data Transparency Coalition (Data Transparency 2013, Sep 11-12)
●
UDI – Unique Device Identification (FDA)
●
ACA – Affordable Care Act (administrative simplification clause)
●
FITA – Foundation for Information Technology Accessibility
●
LEI – Legal Entity Identifier (mandate)
●
FIBO – Financial Industry Business Ontology (Dodd-Frank)
●
ACT – Advisory Committee on Transparency (Sunlight Foundation)
●
Schema.org
●
DATA – Digital Accountability and Transparency Act
●
CMS – Centers for Medicaid and Medicare Services
●
W3C eGovernment
●
Executive Order
Specifically, this Memorandum requires agencies to collect or create information in a way that supports downstream
information processing and dissemination activities. This includes using machine readable and open formats, data standards,
and common core and extensible metadata for all new information creation and collection efforts. OMB Memorandum,
M-13-13, May 9, 2013
31. Unintended consequences (of the good
kind)
●
Other domains and
business use cases
–
Supply chain
–
Philanthropy
–
Social
–
Investor relations
–
Word search
–
Real Estate
–
Arts & humanities
–
E-Commerce (“perfect
capitalism”¹)
–
Education
²Google Semantic Search by David Amerland
¹The Physics of the Future by Michio Kaku
● Other benefits
– SEO - “The Knowledge Graph does
away with the inherent ambiguity of
conventional search at Google's
search box”²
– Accessible community driven
content
– Unleash the full power of “big data”
– Unanticipated reuse
32. The Way Forward
Get the word out
Facilitate ontology development
Build POC's Nurture community and industry involvement
Innovate!
LOD Goes Global
33. Further Reading
●
Use Cases
●
Linked Data e-book
●
Semantic Community
●
Web Science Trust
●
STI International
●
Schema.org
●
semanticweb.com
●
World Wide Web Consortium
●
Depository Trust and Clearing Corporation
●
Freebase
●
Jena
34. Terminology
Term Definition
RDF Resource Description Framework, a way of representing information on the web (ie, a set of
statements), successor to XML, provides the ontology syntax (framework) for describing
resources
OWL Web Ontology Language - Standard framework for describing ontologies, based upon XML for
the low-level syntax and RDF/RDFS for the mid-level syntax
RDFS RDF Schema, recommended structure for RDF resources
R2RML Relational to RDF Mapping Language - W3C sanctioned language for expressing customized
mappings from relational databases to RDF data sets, D2RQ is the same thing in open source
(not as capable)
Open/Closed World Open: Any statement not explicitly known to be true or false is considered unknown (OWL).
Closed: any statement that is not known to be true is false (SQL).
Concept Description logic synonym for “Class”
Jena Apache foundation PPMC (Podling Project Management Committee) to develop a semantic
app framework (Java API)
RIF Rule Interchange Format – W3C recommendation
SPARQL SPARQL Protocol and RDF Query Language – query language for RDF data stores, an
“endpoint” being a resource that can accept such queries and return results
SWRL Semantic Web Rule Language – combines OWL & RML
FIBO Financial Industry Business Ontology
SBVR Semantics of Business Vocabulary and Rules - OMG developed human readable business
modeling language
OMG Object Management Group - Nonprofit industry consortium dedicated to data interoperability
(ex. UML, MDA, CORBA)
OASIS Organization for the Advancement of Structured Information Standards - Standards body for
web services, e-commerce, supply-chain and many other domains (including
35. FacetApp SmartData Platform
OntoApp
Toolkit
FacetApp Platform
Client AppClient App
OntologyOntology
Client AppClient App
OntologyOntology
Enterprise Applications
OntoApp
ServerServer
OntoApp
ServerServer
InferenceInference
ServerServer
InferenceInference
ServerServer
Inferen
ce
Engine
Inferen
ce
Engine
Harness Reusable Objects for
Fast, Scalable Model-driven Enterprise Apps
Configure enterprise applications in a model vs. programming low level source code.
Copyright 2013 FacetApp LLC, Confidential and Proprietary
36. SmartData Breakthrough
Maintained by One Model
Application DataApplication Data
Application LogicApplication Logic
Technical Programmers
Database Admins
Data Scientist
Focus is where it should be – on maximizing ROI