The literature contains a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data reuse. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data.
Content + Signals: The value of the entire data estate for machine learningPaul Groth
Content-centric organizations have increasingly recognized the value of their material for analytics and decision support systems based on machine learning. However, as anyone involved in machine learning projects will tell you the difficulty is not in the provision of the content itself but in the production of annotations necessary to make use of that content for ML. The transformation of content into training data often requires manual human annotation. This is expensive particularly when the nature of the content requires subject matter experts to be involved.
In this talk, I highlight emerging approaches to tackling this challenge using what's known as weak supervision - using other signals to help annotate data. I discuss how content companies often overlook resources that they have in-house to provide these signals. I aim to show how looking at a data estate in terms of signals can amplify its value for artificial intelligence.
From Text to Data to the World: The Future of Knowledge GraphsPaul Groth
Keynote Integrative Bioinformatics 2018
https://docs.google.com/document/d/1E7D4_CS0vlldEcEuknXjEnSBZSZCJvbI5w1FdFh-gG4/edit
Can we improve research productivity through providing answers stemming from knowledge graphs? In this presentation, I discuss different ways of building and combining knowledge graphs.
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
The need for a transparent data supply chainPaul Groth
Illustrating data supply chains and motivating the need for a more transparent data supply chain in the context of responsible data science. Presented at the 2018 KNAW-Royal Society bilateral meeting on responsible data science.
Presentation for NEC Lab Europe.
Knowledge graphs are increasingly built using complex multifaceted machine learning-based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. I present work on combining knowledge graph construction (e.g. information extraction) and refinement (e.g. link prediction) in end to end systems. In particular, I will discuss recent work on using inductive representations for link predication. I then discuss the challenges of ongoing system maintenance, knowledge graph quality and traceability.
Combining Explicit and Latent Web Semantics for Maintaining Knowledge GraphsPaul Groth
A look at how the thinking about Web Data and the sources of semantics can help drive decisions on combining latent and explicit knowledge. Examples from Elsevier and lots of pointers to related work.
Content + Signals: The value of the entire data estate for machine learningPaul Groth
Content-centric organizations have increasingly recognized the value of their material for analytics and decision support systems based on machine learning. However, as anyone involved in machine learning projects will tell you the difficulty is not in the provision of the content itself but in the production of annotations necessary to make use of that content for ML. The transformation of content into training data often requires manual human annotation. This is expensive particularly when the nature of the content requires subject matter experts to be involved.
In this talk, I highlight emerging approaches to tackling this challenge using what's known as weak supervision - using other signals to help annotate data. I discuss how content companies often overlook resources that they have in-house to provide these signals. I aim to show how looking at a data estate in terms of signals can amplify its value for artificial intelligence.
From Text to Data to the World: The Future of Knowledge GraphsPaul Groth
Keynote Integrative Bioinformatics 2018
https://docs.google.com/document/d/1E7D4_CS0vlldEcEuknXjEnSBZSZCJvbI5w1FdFh-gG4/edit
Can we improve research productivity through providing answers stemming from knowledge graphs? In this presentation, I discuss different ways of building and combining knowledge graphs.
Data Communities - reusable data in and outside your organization.Paul Groth
Description
Data is a critical both to facilitate an organization and as a product. How can you make that data more usable for both internal and external stakeholders? There are a myriad of recommendations, advice, and strictures about what data providers should do to facilitate data (re)use. It can be overwhelming. Based on recent empirical work (analyzing data reuse proxies at scale, understanding data sensemaking and looking at how researchers search for data), I talk about what practices are a good place to start for helping others to reuse your data. I put this in the context of the notion data communities that organizations can use to help foster the use of data both within your organization and externally.
The need for a transparent data supply chainPaul Groth
Illustrating data supply chains and motivating the need for a more transparent data supply chain in the context of responsible data science. Presented at the 2018 KNAW-Royal Society bilateral meeting on responsible data science.
Presentation for NEC Lab Europe.
Knowledge graphs are increasingly built using complex multifaceted machine learning-based systems relying on a wide of different data sources. To be effective these must constantly evolve and thus be maintained. I present work on combining knowledge graph construction (e.g. information extraction) and refinement (e.g. link prediction) in end to end systems. In particular, I will discuss recent work on using inductive representations for link predication. I then discuss the challenges of ongoing system maintenance, knowledge graph quality and traceability.
Combining Explicit and Latent Web Semantics for Maintaining Knowledge GraphsPaul Groth
A look at how the thinking about Web Data and the sources of semantics can help drive decisions on combining latent and explicit knowledge. Examples from Elsevier and lots of pointers to related work.
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
The Challenge of Deeper Knowledge Graphs for SciencePaul Groth
Over the past 5 years, we have seen multiple successes in the development of knowledge graphs for supporting science in domains ranging from drug discovery to social science. However, in order to really improve scientific productivity, we need to expand and deepen our knowledge graphs. To do so, I believe we need to address two critical challenges: 1) dealing with low resource domains; and 2) improving quality. In this talk, I describe these challenges in detail and discuss some efforts to overcome them through the application of techniques such as unsupervised learning; the use of non-experts in expert domains, and the integration of action-oriented knowledge (i.e. experiments) into knowledge graphs.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
The Roots: Linked data and the foundations of successful Agriculture DataPaul Groth
Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Data science remains a high-touch activity, especially in life, physical, and social sciences. Data management and manipulation tasks consume too much bandwidth: Specialized tools and technologies are difficult to use together, issues of scale persist despite the Cambrian explosion of big data systems, and public data sources (including the scientific literature itself) suffer curation and quality problems.
Together, these problems motivate a research agenda around “human-data interaction:” understanding and optimizing how people use and share quantitative information.
I’ll describe some of our ongoing work in this area at the University of Washington eScience Institute.
In the context of the Myria project, we're building a big data "polystore" system that can hide the idiosyncrasies of specialized systems behind a common interface without sacrificing performance. In scientific data curation, we are automatically correcting metadata errors in public data repositories with cooperative machine learning approaches. In the Viziometrics project, we are mining patterns of visual information in the scientific literature using machine vision, machine learning, and graph analytics. In the VizDeck and Voyager projects, we are developing automatic visualization recommendation techniques. In graph analytics, we are working on parallelizing best-of-breed graph clustering algorithms to handle multi-billion-edge graphs.
The common thread in these projects is the goal of democratizing data science techniques, especially in the sciences.
Thoughts on Knowledge Graphs & Deeper ProvenancePaul Groth
Thinking about the need for deeper provenance for knowledge graphs but also using knowledge graphs to enrich provenance. Presented at https://seminariomirianandres.unirioja.es/sw19/
The Challenge of Deeper Knowledge Graphs for SciencePaul Groth
Over the past 5 years, we have seen multiple successes in the development of knowledge graphs for supporting science in domains ranging from drug discovery to social science. However, in order to really improve scientific productivity, we need to expand and deepen our knowledge graphs. To do so, I believe we need to address two critical challenges: 1) dealing with low resource domains; and 2) improving quality. In this talk, I describe these challenges in detail and discuss some efforts to overcome them through the application of techniques such as unsupervised learning; the use of non-experts in expert domains, and the integration of action-oriented knowledge (i.e. experiments) into knowledge graphs.
Keynote for Theory and Practice of Digital Libraries 2017
The theory and practice of digital libraries provides a long history of thought around how to manage knowledge ranging from collection development, to cataloging and resource description. These tools were all designed to make knowledge findable and accessible to people. Even technical progress in information retrieval and question answering are all targeted to helping answer a human’s information need.
However, increasingly demand is for data. Data that is needed not for people’s consumption but to drive machines. As an example of this demand, there has been explosive growth in job openings for Data Engineers – professionals who prepare data for machine consumption. In this talk, I overview the information needs of machine intelligence and ask the question: Are our knowledge management techniques applicable for serving this new consumer?
The Roots: Linked data and the foundations of successful Agriculture DataPaul Groth
Some thoughts on successful data for the agricultural domain. Keynote at Linked Open Data in Agriculture
MACS-G20 Workshop in Berlin, September 27th and 28th, 2017 https://www.ktbl.de/inhalte/themen/ueber-uns/projekte/macs-g20-loda/lod/
Sources of Change in Modern Knowledge Organization SystemsPaul Groth
Talk covering how knowledge graphs are making us rethink how change occurs in Knowledge Organization Systems. Based on https://arxiv.org/abs/1611.00217
Prov-O-Viz is a visualisation service for provenance graphs expressed using the W3C PROV vocabulary. It uses the Sankey-style visualisation from D3js.
See http://provoviz.org
ISMB/ECCB 2013 Keynote Goble Results may vary: what is reproducible? why do o...Carole Goble
Keynote given by Carole Goble on 23rd July 2013 at ISMB/ECCB 2013
http://www.iscb.org/ismbeccb2013
How could we evaluate research and researchers? Reproducibility underpins the scientific method: at least in principle if not practice. The willing exchange of results and the transparent conduct of research can only be expected up to a point in a competitive environment. Contributions to science are acknowledged, but not if the credit is for data curation or software. From a bioinformatics view point, how far could our results be reproducible before the pain is just too high? Is open science a dangerous, utopian vision or a legitimate, feasible expectation? How do we move bioinformatics from one where results are post-hoc "made reproducible", to pre-hoc "born reproducible"? And why, in our computational information age, do we communicate results through fragmented, fixed documents rather than cohesive, versioned releases? I will explore these questions drawing on 20 years of experience in both the development of technical infrastructure for Life Science and the social infrastructure in which Life Science operates.
Data science remains a high-touch activity, especially in life, physical, and social sciences. Data management and manipulation tasks consume too much bandwidth: Specialized tools and technologies are difficult to use together, issues of scale persist despite the Cambrian explosion of big data systems, and public data sources (including the scientific literature itself) suffer curation and quality problems.
Together, these problems motivate a research agenda around “human-data interaction:” understanding and optimizing how people use and share quantitative information.
I’ll describe some of our ongoing work in this area at the University of Washington eScience Institute.
In the context of the Myria project, we're building a big data "polystore" system that can hide the idiosyncrasies of specialized systems behind a common interface without sacrificing performance. In scientific data curation, we are automatically correcting metadata errors in public data repositories with cooperative machine learning approaches. In the Viziometrics project, we are mining patterns of visual information in the scientific literature using machine vision, machine learning, and graph analytics. In the VizDeck and Voyager projects, we are developing automatic visualization recommendation techniques. In graph analytics, we are working on parallelizing best-of-breed graph clustering algorithms to handle multi-billion-edge graphs.
The common thread in these projects is the goal of democratizing data science techniques, especially in the sciences.
This presentation was provided by Chris Erdmann of Library Carpentries and by Judy Ruttenberg of ARL during the NISO virtual conference, Open Data Projects, held on Wednesday, June 13, 2018.
Talk at JISC Repositories conference intended for repository managers or research managers on some of the issues involved. Talk had to be originally given unaided because of a technology problem!
Data Harmonization for a Molecularly Driven Health SystemWarren Kibbe
Maximizing the value of data, computing, data science in an academic medical center, or 'towards a molecularly informed Learning Health System. Given in October at the University of Florida in Gainesville
A presentation I gave at the 2018 Molecular Med Tri-Con in San Francisco, February 2018. This addresses the general challenge of biomedical data management, some of the things to consider when evaluation solutions in this space, and concludes with a brief summary of some of the tools and platforms in this space.
CODATA International Training Workshop in Big Data for Science for Researcher...Johann van Wyk
Presentation at NeDICC Meeting on 16 July 2014. Feedback from CODATA International Training Workshop in Big Data for Science for Researchers from Emerging and Developing Countries, Beijing, China, 5-20 June 2014
SciDataCon 2014 Data Papers and their applications workshop - NPG Scientific ...Susanna-Assunta Sansone
Part of the SciDataCon14 workshop on "Data Papers and their applications" run by myself and Brian Hole to help attendees understand current data-publishing journals and trends and help them understand the editorial processes on NPG's Scientific Data and Ubiquity's Open Health Data.
Data Curation and Debugging for Data Centric AIPaul Groth
It is increasingly recognized that data is a central challenge for AI systems - whether training an entirely new model, discovering data for a model, or applying an existing model to new data. Given this centrality of data, there is need to provide new tools that are able to help data teams create, curate and debug datasets in the context of complex machine learning pipelines. In this talk, I outline the underlying challenges for data debugging and curation in these environments. I then discuss our recent research that both takes advantage of ML to improve datasets but also uses core database techniques for debugging in such complex ML pipelines.
Presented at DBML 2022 at ICDE - https://www.wis.ewi.tudelft.nl/dbml2022
Diversity and Depth: Implementing AI across many long tail domainsPaul Groth
Presentation at the IJCAI 2018 Industry Day
Elsevier serves researchers, doctors, and nurses. They have come to expect the same AI based services that they use in everyday life in their work environment, e.g.: recommendations, answer driven search, and summarized information. However, providing these sorts of services over the plethora of low resource domains that characterize science and medicine is a challenging proposition. (For example, most of the shelf NLP components are trained on newspaper corpora and exhibit much worse performance on scientific text). Furthermore, the level of precision expected in these domains is quite high. In this talk, we overview our efforts to overcome this challenge through the application of four techniques: 1) unsupervised learning; 2) leveraging of highly skilled but low volume expert annotators; 2) designing annotation tasks for non-experts in expert domains; and 4) transfer learning. We conclude with a series of open issues for the AI community stemming from our experience.
Progressive Provenance Capture Through Re-computationPaul Groth
Provenance capture relies upon instrumentation of processes (e.g. probes or extensive logging). The more instrumentation we can add to processes the richer our provenance traces can be, for example, through the addition of comprehensive descriptions of steps performed, mapping to higher levels of abstraction through ontologies, or distinguishing between automated or user actions. However, this instrumentation has costs in terms of capture time/overhead and it can be difficult to ascertain what should be instrumented upfront. In this talk, I'll discuss our research on using record-replay technology within virtual machines to incrementally add additional provenance instrumentation by replaying computations after the fact.
Research Data Sharing: A Basic FrameworkPaul Groth
Some thoughts on thinking about data sharing. Prepared for the 2016 LERU Doctoral Summer School - Data Stewardship for Scientific Discovery and Innovation.
http://www.dtls.nl/fair-data/fair-data-training/leru-summer-school/
Data for Science: How Elsevier is using data science to empower researchersPaul Groth
Each month 12 million people use Elsevier’s ScienceDirect platform. The Mendeley social network has 4.6 million registered users. 3500 institutions make use of ClinicalKey to bring the latest in medical research to doctors and nurses. How can we help these users be more effective? In this talk, I give an overview of how Elsevier is employing data science to improve its services from recommendation systems, to natural language processing and analytics. While data science is changing how Elsevier serves researchers, it’s also changing research practice itself. In that context, I discuss the impact that large amounts of open research data are having and the challenges researchers face in making use of it, in particular, in terms of data integration and reuse. We are at just beginning to see of how technology and data is changing science correspondingly this impacts how best to empower those who practice it.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
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!
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
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
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.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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/
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Knowledge engineering: from people to machines and back
Minimal viable-datareuse-czi
1. Minimal Viable Data Reuse
Prof. Paul Groth | @pgroth | pgroth.com | indelab.org
Thanks to Dr. Kathleen Gregory, Dr. Laura Koesten, Prof. Elena
Simperl, Dr. Pavlos Vougiouklis, Dr. Andrea Scharnhorst, Prof. Sally Wyatt
CZI Seed Networks Computational Biology
April 6, 2021
2. Prof. Elena Simperl
King’s College London
Dr. Laura Koesten
King’s College London /
University of Vienna
Dr. Kathleen Gregory
KNAW DANS
Prof. Sally Wyatt
Maastricht University
Dr. Andrea Scharnhorst
KNAW DANS
Dr. Pavlos Vougiouklis
Huawei
We investigate intelligent systems that support people in
their work with data and information from diverse sources.
In this area, we perform applied and fundamental research
informed by empirical insights into data science practice.
Current topics:
• Automated Knowledge Base Construction
• Data Search + Data Provenance
• Data Management for Machine Learning
• Causality for machine learning on messy data
indelab.org
Thanks to my
collaborators on this work in
HCI, social science, humanities
3. What should we do as data providers to enable data reuse?
5. Lots of good advice
• Maybe a bit too much….
• Currently, 140 policies on fairsharing.org as
of April 5, 2021
• We reviewed 40 papers
• Cataloged 39 different features of datasets
that enable data reuse
6. Enable access
Feature Description References
Access
License (1) available, (2) allows reuse W3C 3,22,45–47
Format/machine readability
(1) consistent format, (2) single value type per column, (3) human as well as
machine readable and non-proprietary format, (4) different formats available
W3C2,22,48–50
Code available for cleaning, analysis, visualizations 51–53
Unique identifier PID for the dataset/ID's within the dataset W3C2,53
Download link/API (1) available, (2) functioning W3C47,50
7. Document
Documentation: Methodological Choices
Methodology
description of experimental setup (sampling,
tools, etc.), link to publication or project
3,13,54,60,63,66
Units and reference systems (1) defined, (2) consistently used 54,67
Representativeness/Population in relation to a total population 21,60
Caveats
changes: classification/seasonal or special
event/sample size/coverage/rounding
48,54
Cleaning/pre-processing
(1) cleaning choices described, (2) are the raw
data available?
3,13,21,68
Biases/limitations different types of bias (i.e., sampling bias) 21,49,69
Data management (1) mode of storage, (2) duration of storage 3,70,71
Documentation: Quality
Missing values/null values
(1) defined what they mean, (2) ratio of empty
cells
W3C22,48,49,59,60
Margin of error/reliability/quality control
procedures
(1) confidence intervals, (2) estimates versus
actual measurements
54,65
Formatting
(1) consistent data type per column, (2)
consistent date format
W3C41,65
Outliers
are there data points that differ significantly from
the rest
22
Possible options/constraints on a variable
(1) value type, (2) if data contains an “other”
category
W3C72
Last update
information about data maintenance if
applicable
21,62
Documentation: Summary Representations and
Understandability
Description/README file
meaningful textual description (can also
include text, code, images)
22,54,55
Purpose purpose of data collection, context of creation 3,21,49,56,57
Summarizing statistics (1) on dataset level, (2) on column level 22,49
Visual representations statistical properties of the dataset 22,58
Headers understandable
(1) column-level documentation (e.g.,
abbreviations explained), (2) variable types, (3)
how derived (e.g., categorization, such as
labels or codes)
22,59,60
Geographical scope (1) defined, (2) level of granularity 45,54,61,62
Temporal scope (1) defined, (2) level of granularity 45,54,61,62
Time of data collection (1) when collected, (2) what time span 63–65
8. Situate
Connections
Relationships between variables defined (1) explained in documentation, (2) formulae 21,22
Cite sources (1) links or citation, (2) indication of link quality 21
Links to dataset being used elsewhere i.e., in publications, community-led projects 21,59
Contact person or organization, mode of contact specified W3C41,73
Provenance and Versioning
Publisher/producer/repository
(1) authoritativeness of source, (2) funding
mechanisms/other interests that influenced data
collection specified
21,49,54,59,74,
75
Version indicator version or modification of dataset documented W3C50,66,76
Version history workflow provenance W3C50,76
Prior reuse/advice on data reuse (1) example projects, (2) access to discussions 3,27,59,60
Ethics
Ethical considerations, personal data
(1) data related to individually identifiable
people, (2) if applicable, was consent
given
21,57,71,75
Semantics
Schema/Syntax/Data Model defined W3C47,67
Use of existing taxonomies/vocabularies (1) documented, (2) link W3C2
9. Where should a data provider start?
• Lots of good advice!
• It would be great to do all these things
• But it’s all a bit overwhelming
• Can we help prioritize?
10. Getting some data
• Used Github as a case study
• ~1.4 million datasets (e.g. CSV, excel) from
~65K repos
• Use engagement metrics as proxies for data
reuse
• Map literature features to both dataset and
repository features
• Train a predictive model to see what are
features are good predictors
12. Where to start?
• Some ideas from this study if you’re publishing data with
Github
• provide an informative short textual summary of the
dataset
• provide a comprehensive README file in a
structured form and links to further information
• datasets should not exceed standard processable file
sizes
• datasets should be possible to open with a standard
configuration of a common library (such as Pandas)
Trained a Recurrent Neural Network. Might be better models but useful for
handling text, Not the greatest predicator (good for classifying not reuse)
but still useful for helping us tease out features
15. How would you make sense of this data?
Koesten, L., Gregory, K., Groth, P., & Simperl, E. (2021). Talking datasets –
Understanding data sensemaking behaviours. International Journal of Human-
Computer Studies, 146, 102562. https://doi.org/10.1016/j.ijhcs.2020.102562
16. Patterns of data-centric sense making
• 31 research “data people”
• Brought their own data
• Presented with unknown data
• Think-out loud
• Talk about both their data and then given data
• Interview transcripts + screen captures
18. Engaging with data
Known Unknown
Acronyms
and
abbreviations
“That is a classic abbreviation in the field of hepatic surgery. AFP is
alpha feto protein. It is a marker. It’s very well known by everybody...the
AFP score is a criterion for liver transplantation. (P22)”
“I’m not sure what ‘long’ means. I wonder if it’s not
something to do with longevity. On the other hand, no, it’s
got negative numbers. I can’t make sense of this. (P7)”
Identifiying
strange
things
“Although we’ve tried really hard, because we’ve put in a coding frame
and how we manipulate all the data, I’m sure that there are things in
there which we haven’t recorded in terms of, well, what exactly does
this mean? I hope we’ve covered it all but I’m sure we haven’t. (P10)”
“Now that sounds quite high for the Falklands. I wouldn’t have
thought the population was all that great...and yet it’s only one
confirmed case. Okay [laughs]. So yes...one might need to
actually examine that a little bit more carefully, because the
population of the Falklands doesn’t reach a million, so
therefore you end up with this huge number of deaths per
million population [laughs], but only one case and one death.
(P23)”
19. Placing data
• P2: It’s listing the countries for which data are available, not sure if
this is truly all countries we know of...
• P8: It includes essentially every country in the world
• P29: Global data
• P30: I would like to know whether it’s complete...it says 212 rows
representing countries, whether I have data from all countries or
only from 25% or something because then it’s not really
representative.
• P7: If it was the whole country that was affected or not, affecting the
northern part, the western, eastern, southern parts
• P24: Was it sampled and then estimated for the whole country? Or
is it the exact number of deaths that were got from hospitals and
health agencies, for example? So is it a census or is it an estimate?
21. Recommendations
✅ for data providers
• Help users understand shape
• Provide information at the dataset level (e.g. summaries) ✅
• Column level summaries
• Make it easier to pan and zoom
• Use strange things as an entry point
• Flag and highlight strange things ✅
• Provide explanations of abbreviations and missing values ✅
• Provide metrics or links to other information structures necessary for
understanding the column’s content ✅
• Include links to basic concepts ✅
• Highlight relationships between columns or entities ✅
• Identify anchor variables that are considered most important ✅
• Help users placing data
• Embrace different levels of expertise and enable drill down
• Link to standardized definitions ✅
• Connect to broader forms of documentation ✅
22. Data is Social
Do you want a data community?
Gregory, K., Groth, P. Scharnhorst, A., Wyatt, S. (2020). Lost
or found? Discovering data needed for research. Harvard Data
Science Review. https://doi.org/10.1162/99608f92.e38165eb
23. Conclusion
• For data platforms
• Think about ways of measuring data reuse
• Tooling for summaries and overviews of data
• Automated linking to information for sense making
• For data providers
• Simple steps
• Focus on making it easy to “get to know” your data.
• Easy to load and explore (e.g. in pandas, excel, community tool)
• Links to more information
• Are you trying to be a part or build a data community?
• We still need a lot more work on data practices and methods informed by
practices
Paul Groth | @pgroth | pgroth.com | indelab.org
Editor's Notes
The majority of participants mentioned the overall topic or title as one of the first two attributes (n = 24); roughly half of participants mentioned the format or shape of the data (e.g. the number of columns, rows or observations) either first or second (n = 15).