The document describes Pundit, a semantic annotation tool that allows users to create, explore, and consume semantic annotations. Pundit uses an annotation model based on the Open Annotation Collaboration specification. It allows users to organize annotations into notebooks and provides APIs to programmatically access and visualize the annotation data.
A quick presentation to talk about the benefits of structured knowledge, focused on parallax & freebase, and how their knowledge representation fits into the wider scope of the semantic web.
A quick presentation to talk about the benefits of structured knowledge, focused on parallax & freebase, and how their knowledge representation fits into the wider scope of the semantic web.
Development of Semantic Web based Disaster Management SystemNIT Durgapur
Semantic Web model In the field of disaster management to structurise the data such that any information needed during emergency will be easily available.
Tutorial at OAI5 (cern.ch/oai5). Abstract: This tutorial will provide a practical overview of current practices in modelling complex or compound digital objects. It will examine some of the key scenarios around creating complex objects and will explore a number of approaches to packaging and transport. Taking research papers, or scholarly works, as an example, the tutorial will explore the different ways in which these, and their descriptive metadata, can be treated as complex objects. Relevant application profiles and metadata formats will be introduced and compared, such as Dublin Core, in particular the DCMI Abstract Model, and MODS, alongside content packaging standards, such as METS MPEG 21 DIDL and IMS CP. Finally, we will consider some future issues and activities that are seeking to address these. The tutorial will be of interest to librarians and technical staff with an interest in metadata or complex objects, their creation, management and re-use.
About the Webinar
In May 2012, the Library of Congress announced a new modeling initiative focused on reflecting the MARC 21 library standard as a Linked Data model for the Web, with an initial model to be proposed by the consulting company Zepheira. The goal of the initiative is to translate the MARC 21 format to a Linked Data model while retaining the richness and benefits of existing data in the historical format.
In this webinar, Eric Miller of Zepheira will report on progress towards this important goal, starting with an analysis of the translation problem and concluding with potential migration scenarios for a broad-based transition from MARC to a new bibliographic framework.
Create Linked Open Data (LOD) Microthesauri using Art & Architecture Thesaurus (AAT) LOD. View and manage options by a non-techy person. Everyone can use, create,
derive from, & map to AAT microthesauri and make the digital collection become LOD-ready dataset.
Intelligent Expert systems can provide decisions for users for estimate from user preferences to find better destination from user profits. this present provides description of above system and suggest new approach for next researches.
The Semantic Web and Libraries in the United States: Experimentation and Achi...New York University
This presentation reflects the paper titled "The Semantic Web and Libraries in the United States: Experimentation and Achievements," published in the proceedings of 75th IFLA General Conference and Assembly, Satellite Meeting: Emerging Trends in Technology: Libraries between Web 2.0, Semantic Web and Search Technology 8/19-20/2009, in Florence, Italy, presented by Sharon Yang, Rider University, Yanyi Lee, Wagner College, and Amanda Xu, St. John's University. Here is the URL to the full paper: http://www.ifla2009satelliteflorence.it/meeting3/program/assets/SharonYang.pdf
Organisering av digitale prosjekt: Hva har IT-bransjen lært om store prosjekter?Torgeir Dingsøyr
IT-bransjen har gjort store endringer i måten de gjennomfører prosjekter på gjennom bruk av smidige metoder. Disse metodene ble først brukt på små, samlokaliserte team men brukes nå også i store prosjekter med mange team og flere hundre utviklere. Hvordan jobber IT-bransjen for å sikre vellykkede store prosjekter?
Development of Semantic Web based Disaster Management SystemNIT Durgapur
Semantic Web model In the field of disaster management to structurise the data such that any information needed during emergency will be easily available.
Tutorial at OAI5 (cern.ch/oai5). Abstract: This tutorial will provide a practical overview of current practices in modelling complex or compound digital objects. It will examine some of the key scenarios around creating complex objects and will explore a number of approaches to packaging and transport. Taking research papers, or scholarly works, as an example, the tutorial will explore the different ways in which these, and their descriptive metadata, can be treated as complex objects. Relevant application profiles and metadata formats will be introduced and compared, such as Dublin Core, in particular the DCMI Abstract Model, and MODS, alongside content packaging standards, such as METS MPEG 21 DIDL and IMS CP. Finally, we will consider some future issues and activities that are seeking to address these. The tutorial will be of interest to librarians and technical staff with an interest in metadata or complex objects, their creation, management and re-use.
About the Webinar
In May 2012, the Library of Congress announced a new modeling initiative focused on reflecting the MARC 21 library standard as a Linked Data model for the Web, with an initial model to be proposed by the consulting company Zepheira. The goal of the initiative is to translate the MARC 21 format to a Linked Data model while retaining the richness and benefits of existing data in the historical format.
In this webinar, Eric Miller of Zepheira will report on progress towards this important goal, starting with an analysis of the translation problem and concluding with potential migration scenarios for a broad-based transition from MARC to a new bibliographic framework.
Create Linked Open Data (LOD) Microthesauri using Art & Architecture Thesaurus (AAT) LOD. View and manage options by a non-techy person. Everyone can use, create,
derive from, & map to AAT microthesauri and make the digital collection become LOD-ready dataset.
Intelligent Expert systems can provide decisions for users for estimate from user preferences to find better destination from user profits. this present provides description of above system and suggest new approach for next researches.
The Semantic Web and Libraries in the United States: Experimentation and Achi...New York University
This presentation reflects the paper titled "The Semantic Web and Libraries in the United States: Experimentation and Achievements," published in the proceedings of 75th IFLA General Conference and Assembly, Satellite Meeting: Emerging Trends in Technology: Libraries between Web 2.0, Semantic Web and Search Technology 8/19-20/2009, in Florence, Italy, presented by Sharon Yang, Rider University, Yanyi Lee, Wagner College, and Amanda Xu, St. John's University. Here is the URL to the full paper: http://www.ifla2009satelliteflorence.it/meeting3/program/assets/SharonYang.pdf
Organisering av digitale prosjekt: Hva har IT-bransjen lært om store prosjekter?Torgeir Dingsøyr
IT-bransjen har gjort store endringer i måten de gjennomfører prosjekter på gjennom bruk av smidige metoder. Disse metodene ble først brukt på små, samlokaliserte team men brukes nå også i store prosjekter med mange team og flere hundre utviklere. Hvordan jobber IT-bransjen for å sikre vellykkede store prosjekter?
Meta-modeling: concepts, tools and applicationsSaïd Assar
Presentation made as a tutorial at the rcis2015 conference in Athens, Greece, on May 13, 2015.
Video recording available online on IEEE Education (http://www.computer.org/web/computingnow/education)
Automatic Classification of Springer Nature Proceedings with Smart Topic MinerFrancesco Osborne
The process of classifying scholarly outputs is crucial to ensure timely access to knowledge. However, this process is typically carried out manually by expert editors, leading to high costs and slow throughput. In this paper we present Smart Topic Miner (STM), a novel solution which uses semantic web technologies to classify scholarly publications on the basis of a very large automatically generated ontology of research areas. STM was developed to support the Springer Nature Computer Science editorial team in classifying proceedings in the LNCS family. It analyses in real time a set of publications provided by an editor and produces a structured set of topics and a number of Springer Nature classification tags, which best characterise the given input. In this paper we present the architecture of the system and report on an evaluation study conducted with a team of Springer Nature editors. The results of the evaluation, which showed that STM classifies publications with a high degree of accuracy, are very encouraging and as a result we are currently discussing the required next steps to ensure large-scale deployment within the company.
Mining academic social network is becoming increasingly necessary with the increasing amount of data. It
is a favorite topic of research for many researchers. The data mining techniques are used for the mining of
academic social networks. In this paper, we are presenting an efficient frequent item set mining technique
for social academic network. The proposed framework first processes the research documents and then the
enhanced frequent item set mining is applied to find the strength of relationship between the researchers.
The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory
space for computation purpose.
Talk at 3th Keystone Training School - Keyword Search in Big Linked Data - Institute for Software Technology and Interactive Systems, TU Wien, Austria, 2017
Facilitating Data Curation: a Solution Developed in the Toxicology DomainChristophe Debruyne
Christophe Debruyne, Jonathan Riggio, Emma Gustafson, Declan O'Sullivan, Mathieu Vinken, Tamara Vanhaecke, Olga De Troyer.
Presented at the 2020 IEEE 14th International Conference on Semantic Computing, San Diego, California, 3-5 February 2020
Toxicology aims to understand the adverse effects of
chemical compounds or physical agents on living organisms. For
chemicals, much information regarding safety testing of cosmetic
ingredients is now scattered in a plethora of safety evaluation
reports. Toxicologists in our university intend to collect this
information into a single repository. Their current approach uses
spreadsheets, does not scale well, and makes data curation and
querying cumbersome. Semantic technologies (e.g., RDF, OWL,
and Linked Data principles) would be more appropriate for
this purpose. However, this technology is not very accessible to
toxicologists without extensive training. In this paper, we report
on a tool that supports subject matter experts in the construction
of an RDF–based knowledge base for the toxicology domain. The
tool is using the jigsaw metaphor for guiding the subject matter
experts. We demonstrate that the jigsaw metaphor is a viable
option for generating RDF. Future work includes investigating
appropriate methods and tools for knowledge evolution and data
analysis.
Source code summarization is a process of generating summaries that describes software code, the majority of source code summarization usually generated manually, where the summaries are written by software developers. Recently, new automated approaches are becoming more useful. These approaches have been found to be effective in some cases. The main weaknesses of these approaches are that they never exploit code dependencies and summarize either the software classes or methods but not both. This paper proposes a source code summarization approach (Suncode) that produces a short description for each class and method in the software system. To validate the approach, it has been applied on several case studies. Moreover, the generated summaries are compared to summaries that written by human experts and to summaries that written by a state-of-the-art solution. Results of this paper found that Suncode summaries provide better information about code dependencies comparing with other studies. In addition, Suncode summaries can improve and support the current software documentation. The results found that manually written summaries were more precise and short as well.
IUI 2010: An Informal Summary of the International Conference on Intelligent ...J S
Highlights from the main track, poster/demo-session & the VISSW/UDISW/EGIHMI workshops. This is an informal compilation of personal notes from the conference & proceedings, twitter (#iui2010), Ian Ozsvald's blog (http://ianozsvald.com/), and other sources. Citations were not coherently possible, so I chose to stick with links instead. Please let me know if you'd like to see your work more thoroughly referenced.
Discovering User's Topics of Interest in Recommender Systems @ Meetup Machine...Gabriel Moreira
This talk introduces the main techniques of Recommender Systems and Topic Modeling. Then, we present a case of how we've combined those techniques to build Smart Canvas, a SaaS that allows people to bring, create and curate content relevant to their organization, and also helps to tear down knowledge silos.
We give a deep dive into the design of our large-scale recommendation algorithms, giving special attention to a content-based approach that uses topic modeling techniques (like LDA and NMF) to discover people’s topics of interest from unstructured text, and social-based algorithms using a graph database connecting content, people and teams around topics.
Our typical data pipeline that includes the ingestion millions of user events (using Google PubSub and BigQuery), the batch processing of the models (with PySpark, MLib, and Scikit-learn), the online recommendations (with Google App Engine, Titan Graph Database and Elasticsearch), and the data-driven evaluation of UX and algorithms through A/B testing experimentation. We also touch topics about non-functional requirements of a software-as-a-service like scalability, performance, availability, reliability and multi-tenancy and how we addressed it in a robust architecture deployed on Google Cloud Platform.
Short-Bio: Gabriel Moreira is a scientist passionate about solving problems with data. He is Head of Machine Learning at CI&T and Doctoral student at Instituto Tecnológico de Aeronáutica - ITA. where he has also got his Masters on Science. His current research interests are recommender systems and deep learning.
https://www.meetup.com/pt-BR/machine-learning-big-data-engenharia/events/239037949/
Mining academic social network is becoming increasingly necessary with the increasing amount of data. It
is a favorite topic of research for many researchers. The data mining techniques are used for the mining of
academic social networks. In this paper, we are presenting an efficient frequent item set mining technique
for social academic network. The proposed framework first processes the research documents and then the
enhanced frequent item set mining is applied to find the strength of relationship between the researchers.
The proposed method will be fast in comparison to older algorithms. Also it will takes less main memory
space for computation purpose.
Geometric Processing of Data in Neural NetworksLorenzo Cassani
Feed-forward neural networks can be considered as geometric transformations that act on input data points. It is known that, during training, those transformations generally bring points belonging to the same class closer together and drive points belonging to different classes farther away. The purpose of this work is to carry out a numerical analysis of how this description varies during training. The training task consisted of a binary (e.g.\ even digits vs.\ odd digits) of the elements of MNIST and other similar structured datasets, in order to have a clear vision of the link between structure in data and possible noteworthy behaviours in the evolution of the inner geometries of neural networks. Particular attention has been reserved to data points which neural networks struggle more to correctly classify, and their connection to the neural networks generalization capability.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Generating a custom Ruby SDK for your web service or Rails API using Smithy
SDA2013 Pundit: Creating, Exploring and Consuming Annotations
1. PUNDIT: CREATING, EXPLORING AND
CONSUMING SEMANTIC ANNOTATIONS
Marco Grassi(1), Christian Morbidoni(2), Michele Nucci(3),
Simone Fonda(4), Francesca Di Donato(5)
(1,2,3) DII - Department of Information Engineering. Polytechnic University of Le Marche,Ancona, Italy
(4) NET7 srl, Italy
(5)Scuola Normale Superiore, Italy
This work is licensed under a Creative Commons Attribution 3.0 Unported (CC BY 3.0)
www.netseven.it/ www.sns.it/http://semedia.dii.univpm.it
2. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
OUTLINE
1.PUNDITVISION
2.PUNDIT ANNOTATION MODEL
3.DISPLAYING ANNOTATION DATA
4.SOME EXAMPLES
5.CONCLUSIONS
3. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
Semedia,
Università Politecnica delle Marche
http://semedia.dii.univpm.it
Semlib Project Eu Project
http://semedia.dii.univpm.it
DM2E EU Project
http://dm2e.edu/
AGORA EU Project
http://project-agora.eu/
Net7 SRL, Pisa
www.netseven.it/
SUPPORTING PROJECTS:
DEVELOPED BY:
4. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
Pundit won the LODLAM Challenge 2013!
http://summit2013.lodlam.net
Data visualizations, tools, mashups for Linked Open Data in libraries, archives, and museums
http://www.youtube.com/watch?v=6uUQ4f3z_E0
LINKED DATA THE EARLY DAYS...
CHECK OUT
THE VIDEO
5. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
PUNDITVISION
pundit:annot
ation/
id/b2b3e
LINKED DATA CLOUD
The Divine Comedy (Italian: Divina Commedia) is an
epic poem written by Dante Alighieri between c. 1308
and his death in 1321. It is widely considered the
preeminent work of Italian literature, and is seen as
one of the greatest works of world literature.
TEXTUAL COMMENT
ONTOLOGIES &
TAXONOMIES
Pundit allows user to generate semantically structured data when they create annotations!
6. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
PUNDITVISION
EASILY CREATE DYNAMIC DATAVISUALIZATIONS
SEARCH / EXPLORE / SHARE THE ANNOTATIONS
RESTful APIs are provided to consume created annotations (data):
8. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
ANNOTATION MODEL
• Based on Open Annotation Collaboration (OAC)
Contextual Information
Annotation Content
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:created
ex:ANNOTATION-GRAPH-ID-1
dcterms:creator
pundit:annotation/
id/b2b3e
oa:SpecificResource
rdf:type
oa:SpecificResource
oa:hasSource
ex:selector/id/
u89yt
oa:hasSelector
oa:SpecificResource
rdf:type
{"points":[
{"x":0.29, "y":0.35},
{"x":0.48, "y":0.42},
{"x":0.54, "y":0.89},
{"x":0.16, "y":0.69},
]}
rdf:value
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
Named Graph
9. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
NOTEBOOKS
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
pundit:Notebook
ex:MarcoGrassi
a
2011-01-27 10:30:56
My example annotation
rdfs:label
dcterms:created
dcterms:
creator
• Users can organize their annotations in
different notebooks
• Set as Public/Private
• Activate/Deactivate to filter the amount
of public annotations visualizing only
those of interest.
• Identified by a (dereferenciable) URI
• Annotations are collected in notebooks
• Annotations are shared at notebook level.
10. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
ANNOTATION CENTRIC
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
pundit:Notebook
ex:MarcoGrassi
a
2011-01-27 10:30:56
My example annotation
rdfs:label
dcterms:created
dcterms:
creator
• play a fundamental role for data visualization:
The single annotation maintains its integrity
(context, authorship, web location)
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
DATA VISUALIZATION
Explore collection of annotation grouped into notebooks
12. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
ASK (THE PUNDIT)
A portal to manage annotations, share them and explore public notebooks
ANNOTATION CENTRIC VISUALIZATION
http://ask.thepund.it/
13. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
ITEM CENTRIC
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
oa:Annotation
ex:MarcoGrassi
a
2011-01-27 10:30:56
ex:fragment/id/t67u
oac:hasBody
oa:hasTarget
My example annotation
rdfs:label
dcterms:createddcterms:creator
pundit:annotation/
id/b2b3e
ex:ANNOTATION-GRAPH-ID-1
ex:fragment/id/t67u
http://rdf.freebase.com/
en.dante_alighieri
foaf:depicts
The face of Dante
rdfs:label
http://rdf.freebase.com/
en.giotto
dc:creator
pundit:Notebook
ex:MarcoGrassi
a
2011-01-27 10:30:56
My example annotation
rdfs:label
dcterms:created
dcterms:
creator
Using named graph the content of single
annotation content can be merged into
knowledge graph
DATA VISUALIZATION
semlib:Renassance
http://rdf.freebase.com/
en.dante_alighieri
http://example.com/
img1.jpeg
semlib:mentionsAuthor
semlib:depicts
Fragment: Durante gli
Alighieri...
rdfs:label
semlib:
mentionsPeriod
ex:fragment/id/t67u
foaf:depicts
http://rdf.freebase.com/
en.giottodc:
creator
http://rdf.freebase.com/
en.dante_alighieri
ex:describe
ex:fragment/id/t67u
http://rdf.freebase.com/
en.arthur_schopenhauer
dc:author
ex:fragment/id/t67ucito:cites
http://rdf.freebase.com/
en.plato
dc:author
ex:fragment/id/t67u
http://rdf.freebase.com/
en.arthur_schopenhauer dc:author
ex:fragment/id/t67u
cito:cites
http://rdf.freebase.com/
en.plato
dc:author
Fragment: Cosi come
immobile...
rdfs:label
Fragment: Fatti non
foste a viver come
bruti...
rdfs:label
Visualization on the annotated items and their relations with other items
A-priori knowledge on ontologies/custom vocabularies and relations used
in annotations is beneficial!
14. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
CONFIGURING PUNDIT
ITEM CENTRIC VISUALIZATION
CustomVocabularies/Taxonomies:
• Online JSONp file (created manually or
automatically from an ontology )
• Add URL to Pundit configuration
Linked Data Selector:
15. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
CONFIGURING PUNDIT
ITEM CENTRIC VISUALIZATION
create specific pattern in the annotations to create engaging user
interfaces
Predicates used in relations:
address different communities and foster data reuse
16. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
DEPLOYING PUNDIT
• As a JavaScript library
• As a bookmarklet
• As a Browser Extension
(Chrome and Firefox)
Select between different
instances of Pundit
Restrict the web pages where
Pundit is automatically launched
ITEM CENTRIC VISUALIZATION
https://github.com/marcograssi/
PunditBookmarklet
17. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
CREATING DATAVISUALIZATION
ITEM CENTRIC VISUALIZATION
semlib:Renassance
http://rdf.freebase.com/
en.dante_alighieri
http://example.com/
img1.jpeg
semlib:mentionsAuthor
semlib:depicts
Fragment: Durante gli
Alighieri...
rdfs:label
semlib:
mentionsPeriod
ex:fragment/id/t67u
foaf:depicts
http://rdf.freebase.com/
en.giottodc:
creator
http://rdf.freebase.com/
en.dante_alighieri
ex:describe
ex:fragment/id/t67u
http://rdf.freebase.com/
en.arthur_schopenhauer
dc:author
ex:fragment/id/t67ucito:cites
http://rdf.freebase.com/
en.plato
dc:author
ex:fragment/id/t67u
http://rdf.freebase.com/
en.arthur_schopenhauer dc:author
ex:fragment/id/t67u
cito:cites
http://rdf.freebase.com/
en.plato
dc:author
Fragment: Cosi come
immobile...
rdfs:label
Fragment: Fatti non
foste a viver come
bruti...
rdfs:label
REST API + SPARQL
PUNDIT
custom
Specific patterns &
Standardized vocabularies
COMMUNITYUSERS
SPECIALIZED DATA VISUALIZATION
PUNDIT
custom
Other data sources
Third party
libraries and tools
18. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
TIMELINE
RESTAPI
semlib:Renassance
http://rdf.freebase.com/
en.dante_alighieri
http://example.com/
img1.jpeg
semlib:mentionsAuthor
semlib:depicts
Fragment: Durante gli
Alighieri...
rdfs:label
semlib:
mentionsPeriod
ex:fragment/id/t67u
foaf:depicts
http://rdf.freebase.com/
en.giottodc:
creator
http://rdf.freebase.com/
en.dante_alighieri
ex:describe
ex:fragment/id/t67u
http://rdf.freebase.com/
en.arthur_schopenhauer
dc:author
ex:fragment/id/t67ucito:cites
http://rdf.freebase.com/
en.plato
dc:author
ex:fragment/id/t67u
http://rdf.freebase.com/
en.arthur_schopenhauer dc:author
ex:fragment/id/t67u
cito:cites
http://rdf.freebase.com/
en.plato
dc:author
Fragment: Cosi come
immobile...
rdfs:label
Fragment: Fatti non
foste a viver come
bruti...
rdfs:label
Notebook ID
TimelineJS
compliant JSON
• ReuseTimelineJS (http://www.timeline.verite.co/)
• Create annotations in a notebook of text fragment or images, containing a
date, title and creator.
EASILY CREATE INTERACTIVE TIMELINE USING PUNDIT...
19. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
TIMELINE
...JUST LIKE THIS ONE I’VE CREATED DURING THE WORKSHOP
http://semedia.dii.univpm.it/news/16-having-fun-with-pundit-sda-2013
20. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
EDGEMAPSVISUALIZATION
BodeRaphael
influenced
Every-time that an annotation like this is created A relation is generated between the authors
ex:fragment/id/t67u
http://rdf.freebase.com/
en.wilhelm_von_bode
dc:author
ex:fragment/id/t67u cito:cites
http://rdf.freebase.com/
en.raphael
dc:author
Reuse http://mariandoerk.de/edgemaps/
21. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
BURCKHARDT SOURCE
http://burckhardtsource.org/
• Burckhardtsource.org platform aims at mapping and producing a critical edition of
the extensive correspondence of 400 European intellectuals with Jacob
Burckhardt over a period of more than half a century from 1842 to 1897.
• Resources of interest:
Person, Places,Works of art
• Named entities source:
Freebase
•Missing resources have been
added to Freebase
24. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
MORE ON http://thepund.it
http://ask.thepund.it/#/timeline/31951d93
DATA JOURNALISM LOD LIVE
25. Pundit: Creating, Exploring and Consuming Semantic Annotations m.grassi@univpm.itSDA 2013
CONCLUSIONS
• Pundit a customizable and flexible semantic web annotation tool.
• Custom instances can be created for specific use scenarios...
• ...and easily deployed to users (bookmarklet or browser extension).
• Specific data visualization can be created also using third party
applications.
26. http://thepund.it
THANKYOU!
Semlib Project Eu Project
http://www.semlibproject.eu/
DM2E EU Project
http://dm2e.edu/
AGORA EU Project
http://project-agora.eu/
This work is licensed under a Creative Commons Attribution 3.0 Unported (CC BY 3.0)
www.netseven.it/ www.sns.it/http://semedia.dii.univpm.it