Supporting Sensemaking by Modelling Discourse as Hypermedia NetworksSimon Buckingham Shum
10 July 2009: Presentation to W3C "Semantic Web for Health Care and Life Sciences" Interest Group: "Scientific Discourse" task group: http://esw.w3.org/topic/HCLSIG/SWANSIOC
General Session: Successful Culture Development & Integration in an Active M&...WorkforceNEXT
Presented by Marty Kunz - VP of HR at C&J Energy, Roger Mosby - VP of HR at Kinder Morgan, Laura Ramey - VP of HR at Crestwood Midstream. WorkforceNEXT Summit. September 30, 2014.
From Bibliometrics to Cybermetrics - a book chapter by Nicola de BellisXanat V. Meza
Disclaimer: All original texts and images belong to their rightful owners.
Chapter 8 of the book "Bibliometrics and Citation Analysis" by Nicola de Bellis.
Social and Collaborative Construction of Structured Knowledge WWW2007Simon Buckingham Shum
Sereno, B., Buckingham Shum, S. and Motta, E. (2007). Formalization, User Strategy and Interaction Design: Users’ Behaviour with Discourse Tagging Semantics. Workshop on Social and Collaborative Construction of Structured Knowledge, 16th International World Wide Web Conference (WWW 2007), Banff, AB, Canada; 8-12 May 2007. [http://www2007.org/workshops/paper_30.pdf]
Supporting Sensemaking by Modelling Discourse as Hypermedia NetworksSimon Buckingham Shum
10 July 2009: Presentation to W3C "Semantic Web for Health Care and Life Sciences" Interest Group: "Scientific Discourse" task group: http://esw.w3.org/topic/HCLSIG/SWANSIOC
General Session: Successful Culture Development & Integration in an Active M&...WorkforceNEXT
Presented by Marty Kunz - VP of HR at C&J Energy, Roger Mosby - VP of HR at Kinder Morgan, Laura Ramey - VP of HR at Crestwood Midstream. WorkforceNEXT Summit. September 30, 2014.
From Bibliometrics to Cybermetrics - a book chapter by Nicola de BellisXanat V. Meza
Disclaimer: All original texts and images belong to their rightful owners.
Chapter 8 of the book "Bibliometrics and Citation Analysis" by Nicola de Bellis.
Social and Collaborative Construction of Structured Knowledge WWW2007Simon Buckingham Shum
Sereno, B., Buckingham Shum, S. and Motta, E. (2007). Formalization, User Strategy and Interaction Design: Users’ Behaviour with Discourse Tagging Semantics. Workshop on Social and Collaborative Construction of Structured Knowledge, 16th International World Wide Web Conference (WWW 2007), Banff, AB, Canada; 8-12 May 2007. [http://www2007.org/workshops/paper_30.pdf]
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
A whirlwind introduction to digital humanities for CDP Digital Humanities: Collections & Heritage - current challenges and futures workshop. February 22, 2018 Imperial War Museum
Nuts & Bolts of Research Methods: Doctoral Training ConferenceThe Open University, March 22nd 2011
Simon Buckingham Shum
Knowledge Media Institute
Open University UK
http://simon.buckinghamshum.net
Introduction to Computational Social Science - Lecture 1Lauri Eloranta
First lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015. (http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
The Liber 2009 presentation repeated for a Dutch audience IN Dutch but with the english slides (just the first one is in Dutch :-)
Samenwerking Hogeschool bibliotheken SHB, 5 november 2009
In the last decade, several Scientific Knowledge Graphs (SKG) were released, representing scientific knowledge in a structured, interlinked, and semantically rich manner. But, what kind of information they describe? How they have been built? What can we do with them? In this lecture, I will first provide an overview of well-known SKGs, like Microsoft Academic Graph, Dimensions, and others. Then, I will present the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to i) the research topics drawn from the Computer Science Ontology, ii) the type of the author's affiliations (e.g, academia, industry), and iii) 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). Finally, I will showcase a number of tools and approaches using such SKGs, supporting researchers, companies, and policymakers in making sense of research dynamics.
Keynote presentation delivered at ELAG 2013 in Gent, Belgium, on May 29 2013. Discusses Research Objects and the relationship to work my team has been involved in during the past couple of years: OAI-ORE, Open Annotation, Memento.
Lecture presented by Marian Ramos Eclevia at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016
The Generative AI System Shock, and some thoughts on Collective Intelligence ...Simon Buckingham Shum
Keynote Address: Team-based Learning Collaborative Asia Pacific Community (TBLC-APC) Symposium (“Impact of emerging technologies on learning strategies”) 8-9 February 2024, Sydney https://tbl.sydney.edu.au
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
The Computer Science Ontology: A Large-Scale Taxonomy of Research AreasAngelo Salatino
Ontologies of research areas are important tools for characterising, exploring, and analysing the research landscape. Some fields of research are comprehensively described by large-scale taxonomies, e.g., MeSH in Biology and PhySH in Physics. Conversely, current Computer Science taxonomies are coarse-grained and tend to evolve slowly. For instance, the ACM classification scheme contains only about 2K research topics and the last version dates back to 2012. In this paper, we introduce the Computer Science Ontology (CSO), a large-scale, automatically generated ontology of research areas, which includes about 15K topics and 70K semantic relationships. It was created by applying the Klink-2 algorithm on a very large dataset of 16M scientific articles. CSO presents two main advantages over the alternatives: i) it includes a very large number of topics that do not appear in other classifications, and ii) it can be updated automatically by running Klink-2 on recent corpora of publications. CSO powers several tools adopted by the editorial team at Springer Nature and has been used to enable a variety of solutions, such as classifying research publications, detecting research communities, and predicting research trends. To facilitate the uptake of CSO we have developed the CSO Portal, a web application that enables users to download, explore, and provide granular feedback on CSO at different levels. Users can use the portal to rate topics and relationships, suggest missing relationships, and visualise sections of the ontology. The portal will support the publication of and access to regular new releases of CSO, with the aim of providing a comprehensive resource to the various communities engaged with scholarly data.
A whirlwind introduction to digital humanities for CDP Digital Humanities: Collections & Heritage - current challenges and futures workshop. February 22, 2018 Imperial War Museum
Nuts & Bolts of Research Methods: Doctoral Training ConferenceThe Open University, March 22nd 2011
Simon Buckingham Shum
Knowledge Media Institute
Open University UK
http://simon.buckinghamshum.net
Introduction to Computational Social Science - Lecture 1Lauri Eloranta
First lecture of the course CSS01: Introduction to Computational Social Science at the University of Helsinki, Spring 2015. (http://blogs.helsinki.fi/computationalsocialscience/).
Lecturer: Lauri Eloranta
Questions & Comments: https://twitter.com/laurieloranta
The Liber 2009 presentation repeated for a Dutch audience IN Dutch but with the english slides (just the first one is in Dutch :-)
Samenwerking Hogeschool bibliotheken SHB, 5 november 2009
In the last decade, several Scientific Knowledge Graphs (SKG) were released, representing scientific knowledge in a structured, interlinked, and semantically rich manner. But, what kind of information they describe? How they have been built? What can we do with them? In this lecture, I will first provide an overview of well-known SKGs, like Microsoft Academic Graph, Dimensions, and others. Then, I will present the Academia/Industry DynAmics (AIDA) Knowledge Graph, which describes 21M publications and 8M patents according to i) the research topics drawn from the Computer Science Ontology, ii) the type of the author's affiliations (e.g, academia, industry), and iii) 66 industrial sectors (e.g., automotive, financial, energy, electronics) from the Industrial Sectors Ontology (INDUSO). Finally, I will showcase a number of tools and approaches using such SKGs, supporting researchers, companies, and policymakers in making sense of research dynamics.
Keynote presentation delivered at ELAG 2013 in Gent, Belgium, on May 29 2013. Discusses Research Objects and the relationship to work my team has been involved in during the past couple of years: OAI-ORE, Open Annotation, Memento.
Lecture presented by Marian Ramos Eclevia at PAARL's Summer Conference on the theme "Library Analytics: Data-driven Library Management", held at Pearl Hotel, Manila on 20-22 April 2016
The Generative AI System Shock, and some thoughts on Collective Intelligence ...Simon Buckingham Shum
Keynote Address: Team-based Learning Collaborative Asia Pacific Community (TBLC-APC) Symposium (“Impact of emerging technologies on learning strategies”) 8-9 February 2024, Sydney https://tbl.sydney.edu.au
Slides from my contribution to the panel convened by Jeremy Roschelle at the International Society for the Learning Sciences: Engaging Learning Scientists in Policy Challenges: AI and the Future of Learning
Deliberative Democracy as a strategy for co-designing university ethics aro...Simon Buckingham Shum
Buckingham Shum, S. (2021). Deliberative Democracy as a strategy for co-designing university ethics around analytics and AI in education. AARE2021: Australian Association for Research in Education, 28 Nov. – 2 Dec. 2021
Deliberative Democracy as a Strategy for Co-designing University Ethics Around Analytics and AI in Education
Simon Buckingham Shum
Connected Intelligence Centre, University of Technology Sydney
Universities can see an increasing range of student and staff activity as it becomes digitally visible in their platform ecosystems. The fields of Learning Analytics and AI in Education have demonstrated the significant benefits that ethically responsible, pedagogically informed analysis of student activity data can bring, but such services are only possible because they are undeniably a form of “surveillance”, raising legitimate questions about how the use of such tools should be governed.
Our prior work has drawn on the rich concepts and methods developed in human-centred system design, and participatory/co-design, to design, deploy and validate practical tools that give a voice to non-technical stakeholders (e.g. educators; students) in shaping such systems. We are now expanding the depth and breadth of engagement that we seek, looking to the Deliberative Democracy movement for inspiration. This is a response to the crisis in confidence in how typical democratic systems engage citizens in decision making. A hallmark is the convening of a Deliberative Mini-Public (DMP) which may work at different scales (organisation; community; region; nation) and can take diverse forms (e.g. Citizens’ Juries; Citizens’ Assemblies; Consensus Conferences; Planning Cells; Deliberative Polls). DMP’s combination of stratified random sampling to ensure authentic representation, neutrally facilitated workshops, balanced expert briefings, and real support from organisational leaders, has been shown to cultivate high quality dialogue in sometimes highly conflicted settings, leading to a strong sense of ownership of the DMP's final outputs (e.g. policy recommendations).
This symposium contribution will describe how the DMP model is informing university-wide consultation on the ethical principles that should govern the use of analytics and AI around teaching and learning data.
March 2021 • 24/7 Instant Feedback on Writing: Integrating AcaWriter into yo...Simon Buckingham Shum
Slides accompanying the monthly UTS educator briefing https://cic.uts.edu.au/events/24-7-instant-feedback-on-writing-integrating-acawriter-into-your-teaching-18-march/
What difference could instant feedback on draft writing make to your students? Over the last 5 years the Connected Intelligence Centre has been developing and piloting an automated feedback tool for academic writing (AcaWriter), working closely with academics across several faculties. The research portal documents how educators and students engage with this kind of AI, and what we’ve learnt about integrating it into teaching and assessment.
In May, AcaWriter was launched to all students along with an information portal. Now we want to start upskilling academics, tutors and learning technologists, in a monthly session to give you the chance to learn about AcaWriter, and specifically, good practices for integrating it into your subject. CIC can support you, and we hope you may be interested in co-designing publishable research.
AcaWriter handles several different ‘genres’ of writing, including reflective writing (e.g. a Reflective Essay; Reflective Blogs/Journals on internships/work-placements) and analytical writing (e.g. Argumentative Essays; Research Abstracts & Introductions). This briefing will demo AcaWriter, and show it can be embedded in student activities. We hope this sparks ideas for your own teaching, which we can discuss in more detail.
ICQE20: Quantitative Ethnography Visualizations as Tools for ThinkingSimon Buckingham Shum
Slides for this keynote talk to the 2nd International Conference on Quantitative Ethnography
http://simon.buckinghamshum.net/2021/02/icqe2020-keynote-qe-viz-as-tools-for-thinking/
24/7 Instant Feedback on Writing: Integrating AcaWriter into your TeachingSimon Buckingham Shum
https://cic.uts.edu.au/events/24-7-instant-feedback-on-writing-integrating-acawriter-into-your-teaching-2-dec/
What difference could instant feedback on draft writing make to your students? Over the last 5 years the Connected Intelligence Centre has been developing and piloting an automated feedback tool for academic writing (AcaWriter), working closely with academics across several faculties. The research portal documents how educators and students engage with this kind of AI, and what we’ve learnt about integrating it into teaching and assessment.
In May, AcaWriter was launched to all students along with an information portal. Now we want to start upskilling academics, tutors and learning technologists, in a monthly session to give you the chance to learn about AcaWriter, and specifically, good practices for integrating it into your subject. CIC can support you, and we hope you may be interested in co-designing publishable research.
AcaWriter handles several different ‘genres’ of writing, including reflective writing (e.g. a Reflective Essay; Reflective Blogs/Journals on internships/work-placements) and analytical writing (e.g. Argumentative Essays; Research Abstracts & Introductions).
This briefing will demo AcaWriter, and show it can be embedded in student activities. We hope this sparks ideas for your own teaching, which we can discuss in more detail.
An introduction to argumentation for UTS:CIC PhD students (with some Learning Analytics examples, but potentially of wider interest to students/researchers)
Webinar: Learning Informatics Lab, University of Minnesota
Replay the talk: https://youtu.be/dcJZeDIMr2I
Learning Informatics
AI • Analytics • Accountability • Agency
Simon Buckingham Shum
Professor of Learning Informatics
Director, Connected Intelligence Centre
University of Technology Sydney
Abstract:
“Health Informatics”. “Urban Informatics”. “Social Informatics”. Informatics offers systemic ways of analyzing and designing the interaction of natural and artificial information processing systems. In the context of education, I will describe some Learning Informatics lenses and practices which we have developed for co-designing analytics and AI with educators and students. We have a particular focus on closing the feedback loop to equip learners with competencies to navigate a complex, uncertain future, such as critical thinking, professional reflection and teamwork. En route, we will touch on how we build educators’ trust in novel tools, our design philosophy of “embracing imperfection” in machine intelligence, and the ways that these infrastructures embody values. Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences spark productive reflection around as the UMN Learning Informatics Lab builds its program.
Biography:
Simon Buckingham Shum is Professor of Learning Informatics at the University of Technology Sydney, where he serves as inaugural director of the Connected Intelligence Centre. CIC is a transdisciplinary innovation centre, using analytics to provide new insights for university teams, with particular expertise in educational data science. Simon’s career-long fascination with software’s ability to make thinking visible has seen him active in communities including Computer-Supported Cooperative Work, Hypertext, Design Rationale, Scholarly Publishing, Semantic Web, Computational Argumentation, Educational Technology and Learning Analytics. The challenge of visualizing contested knowledge has produced several books: Visualizing Argumentation, Knowledge Cartography, and Constructing Knowledge Art. He has been active over the last decade in shaping the field of Learning Analytics, co-founding the Society for Learning Analytics Research, and catalyzing several strands: Social Learning Analytics, Discourse Analytics, Dispositional Analytics and Writing Analytics. http://Simon.BuckinghamShum.net
Despite AI’s potential for beneficial use, it creates important risks for Australians. AI, big data, and AI-informed decision making can cause exclusion, discrimination, skill loss, and economic impact; and can affect privacy, security of critical infrastructure and social well-being. What types of technology raise particular human rights concerns? Which human rights are particularly implicated?
Abstract: The emerging configuration of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards). The idea that we may be transitioning into significantly new ways of knowing – about learning and learners, teaching and teachers – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. What should we see when open the black box powering analytics? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? This isn’t just interesting to ponder academically: your school or university will be buying products that are being designed now. Or perhaps educational institutions should take control, building and sharing their own open source tools? How are universities accelerating the transition from analytics innovation to infrastructure? Speaking from the perspective of leading an institutional innovation centre in learning analytics, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.
Towards Collaboration Translucence: Giving Meaning to Multimodal Group DataSimon Buckingham Shum
Vanessa Echeverria, Roberto Martinez-Maldonado, and Simon Buck- ingham Shum.. 2019. Towards Collaboration Translucence: Giving Meaning to Multimodal Group Data. In Proceedings of ACM CHI conference (CHI’19). ACM, New York, NY, USA, Paper 39, 16 pages. https://doi.org/10.1145/3290605.3300269
Collocated, face-to-face teamwork remains a pervasive mode of working, which is hard to replicate online. Team members’ embodied, multimodal interaction with each other and artefacts has been studied by researchers, but due to its complexity, has remained opaque to automated analysis. However, the ready availability of sensors makes it increasingly affordable to instrument work spaces to study teamwork and groupwork. The possibility of visualising key aspects of a collaboration has huge potential for both academic and professional learning, but a frontline challenge is the enrichment of quantitative data streams with the qualitative insights needed to make sense of them. In response, we introduce the concept of collaboration translucence, an approach to make visible selected features of group activity. This is grounded both theoretically (in the physical, epistemic, social and affective dimensions of group activity), and contextually (using domain-specific concepts). We illustrate the approach from the automated analysis of healthcare simulations to train nurses, generating four visual proxies that fuse multimodal data into higher order patterns.
Panel held at LAK13: 3rd International Conference on Learning Analytics & Knowledge
http://simon.buckinghamshum.net/2013/03/lak13-edu-data-scientists-scarce-breed
Educational Data Scientists: A Scarce Breed
The Educational Data Scientist is currently a poorly understood, rarely sighted breed. Reports vary: some are known to be largely nocturnal, solitary creatures, while others have been reported to display highly social behaviour in broad daylight. What are their primary habits? How do they see the world? What ecological niches do they occupy now, and will predicted seismic shifts transform the landscape in their favour? What survival skills do they need when running into other breeds? Will their numbers grow, and how might they evolve? In this panel, the conference will hear and debate not only broad perspectives on the terrain, but will have been exposed to some real life specimens, and caught glimpses of the future ecosystem.
Keynote Address, International Conference of the Learning Sciences, London Festival of Learning
Transitioning Education’s Knowledge Infrastructure:
Shaping Design or Shouting from the Touchline?
Abstract: Bit by bit, a data-intensive substrate for education is being designed, plumbed in and switched on, powered by digital data from an expanding sensor array, data science and artificial intelligence. The configurations of educational institutions, technologies, scientific practices, ethics policies and companies can be usefully framed as the emergence of a new “knowledge infrastructure” (Paul Edwards).
The idea that we may be transitioning into significantly new ways of knowing – about learning and learners – is both exciting and daunting, because new knowledge infrastructures redefine roles and redistribute power, raising many important questions. For instance, assuming that we want to shape this infrastructure, how do we engage with the teams designing the platforms our schools and universities may be using next year? Who owns the data and algorithms, and in what senses can an analytics/AI-powered learning system be ‘accountable’? How do we empower all stakeholders to engage in the design process? Since digital infrastructure fades quickly into the background, how can researchers, educators and learners engage with it mindfully? If we want to work in “Pasteur’s Quadrant” (Donald Stokes), we must go beyond learning analytics that answer research questions, to deliver valued services to frontline educational users: but how are universities accelerating the analytics innovation to infrastructure transition?
Wrestling with these questions, the learning analytics community has evolved since its first international conference in 2011, at the intersection of learning and data science, and an explicit concern with those human factors, at many scales, that make or break the design and adoption of new educational tools. We are forging open source platforms, links with commercial providers, and collaborations with the diverse disciplines that feed into educational data science. In the context of ICLS, our dialogue with the learning sciences must continue to deepen to ensure that together we influence this knowledge infrastructure to advance the interests of all stakeholders, including learners, educators, researchers and leaders.
Speaking from the perspective of leading an institutional analytics innovation centre, I hope that our experiences designing code, competencies and culture for learning analytics sheds helpful light on these questions.
1. HypER Workshop: Hypotheses, Evidence and Relationships
11-12 May 2009, Elsevier, Amsterdam
The Hypermedia Discourse Project
Tools for Annotating, Visualizing & Navigating
Literature as Discourse Networks
Simon Buckingham Shum
Knowledge Media Institute
The Open University
Milton Keynes, UK
http://people.kmi.open.ac.uk/sbs
http://projects.kmi.open.ac.uk/hyperdiscourse
1
5. 1665 throws a long shadow
From: To…?
Chaomei Chen, 2006: Citation analysis
Le Journal des Sçavans
January 1665
Philosophical Transactions
of the Royal Society of
London
March 1665 Buckingham Shum et al, 2003: lineage analysis
Buckingham Shum, S. (2007). Digital Research Discourse? Computational Thinking Seminar Series, School of Informatics, 5
University of Edinburgh, 25 Apr. 2007. http://kmi.open.ac.uk/projects/hyperdiscourse/docs/Simon-Edin-CompThink.pdf
6. The question we used to ask in 2001 at
the start of the ScholOnto project
In 2010, will we still be publishing scientific results
primarily as prose papers, or will a complementary
infrastructure emerge that exploits the power of
the social, semantic web to model the literature as
a network of claims and arguments?
6
7. The question we used to ask in 2001 at
the start of the ScholOnto project
20xx?
In 2010, will we still be publishing scientific results
primarily as prose papers, or will a complementary
infrastructure emerge that exploits the power of
the social, semantic web to model the literature as
a network of claims and arguments?
7
8. Questions the next generation scientific
infrastructure should help answer
• “What is the evidence for this claim?”
• “Was this prediction accurate?”
• “What are the conceptual foundations for this idea?”
• “Who’s built on this idea? How?”
• “Who’s challenged this idea? Why? How?”
• “Are there distinctive perspectives on this problem?”
• “Are there inconsistencies within this school of thought?”
8
10. Researchers read meanings into texts that are not
there, and with which the author might disagree
so we will always require manual annotation tools
we need ways to make connections to connections
extremely complex connections may remain the province of human sensemaking
(e.g. is analogous to)
Good user interfaces will be needed
to view, edit and navigate HypERnets, whether manually or automatically constructed
Scientific discourse is a social process
we take huge care in our writing about how we position ourselves in relation to our
peers — will we trust unsupervised machines to extract and position our more
complex claims?
10
13. Compendium: customisable, collaborative,
hypermedia IBIS mapping
Buckingham Shum, S., Selvin, A., Sierhuis, M., Conklin, J., Haley, C. and Nuseibeh, B. (2006). Hypermedia Support for Argumentation-Based
Rationale: 15 Years on from gIBIS and QOC. In: Rationale Management in Software Engineering (Eds.) A.H. Dutoit, R. McCall, I. Mistrik, and B. Paech.
13
Springer-Verlag: Berlin
14. IBIS mapping of Iraq debate
Buckingham Shum, S., and A. Okada. 2008. Knowledge cartography for controversies: The Iraq debate. In Knowledge cartography: 14
Software tools and mapping techniques, ed. A. Okada, S. Buckingham Shum, and T. Sherborne, 249–66. London: Springer.
19. ScholOnto schema
Connecting freeform tags with naturalistic connections (“dialects”)
grounded in a formal set of relations (from semiotics and coherence relations)
Mancini, C. and Buckingham Shum, S.J. (2006). Modelling Discourse in Contested Domains: A Semiotic and Cognitive Framework. 19
International Journal of Human Computer Studies, 64, (11), pp.1154-1171. [PrePrint: http://oro.open.ac.uk/6441]
20. Scholarly discourse as CKS…
Beyond document citations… Making formal connections
These annotations are between ideas creates a
freeform summaries of an semantic citation network —>
idea, as one would also find novel literature navigation,
in researchers’ journals, querying and visualization
fieldnotes, lit. review notes “People try to maximise
or blog entries their rate of gaining
“Information scent
information” models”
Method
“Web User Flow by
applies
Theory “Information
Information Scent foraging
(WUFIS)” Claim theory”
?
Paper: “The Scent of a Site: A System for
Analyzing and Predicting Information Scent,
Usage, and Usability of a Web Site”
Paper: “Information
foraging” 20
25. Interaction design for literature
visualization: pilot study: paper-based literature modelling
S. Buckingham Shum, V. Uren, G. Li, B. Sereno, and C. Mancini. Computational Modelling of Naturalistic Argumentation in Research 25
Literatures: Representation and Interaction Design Issues. International Journal of Intelligent Systems, 22(1):17–47, 2006
26. Interaction design for lit. visualization
From paper prototype to semiformal mapping tool
The ClaiMapper tool
Starting from paper-based modelling,
move from literature sketches…
…to formal argument maps
Evaluated in: V. Uren, S. Buckingham Shum, G. Li, and M. Bachler. Sensemaking Tools for Understanding Research Literatures: Design, Implementation and User 26
Evaluation. International Journal of Human Computer Studies, 64(5):420–445, 2006
27. Interaction design for doc. annotation
Pilot study: paper-based annotation
Pilot study reported in: B. Sereno, S. Buckingham Shum, and E. Motta. (2005). ClaimSpotter: an Environment to Support 27
Sensemaking with Knowledge Triples. Proc. Int. Conf. Intelligent User Interfaces, pages 199–206, ACM
28. The ClaimSpotter annotation tool
Web 2.0-style tagging with optional community/system tag
recommendations
Sereno, B., Buckingham Shum, S. and Motta, E. (2007). Formalization, User Strategy and Interaction Design:
Users’ Behaviour with Discourse Tagging Semantics. Workshop on Social and Collaborative Construction 28
of
Structured Knowledge, 16th Int. World Wide Web Conference, Banff, Canada; 8-12 May 2007.
29. Lessons Learnt & Design Principles
Untrained users can do it: in their first hour they created
coherent claims. UI design validated to this degree.
—future work: longitudinal evaluation at scale
New users attend to what is highlighted for them (matching
tags; primary doct.), and generally don’t click down a level
—next version combines visualizations and document-centric features
Support incremental formalization
—cf. use of is-about as a placeholder link; provide an Other… category and try
to map automatically to the ontology
Users’ strategies vary — don’t assume a strong workflow
a paper-based pilot study can provide insights into this
Web 2.0 UI simplicity: good design needed to provide high
functionality, walk-up-and-use tools
—we overwhelmed some users with overlaid suggestions for tags
Sereno, B., Buckingham Shum, S. and Motta, E. (2007). Formalization, User Strategy and Interaction Design:
Users’ Behaviour with Discourse Tagging Semantics. Workshop on Social and Collaborative Construction 29
of
Structured Knowledge, 16th Int. World Wide Web Conference, Banff, Canada; 8-12 May 2007.
30. Cohere: from tag clouds to idea webs
Buckingham Shum, Simon (2008). Cohere: Towards Web 2.0 Argumentation. In: Proc. COMMA'08: 2nd International Conference on
Computational Models of Argument, 28-30 May 2008, Toulouse, France. IOS Press [PrePrint: http://oro.open.ac.uk/10421] 30
31. Cohere: embedding an Idea or Map in
another website (a blog post)
Buckingham Shum, Simon (2008). Cohere: Towards Web 2.0 Argumentation. In: Proc. COMMA'08: 2nd International Conference on
Computational Models of Argument, 28-30 May 2008, Toulouse, France. IOS Press [PrePrint: http://oro.open.ac.uk/10421] 31
32. Cohere: a mashup visualization merging
different connections around a common Idea
Buckingham Shum, Simon (2008). Cohere: Towards Web 2.0 Argumentation. In: Proc. COMMA'08: 2nd International Conference on
Computational Models of Argument, 28-30 May 2008, Toulouse, France. IOS Press [PrePrint: http://oro.open.ac.uk/10421] 32
33. Cohere: semantically filtering a focal
Idea by “contrasting” connections
Buckingham Shum, Simon (2008). Cohere: Towards Web 2.0 Argumentation. In: Proc. COMMA'08: 2nd International Conference on
Computational Models of Argument, 28-30 May 2008, Toulouse, France. IOS Press [PrePrint: http://oro.open.ac.uk/10421] 33
34. Cohere: semantically filtering a focal
Idea by “contrasting” connections
Buckingham Shum, Simon (2008). Cohere: Towards Web 2.0 Argumentation. In: Proc. COMMA'08: 2nd International Conference on
Computational Models of Argument, 28-30 May 2008, Toulouse, France. IOS Press [PrePrint: http://oro.open.ac.uk/10421] 34
35. “What papers contrast with this paper?”
1. Extract concepts for this document
2. Trace concepts on which they build
3. Trace concepts challenging this set
4. Show root documents
Evaluated in: V. Uren, S. Buckingham Shum, G. Li, and M. Bachler. Sensemaking Tools for Understanding Research Literatures: Design, Implementation and User 35
Evaluation. International Journal of Human Computer Studies, 64(5):420–445, 2006
36. “What is the lineage of this idea?”
Buckingham Shum, S.J., Uren, V., Li, G., Sereno, B. and
Mancini, C. (2007).Modelling Naturalistic Argumentation in
Research Literatures: Representation and Interaction Design
Issues. International Journal of Intelligent Systems, (Special
Issue on Computational Models of Natural Argument, Eds: C.
Reed and F. Grasso, 22, (1), pp.17-47. [PrePrint: http://
oro.open.ac.uk/6463] 36
37. Current projects: scientific collective
intelligence through discourse
OLnet: Open Learning Network to connect the open
educational resource movement’s discourse/
evidence base: http://olnet.org
ESSENCE: e-Science/Sensemaking/Climate Change
testing and integrating Web argumentation tools:
http://events.kmi.open.ac.uk/essence
SocialLearn: Web 3.0 social learning/sensemaking
platform with semantic discourse connections
(launches end of year)
37
39. Workshop Qs:
Corpus you are working on; community, type of
content (abstracts, full-text, book..)full text:
scholarly/scientific, blogs, newspapers, real time
discussions (and video of it), mission doctrine/
policy
Granularity of knowledge element you are
identifying
arbitrary: statements, single words, paragraphs
Relationships between knowledge elements you
have identified
IBIS: relational types + node types
ScholOnto: relations + roles...
Cohere 39
40. Workshop Qs:
Type of annotation: automatic, manual, combination
manual annotation
partial automatic highlighting of text based on
Simone Teufel's work on Argumentative Zoning
Size of corpus you have annotated so far
40 pages of blog debate
12 hours of video
distill 2 cm of policy docts into IBIS maps
several books in a literature
10-30 papers in a sample literature
30 articles on Iraq
5 days workshop discussions 40
41. Workshop Qs:
Data standards, outline of architecture of system
built (if relevant)
Compendium: XML DTD; SQL
Cohere API: RDF; XML; JSON
TopicSpaces: XML Topic Map; RDF; OWL
Visualisations
Compendium/ClaiMapper manual maps
ClaiMaker/Cohere/TopicSpaces generated maps
41
42. Workshop Qs:
User studies: yes, focusing on interaction design and
usage patterns in both field trials and lab studies
IUI 2005: evaluation of ClaimSpotter
IJHCS 2006: evaluation of ClaiMaker
WWW'07 CKC: evaluation of ClaimSpotter
IJRME 2008: evaluation of Compendium for mapping
climate change arguments
Space Ex. Conf 2005: NASA Ames field trials
DIAC 2008: evaluation of Compendium for mapping
planning discourse
HCI (under review): evaluation of Compendium
mapping for hostage recovery
42