Modeling arguments in scientific papers ArgDiaP 2014 05-23

•Télécharger en tant que PPTX, PDF•

3 j'aime•1,451 vues

Paper: http://jodischneider.com/pubs/argdiap2014.pdf We present a motivating scenario for using argumentation within a scientific knowledge base. Our goal is to transform natural language papers, with the manual work of expert curators, into elaborated claim-argument networks. In ongoing work, we are focusing on creating Micropublications (Tim Clark, Paolo N. Ciccarese, Carole A. Goble http://arxiv.org/abs/1305.3506 ) through manual annotation. Currently we are engaging with pharmaceutical curators to envision the most appropriate way to record evidence for the next generation of the the Drug Interaction Knowledge Base. Presented at The 12th ArgDiaP Conference "From Real Data to Argument Mining" https://sites.google.com/site/argdiapen/12th-argdiap

Technologie Santé & Médecine

Jodi Schneider, Carol Collins, Lisa E Hines,
John R Horn, and Richard Boyce
12th Argumentation, Dialogue, Persuasion
conference (ArgDiaP 2014)
Warsaw, Poland
2014-05-25

Prescribers consult drug interaction
references…
Medscape EpocratesMicromedex 2.0

…which are maintained by expert
pharmacists
Medscape EpocratesMicromedex 2.0

Goal: Support evidence-based updates
to drug-interaction reference DBs
• Make sense of the EVIDENCE
– New clinical trials
– Adverse drug event reports
– Drug product labels
– Updates to regulatory
information (U.S. FDA,…)
– …
• Significant discrepancies between different
drug-interaction reference DBs
http://jama.jamanetwork.com/article.aspx?articleid=183454

Drug Interaction Knowledge Base
(DIKB) - Boyce 2007-2009
– Hand-constructed knowledge base
– Safety issues when 2 drugs are taken together
– Focus is on EVIDENCE

DIKB supports queries about
assertions & evidence:
• Get all assertions that are supported by a
U.S. FDA regulatory guidance statement
• Are the evidence use assumptions are
concordant, unique, and non-ambiguous?
• Which assertions are supported/refuted by
just one type of evidence?

Limitations of DIKB v1.2
• Minimal argumentation model
– swanco:citesAsSupportingEvidence
– swanco:citesAsRefutingEvidence
• Cannot recover the source text
– Document-level citation
– Quote & section citation preferrable
• Level of detail
– Want more detail on data, methods, materials

Record deeper relationships between
assertions and evidence
• Assertions
– “there (is/is not) an interaction between A & B”
– “Enzyme E reduces clearance of drug D by 25%”
• Evidence
– Scientific literature
– Drug product labeling
– U.S. FDA guidance documents

Micropublication: Claim + Support
(e.g. Attribution)
Micropublications: a Semantic Model for Claims, Evidence, Arguments and Annotations in Biomedical Communications
Tim Clark, Paolo N. Ciccarese, Carole A. Goble
http://arxiv.org/abs/1305.3506

Constructs claim-argument network
across scientific papers
Micropublications: a Semantic Model for Claims, Evidence, Arguments and Annotations in Biomedical Communications
Tim Clark, Paolo N. Ciccarese, Carole A. Goble
http://arxiv.org/abs/1305.3506

Model Data,
Methods,
Materials,
References
Micropublications: a Semantic Model for
Claims, Evidence, Arguments and
Annotations in Biomedical
Communications
Tim Clark, Paolo N. Ciccarese, Carole A.
Goble
http://arxiv.org/abs/1305.3506

Micropublications Ontology
Micropublications: a Semantic Model for Claims, Evidence, Arguments and Annotations in Biomedical Communications
Tim Clark, Paolo N. Ciccarese, Carole A. Goble
http://arxiv.org/abs/1305.3506

What does this do for the DIKB
• Network of claims with supports & challenges
• Can record materials, methods, data
• Quotes can be naturally linked into the graph
• We can query for every mp:Claim that has
no support.
– An assumption (mp:Claim)
– Can have its own support graph, specified once
– We can query the support graph

"escitalopram does not
inhibit CYP2D6"
Support graph Challenge graph

Methods
Methods section of challenge graph

Direct Annotation with Domeo
http://swan.mindinformatics.org/ Paolo N Ciccarese

From individual documents to a
searchable claim-argument network
• "Pay as you go" annotation of source
documents with Domeo & Micropublications
• Generates claim-argument network
– Supports & challenges
– Materials, methods, data
– Quotes linked into the graph
– … within & across documents
• Query support

Contenu connexe

Plus de jodischneider

Keynote talk 2020-08-01 for the JCDL Workshop on Conceptual Models: https://sig-cm.github.io/news/JCDL-2020-CFP/ Discussion points: * Methods are a key part of the Knowledge Organizing Structure for Evidence-Based Medicine. * Methods relate to how we GENERATE evidence. * Different methods generate evidence of different kinds and strength. * I believe Methods can be useful in mining claims and arguments from papers: methods AUTHORIZE claims. * More specialized hierarchies of evidence can be found in medicine * Various groups are complicating the “evidence pyramid” hierarchy of evidence.

Methods Pyramids as an Organizing Structure for Evidence-Based Medicine--SIGC...

jodischneider

Annotation examples. This is an overview of some of the software I have used for annotation (and a few extra features some of this software has.) This was presented in the SwissUniversities Doctoral Programme, Language & Cognition, in the Module: Linguistic and corpus perspectives on argumentative discourse. Screenshots are given of GATE, UAM Corpus Tool, Excel, BRAT, EPPI Reviewer, and a custom tool. In most cases there are references to one of my papers for further details. I briefly describe a typical annotation process: Find text of interest Find phenomena of interest Draft an annotation manual Iteratively test annotation & revise manual Find questionable annotations, check disagreements. Revise the manual. Iterate. Annotate

Annotation examples--Fribourg--2019-09-03

jodischneider

An introduction to argumentation mining for PhD students. This was presented in the SwissUniversities Doctoral Programme, Language & Cognition, in the Module: Linguistic and corpus perspectives on argumentative discourse. The presentation largely follows Chapters 1-4 and Chapter 10 of my book, Argumentation Mining, co-authored with Manfred Stede in the Synthesis Lectures on Human Language Technologies from Morgan & Claypool: https://doi.org/10.2200/S00883ED1V01Y201811HLT040 Topics: My book w/computational linguist Manfred Stede: Argumentation Mining What is argumentation? Argumentation mining: a first look Argumentative language Challenges for argumentation mining Argumentation structures Corpus annotation Why study argumentation mining?

Argumentation mining--an introduction for linguists--Fribourg--2019-09-02

jodischneider

Talk at the 3rd European Conference on Argumentation ABSTRACT: Specialized fields may at any time invent new inference rules—that is, new warrants—to improve on their stock of resources for drawing and defending conclusions. Yet disagreement over the acceptability of an invented warrant can always be re-opened. Randomized Clinical Trial is widely regarded as the gold standard for making inferences about causal relationships between medical treatments and patient outcomes. Once controversial, RCT achieved broad acceptance within the field as a result of warrant-establishing arguments circulating in the medical literature starting in the 1950s. And RCT has accumulated a very impressive track record of generating new conclusions that withstand critical scrutiny. Here we look at two emerging innovations whose purpose is to support reasoning about health, offering ways to generate different classes of conclusions. These innovations could be seen as complementary to RCTs, but for both there are also hints of challenge to the enormous prestige of RCTs. We see this most particularly in the gap that has developed between the RCT-generated fact base and the decisions doctors and health policy officials have to make about treatments for patients. We’ve mentioned before that specialized inference methods that become stabilized within an expert community can meet unexpected challenges when they become components of reasoning by other communities. The two innovations considered here each allow us to explore the tensions that arise from the contrasting perspectives of scientists, clinicians, and patients.

Beyond Randomized Clinical Trials: emerging innovations in reasoning about he...

jodischneider

Publishers are caretakers of science. Part of that work is maintaining the integrity of scientific literature. Science builds directly upon past work, so we need to be sure that we are building upon a solid foundation and not faulty research. Publishers need to take an active role in monitoring and tracking faulty, retracted research and its influence. I'm asking publishers to (1) clearly mark retracted papers; (2) alert authors who have already cited a retracted paper; and (3) before publishing an article, check its bibliography for retracted papers. Retracted papers should be clearly marked everywhere they appear, but today that is not the case. Publishers can also use the CrossRef CrossMark service, which lets readers check for article updates (such as retraction) from a little red ribbon at the top of an article. Checking for citations to retracted articles, and limiting future citations, can help science self-correct by shoring up its foundations.

Problem-citations--CrossrefLive18--2018-11-13

jodischneider

The structure of citation networks provides evidence about how scientific information is diffused. Problematic citation patterns include the selective citation of positive findings, citation bias, as well as the continued citation of retracted literature (i.e. literature formally withdrawn due to error, fraud, or ethical problems). For instance, there is some evidence that positive results tend to receive more citations. The public domain licensing of the Open Citations Corpus makes it possible, in principle, to estimate the likelihood that any network of research papers suffers from problematic citation. To-date, problematic citation been documented ad-hoc, in several striking studies. In Alzheimer's disease research, biased citation, ignoring critical findings, was used to support successful U.S. NIH grant proposals (Greenberg 2009). Mistranslation of obesity research has been used to justify exertion game research (Marshall & Linehan 2017). Citation of fraudulent research about Chronic Obstructive Pulmonary Disease continued after its retraction (Fulton et al. 2015). The data resulting from such studies is of great use to my lab in replicating and determining how to generalize the detection of problematic citation patterns. Previously, the detection of problematic citation patterns has been a side effect of astute researchers, noticing suspicious findings while conducting systematic literature reviews. This talk will describe work-in-progress in my lab detecting problematic citation patterns using natural language processing, combined with network analysis on the Open Citations Corpus.

Problematic citations--Workshop-on-Open-Citations--2018-09-03

jodischneider

Presentation: Jodi Schneider and Novejot Sandhu, “Modeling Alzheimer’s Disease Reseach Claims, Evidence, and Arguments from a Biology Research Paper.” 9th International Conference on Argumentation, International Society for the Society of Argumentation, Amsterdam, Netherlands, July 5, 2018 Abstract: Argument visualization may help make research papers easier to understand, which could both speed quality assessment within a discipline and help build interdisciplinary knowledge networks. This paper presents a case study of the arguments in a single high-profile paper on Alzheimer's disease research. Within this one paper, we analyze and hand-annotate the main claim, which is supported by 4 subclaims, in turn supported by data, methods, and materials. We also investigate how the paper imports and uses knowledge claims from other research papers. We create a specialized argument-based knowledge representation called a micropublication. In future work, we will investigate automatic argumentation mining for experimental biology research papers. Our long-term vision is to create literature-scale claim-argument networks that help more quickly use new knowledge about human health.

Modeling Alzheimer’s Disease research claims, evidence, and arguments from a ...

jodischneider

Presentation: Jodi Schneider and Sally Jackson, “Innovations in Reasoning About Health: The Case of the Randomized Clinical Trial.” 9th International Conference on Argumentation, International Society for the Society of Argumentation, Amsterdam, Netherlands, July 5, 2018 Abstract: Field-dependence in argumentation comes about through forms of inference invented by specialized fields. In recent work we introduced the concept of a "warranting device": (1) an inference license (2) invented for a specialized argumentative purpose and (3) backed by institutional, procedural, and material assurances of the dependability of conclusions generated by the device. Once established, fields employ such devices across many situations without further defense, even as the devices develop in response to newly-noticed problems. Many new warranting devices have appeared over the past century to solve problems in reasoning about health and medicine, replacing and obsolescing earlier forms of medical reasoning. One such device is the Randomized Controlled Trial. This case study traces its historical evolution and discusses some current movements toward competing device types.

Innovations in reasoning about health: the case of the Randomized Clinical Tr...

jodischneider

Viewing universities as landscapes of scholarship, VIVO keynote, 2017-08-04

jodischneider

European Conference on Argumentation talk Jodi Schneider, Graciela Rosemblat, Shabnam Tafreshi and Halil Kilicoglu “Rhetorical moves and audience considerations in the discussion sections of Randomized Controlled Trials of health interventions” [Conference Panel Presentation], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23 1 of 3 talks in Jodi Schneider and Sally Jackson, organizers, “Innovations in Reasoning and Arguing about Health ”[Conference Panel], 2nd European Conference on Argumentation: Argumentation and Inference, Fribourg, Switzerland, June 20-23.

Rhetorical moves and audience considerations in the discussion sections of ra...

jodischneider

Citation practices and the construction of scientific fact--ECA-facts-preconf...

jodischneider

What WikiCite can learn from biomedical citation networks--Wikicite2017--2017...

jodischneider

Medication safety as a use case for argumentation mining We present a use case for argumentation mining, from biomedical informatics, specifically from medication safety. Tens of thousands of preventable medical errors occur in the U.S. each year, due to limitations in the information available to clinicians. Current knowledge sources about potential drug-drug interactions (PDDIs) often fail to provide essential management recommendations and differ significantly in their coverage, accuracy, and agreement. The Drug Interaction Knowledge Base Project (Boyce, 2006-present; dikb.org) is addressing this problem. Our current work is using knowledge representations and human annotation in order to represent clinically-relevant claims and evidence. Our data model incorporates an existing argumentation-focused ontology, the Micropublications Ontology. Further, to describe more specific information, such as the types of studies that allow inference of a particular type of claim, we are developing an evidence-focused ontology called DIDEO--Drug-drug Interaction and Drug-drug Interaction Evidence Ontology. On the curation side, we will describe how our research team is hand-extracting knowledge claims and evidence from the primary research literature, case reports, and FDA-approved drug labels for 65 drugs. We think that medication safety could be an important domain for applying automatic argumentation mining in the future. In discussions at Dagstuhl, we would like to investigate how current argumentation mining techniques might be used to scale up this work. We can also discuss possible implications for representing evidence from other biomedical domains. Talk for Dagstuhl Seminar 16161: Natural Language Argumentation: Mining, Processing, and Reasoning over Textual Arguments http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16161

Medication safety as a use case for argumentation mining, Dagstuhl seminar 16...

jodischneider

Acquiring and representing drug-drug interaction knowledge and evidence, Litm...

jodischneider

Acquiring and representing drug-drug interaction knowledge and evidence, TRIA...

jodischneider

A talk for the Moore Institute for Humanities - People and documents are of enduring interest. Documents may be generated by individuals, collective groups, and administrations, on any number of topics. We are particularly interested in the relationships between people and documents. The most important relationships are creation (authors, illustrators, translators, ...), usage (e.g. association copies), and topic-of (e.g. people may be the subjects of biographies). In this lecture, we will talk about several approaches for modeling, or representing, people and documents. We pay particular attention to computer-based approaches to organization, and to organizing information for websites. We will talk briefly about TEI and XML, and the focus on my area of research expertise: modeling "linked data", a widely adopted approach for interlinking data. Adopted by the UK and US governments and search engines such as Google and Yahoo!, linked data has also been widely used in the digital humanities and by libraries, archives, and museums. It consists in naming objects of interest (be they authors, documents, or whatnot) and using standard data formats to enable interlinking.

Persons, documents, models: organising and structuring information for the We...

jodischneider

Synthesizing knowledge from disagreement -- Manchester -- 2015-05-06

jodischneider

Synthesizing knowledge from disagreement -cwi-2015-04-23

jodischneider

Invited talk 2015-01-19 at EURCOM. Two themes: How do communities curate knowledge? and How can information technology help? Q: How do communities curate knowledge? A: Communities curate knowledge by discussing evidence and applying community standards to it. In Wikipedia, 4 questions are used to evaluate borderline articles: Notability – Is the topic appropriate for our encyclopedia? Sources – Is the article well-sourced? Maintenance – Can we maintain this article? Bias – Is the article neutral? POV appropriately weighted? Q: How can information technology help? A: Information technology can organize evidence based on the criteria communities use. In Wikipedia, we developed an alternate interface for deletion discussions.

How communities curate knowledge & how ontologists can help -Eurecom--2015-01-19

jodischneider

ERCIM Marie Curie - Alain Bensoussan Fellowship info - ERCIM 25 years Pisa

jodischneider

Plus de jodischneider (20)