The task-centric revolution. Weaving information into workflows Dagobert Soergel College of...

The task-centric revolution. Weaving information into

workflows

Dagobert Soergel

College of Information Studies

LACASIST 2009-02-05

Disclaimer

This talk pulls together many ideasmany old, some newmany other people’s, some minesome implemented here and there,some still awaiting implementation

The point is the total vision

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Computer work must be organized

not around applications

but around tasks

Functional or vertical integration

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Tasks are accomplished collaboratively

Collaboration

Cross-user or horizontal integration

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Ontology S.

Search

History / PIM

Creating work product

History-aware personal information store.  Planning

Ontology Support

Functional Integration

Collaboration

Search

Creating

History / PIM

Ontology S.

Creating

Search

Collaborator User in focus Collaborator

Sense-makingSense-makingSense making

Task oriented processing

Outline

• The digital library of the future ─ DELOS

• CLASS. A Collaborative Lesson-planning And Search System ─ Katy Lawley

• Sense-making ─ Pengyi Zhang

• History-aware personal information store ─ Anita Komlodi

• Relevance relationships ─ Xiaoli Huang

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The digital library of the future: A look at the DELOS

vision

DELOS Network of Excellence in Digital Libraries

Now the DELOS association

Funded by the EU, >50 DL research groups as members

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The digital library of the future

A broad system of interlinked information & services that

• Is person- and task-centric

• Provides rich seamlessly integrated functionality DL = information + tools to process information

• Supports process execution and workflowin business, government, and daily work

• Supports user-to user communication & collaboration

• Supports users as consumers and as contributorsSupports massive collaboration leveraging many small contributions to construct very large resources (e.g., Wikipedia, social tagging)

• Thus, supports new ways of intellectual work

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Issues in research and teaching

• User studies to• develop a truly user-centric view of computer-supported

functionality• learn how users could and would collaborate if properly supported

• Methods for deriving rich user profiles with minimal active user involvement, including discovery of users’ conceptual structures

• Interfaces that use a well-structured ontology (a faceted classification, an entity-relationship model) to • help users analyze a problem they face or a search topic • help users over time to assimilate the ontology structure

• Usability, effectiveness, and impact of such interfaces

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Issues in research and teaching• The core issue of ontologies / classifications /

tagging schemes• Capturing individual and shared understandings of the

concepts in a domain• Expressing these understandings in a structured way to

communicate a common understanding within a community• Supporting individual users in developing their own ontology• Collaborative development of ontologies • Automatic integration or harmonization of ontologies

• Integrated storage and search of documents and data in many formats and degrees of structure

• Annotation, communication, and collaboration functionality

• Seamless integration of multiple systems and tools

CLASS

A Collaborative Lesson-planning And Search System

Katy Lawley

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CLASS functionality The design

of a lesson planning system

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1 A knowledge organization infrastructure that fosters development of shared understandings and supports organization of materials.

2 Mediated access to digital libraries containing many types of materials with powerful, ontology-enhanced search

3 Intellectual property rights and access management

4 A collaborative template-based authoring system with annotation facility

5 Communication functionality

6 Integration with the teachers total work environment, including lesson plan evaluation

7 Interaction history. Customization and personalization, adaptive

8 An interface that provides easy access to all this functionality

9 Extensibility

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CLASS walk-through

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Figure 1. Opening screen for creating a lesson plan

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Figure 2. Lesson plan outline

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Figure 2. Lesson plan outline

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Figure 3. Selection of applicable standards

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Figure 4. Selection of applicable vocabulary words

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Figure 5. Query formulation for a theme in the lesson planning module

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Figure 6a. Browse the thesaurus

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Figure 6b. Descriptor found in detailed information for soldiers

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Figure 7. Display of results

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Figure 8a. Segment Display. Detailed information about the segment

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Figure 8a. Segment Display. Detailed information about the segment

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Figure 8b. Segment Assessment. The full form

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Figure 8b. Segment Assessment. The full form

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Figure 8c. Segment Display and Segment Assessment sharing the screen

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Figure 9. Interview segment added to lesson plan module

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1 Knowledge organization infrastructure

Foster development of shared understandings among users and support organization of materials. This includes

1.1 A lesson plan template that lays out the components of a lesson plan.

1.2 A query template to assist in formulating queries for learning objects and other materials.

1.3 A material appraisal form using the same structure as the query template, same structure as 1.2.

1.5 A hierarchy of educational standards from several jurisdictions.

1.4 A thesaurus / classification of topics in each accessed database domain that is useful for searching and for giving teachers content ideas.

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6 Integration with the teacher’s total work environment

6.1 Associate lesson plans with time slots for a given class.

6.2 Prepare requests for permission to use materials as needed.

6.3 Have material, such as quizzes, prepared in sufficient number or arrange for electronic administration.

6.4 Order equipment needed as specified in the lesson plan.

6.5 Notify the school library media specialist of assignments that require the use of the school library media center.

6.6 Have a function for recording grades and importing detailed standardized test scores (broken down by educational standard).

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Experience with CLASS

• Teachers were able to search the archive.

• Teachers used the materials appraisal form for their own purposes (not enough critical mass for collaboration).

• Teachers were able to develop well-structured lesson plans in a short time.

• Teachers indicated that they do not collaborate much at present but would be inclined to collaborate more if they had a tool like CLASS.

Sense-making

Pengyi Zhang

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Beyond Search

Beyond search: Users need support for the next step: making sense of and applying large quantities of information found.

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Outline

• What is sense-making?

• Examples

• Theoretical framework and comprehensive sense-making model

• Findings from a pilot study

• Conclusions on the design of sense-making systems

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Sense-making

Sense-making is

the process of creating an understanding of a problem or task

so that further actions may be taken in an informed manner (Stefik et al., 1999).

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Sense-making

• Sense-making is a pre-requisite for many other tasks such as decision making and problem solving;

• Sense-making involves making clear the interrelated concepts and their relationships in a problem or task space.

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Sense-making examples

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Sample Sense-making Scenario 1

Task T1: al-Bashir (Abridged Version)

The US wants to take action to towards a resolution of the Darfur conflict . Al-Bashir, the Sudanese president, is one of the key players in the area who is believed to have significant responsibility for continuous conflicts in the region. The administration needs to know as much as possible about al-Bashir in order to better negotiate with the involved parties and strategize its efforts. Your task is to produce a report that identifies information to assess the influence of al-Bashir and makes recommendations for policy decisions and diplomatic actions.

Requested information includes:• key figures, organizations, and countries who have been associated with

al-Bashir;• his rise to power; and • groups who have resisted him and the level of success in their resistance.

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Sample Sense-making Scenario 2

Task T2: Energy Security (Abridged Version)

At present, U.S. energy security depends on a range of countries across the globe, many of which could be characterized as politically unstable and afflicted with war, piracy and terrorism. Your task is to produce a report of the geopolitics of oil in the major suppliers of U.S., including Mexico, Saudi Arabia, Venezuela, Nigeria, Algeria, etc. Requested information includes:

•the political, economic, and military status of major oil suppliers;

•threats to U.S. oil supplies;

•transit chokepoints of world oil.41

Sample Think-aloud Protocol with Coding

Protocol (Energy Security Task) Loops Processes Conceptual Changes

Cognitive Mechanisms

Okay that was actually a very useful search. So let’s still take this query and look at Algeria, ‘cause obviously Algeria and Nigeria are very close…

Loop 4 Focused Search for data

Comparison

I understand some of the keywords in the article but I don’t understand what the article…

Sense-making Failure

Key item extraction

Okay this has to do with Algeria, southern Algeria. The minister of energy… OPEC meeting… so I am going to see what their connections are with OPEC.

Building structures

Key item extraction

… with all the violence in Nigeria, I was expecting to find the same types of political outrage in Algeria…

Instantiating structures

Comparison and analogy

and I’m not seeing any notice of that at all.

Updating of knowledge

Re-structuring

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Example Concepts and Relationships Concepts/Entity Relationships

in new informationRelationship in existing knowledge

Nigeria (entity)

Political violence (concept)

Nigeria <hasSituation> Political violence

Algeria Algeria <is very close to> Nigeria

Algeria <hasSituation> Political violence ?

Political stability Algeria <hasSituation> Political stability

Nigeria <hasSituation> Political violence

The minister of energy

OPEC meeting Nigeria <hasRelationshipTo> OPEC ?

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Sample Sense-making Scenario 3

Task

Write a newspaper article on the role of energy policy in the presidential campaign.

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Candidates positions on energy. Take 1

Candidates positions on energy. Take 2

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Theoretical Framework

Sense-making models– Generic sense-making models

– Sense-making models of intelligence analysis

– Conducting research as sense-making

– Organizational sense-making

– Individual vs. collaborative sensemaking

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Cognition– Types of conceptual changes

– Cognitive mechanisms

– Cognitive structures of knowledge

Learning– Schema theory

– Assimilation theory

– Generative Learning Theory

– Structural knowledge acquisition

Sense-making Elements

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Processes

Sensing

Making sense

Activities Mechanisms

Accretion

Tuning

Restructuring

Inductive, data-driven•Key item extraction•Comparison•Schema induction•Generalization

Deductive, structure-driven•Definition•Specification•Elimination•Explanation •Inference

Outcomes

Identification of gaps

Search

Building structure

Instantiating structure

Consuming instantiated structure

Other•Metaphor•Classification•Semantic fit•Socratic dialogues

Exploratory searchFocused search

For dataFor structure

An Iterative Sense-making Model

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Datagap

Structuregap

Search:exploratory / focused

OutcomesUpdated knowledge

Structure loop

Data loop

Identification of Gaps

Searchingfor data

Instantiating structure

Accretion: Instantiated structure

Tuning:Adapted structure

Re-structuring:New structure

Searching for structure

Building structure

Existing Knowledge

Structures and their

instantiations with data

The iterations proceed from exploratory to focused search and sense-making.

Task / Problem

Decision / Solution / Task completion

Findings – Search and Sense-making Loops

• The overall search and sense-making loops followed four stages:– Task analysis– Exploratory stage– Focused stage– Updates of knowledge representation.

• Reasons for starting a new loop of search and sense-making:– Success of previous sense-making– Failure of previous sense-making– New lead– Failure of search

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Conclusions on the designof sense-making systems

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Integration of tools

• Search tool

• Annotation / indexing tool

• Structure-building and visualization tools, manual and computer-assisted

• Writing tool

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Functions in the integrated environment

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Functions in the integrated environment

• Build structures: concept maps, templates, outlines

• Select in search results (within one document, a single document, or multiple documents), drag and drop on existing structure node or link (accretion), or drop in empty space to create a new node (structure modification)

• Have system find the node or link where a text passage (or image) should be attached

• Assist in extracting assertions – information extraction

• Find other sources for an existing assertion (automated accretion)

• Always preserve the source (as in MS OneNote) 54

Functions in the integrated environment

• Start a search from a structure node, using as query the node label or a query learned automatically from the documents already at the node

• Start a search from any selected text passage, for example, a text passage in the draft report

• KOS-supported search(KOS = Knowledge Organization System)

– Query expansion

– Browse KOS structure to clarify search topic, find search terms

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Functions in the integrated environment

• Switch between structure formats, for example, from concept map to outline or template

• Assist in creating structure, for example

– Insert relationships extracted from text into a concept map or construct a concept map from scratch from extracted relationships

– Clustering

– Find existing structures – for example, search for structures on the Web

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Functions in the integrated environment

• Create a draft report from a concept map

– Create outline and insert texts associated with each node

– Express relationships through text (text generation)

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Functions in the integrated environment

• Screen real estate is important – a very large screen or two monitors

• Easy input mode (so as not to distract from thinking)

– Voice input

– OCR pen input from print material

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History-aware personal or group information store

Anita Komlodi

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The total information store

• Store everything

• Connect everything

• Search and find everything

• Keep detailed provenance & use history

• History is both past and future ─ what was done and what is to be done

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Store everything

• Documents of all kinds

• Tasks

• Actions, events

• People and organizations

• Concepts, issues, frameworks (ontology)

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Search and find everything

• Flexible search − any item as starting point, any connection type.For example– Find all items connected to a task– Find all events and actions in a given time span– Find all items connected, directly or indirectly, to a

person• Present search results in different views, organize

into a meaningful structure

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Tools

• Search tool

• Gather tool

• Compare tool

• Connection tool, including annotation function

• Structure creation and display tool

• Scratch pad

• Ontology generation tool

– Personal ontology

– Group ontology

– Task ontology

• Writing / action tool63

Relevance relationships

Xiaoli Huang

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Information arranged by role in argument

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Relationship of info to task

Matching / allowing inference on a topic

. Matching topic / direct relevance

. Allowing inference on the topic / indirect relevance

 Context

 Comparison

 Cause and effect

 Goal

Method / Solution

Evaluation

Use these relationships when linking information to tasks

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Relevance relationships

Matching / allowing inference on a topic

. Matching topic / direct relevance

. . Matching topic at same level of detail

. . . Direct statement

. . . Reference

. . . Definition

. . . Restatement

. . . . paraphrase,

. . . . clarification

. . Summarization

. . . Abstraction

. . Elaboration

. . Interpretation

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Relevance relationships

Context. Background

. Scope, broader

. Framework

. Assumption or expectation

. Preparation

. Environmental setting

. . Physical environment

. . Social, political, cultural background

. By time sequence

. Condition

. . Enabling or hindering condition

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Relevance relationshipsComparison. By similar vs. different

. . Comparison, similar

. . Comparison, different

. By factor that is different

. . Difference in external factor

. . . Different time

. . . Different place

. . . Different type of situation

. . Difference in participant

. . Difference in act or experience

. . . Difference in act

. . . . Different purpose

. . . . Different method

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Some conclusions

• The system needs to think with the user– Adapt to the user’s way of thinking

but also– Help the user to structure a problem, organize information,

and prepare a plan for a task• Dividing users’ work into applications is a distraction• The gradual evolution of systems needs to make way for a

task-centric revolution• This presents challenges

– Task-oriented organization and intelligent processing of information

– Technical implementation

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Questions

dsoergel @ umd.edu

www.dsoergel.com

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Leftover slides

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Pilot Test

• Participants

• Data collection

• Data analysis

• Findings

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Participants

• 6 information science students

• Tasked with synthesizing data from a variety of sources, assessing the credibility of information, and evaluating claims based on supporting evidence

• Trained in using Rosetta - a multilingual, multimedia news retrieval system

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Data Collection

• 2-hour task sessions, 6 taskstotal of 17 sessions completed

• Participants were instructed to verbalize their thoughts as they work on the tasksThink-aloud protocols were transcribed

• Post-session interviews as supplemental data

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Data Analysis – Coding SchemeA Processes (from the model)

Search Sense-making

Exploratory search

Exploratory search for data

Exploratory search for structure

Focused search

Focused search for data

Focused search for structure

Gap identification

Data gap

Structural gap

Building structures

Using automatically extracted results

Extracting relationships manually

Instantiating structures

Updating knowledge

B Conceptual Changes (from the model)

Sense-making success Sense-making failure

Accretion Unable to fit data into structure

Tuning

Re-structuring Unable to build new structure

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Data Analysis – Coding SchemeC Cognitive Mechanisms (from the model)

Inductive mechanisms Deductive mechanisms Other

Key item extraction

Comparison

Similarity

Differentiation

Schema induction

Generalization

Definition

Specification

Explanation-based

Elimination

Inference

Analogy and metaphor

Classification

Socratic dialogues

Semantic fit

D Emerging Codes Added During Analysis

Reasons starting a new loop Resolution of conflicts

New lead Disregard conflicting evidence

Sense-making success Compromise

Sense-making failure Accept new evidence

Search failure Confusion78

Findings – Cognitive Mechanisms

• Our participants used a two-way approach:

data-driven (bottom-up) 80%

structure-driven (top-down)20%

• Key item extraction and comparison were used most often.

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Findings – Dealing with Conflicts

• Disregard – “… I wanted that article to say something else. I have to disregard it.”

• Compromise – “…it is not my understanding that this has anything to do with oil. But okay, this is a different take.”

• Acceptance – “…I thought these countries had similar serious problems. But [in fact, they] seem to be relatively stable and put a lot of effort into establishing their economy.”

• Confusion – “…Obviously I have no idea what this is about...”

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Findings – Role of Instantiated Structures

• Entities (represented as names) and key concepts (represented as keywords) were often the basis for relevance judgments.

• The relationships embedded in new information and between the new information and participants’ previous knowledge seemed to play an important role in structure building and data fitting.

• Both concepts and relationships seemed to be crucial for updating knowledge.

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Pengyi Zhang

pengyi@umd.edu

terpconnect.umd.edu/~pengyi/sensemaking/

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Extending Sense-Making Models with Ideas from Cognition and Learning TheoriesPengyi Zhang, Dagobert Soergel, Judith Klavans, and Douglas Oard pengyi@umd.edu, ASIST 2008, Sunday Oct. 26, Session on Sense Making. Online Proceedings, Paper 29, p. 1 – 23 [34 – 56]http://terpconnect.umd.edu/~pengyi/sensemaking/files/zhang_asist08_sensemaking_model.pdf

Conclusions

The model is a useful framework for understanding the users’ sensemaking process

•The empirical think-aloud data seem to be consistent with the iterative sense-making model

•Users used a combination of data-driven and structure-driven mechanisms

•Instantiated structure elements play an important role in sense-making

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Implications and Future Work

• Theoretical– Better understanding of sense-making processes

by extending the existing models with ideas from cognition and learning

– Further testing and refinement.

• Empirical– Better system design based on findings from user

studies.– Making design suggestions to support sense-

making, not just search.

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A Sample Sense-making Scenario

Task

An intelligence analyst is tasked to gather, analyze, and synthesize information related to Al-Bashir and, on that basis, make recommendations for action in the form of a report.

Background

The Darfur conflict is a crisis in the Darfur region of western Sudan. Al-Bashir, the Sudanese president, is one of the key players in the area who is believed to have significant responsibility for continuous conflicts in the region. As part of an effort to resolve these armed conflicts, the administration needs to know as much as possible about al-Bashir in order to better negotiate with the involved parties and strategize its efforts.

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A Sample Task Scenario

Task T1: al-Bashir (Abridged Version)

Omar Hasan Ahmad al-Bashir is a Sudanese military leader, dictator, and current president of Sudan. Your task is to produce a report identifying information to assess the influence of al-Bashir as a basis for policy decisions and diplomatic actions. Requested information includes:

• key figures, organizations, and countries who have been associated with al-Bashir;

• his rise to power; and • groups who have resisted him and the level of success

in their resistance.

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