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

Disclaimer This talk pulls together many ideas many old, some new many other peoples, some mine some implemented here and there, some still awaiting implementation The point is the total vision 2

Computer work must be organized not around applications but around tasks Functional or vertical integration 3

Tasks are accomplished collaboratively Collaboration Cross-user or horizontal integration 4

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 CollaboratorUser in focusCollaborator Sense-making Sense 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 6

7 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

8 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 workflow in business, government, and daily work Supports user-to user communication & collaboration Supports users as consumers and as contributors Supports massive collaboration leveraging many small contributions to construct very large resources (e.g., Wikipedia, social tagging) Thus, supports new ways of intellectual work

9 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

10 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 11

12 CLASS functionality The design of a lesson planning system

13 1A knowledge organization infrastructure that fosters development of shared understandings and supports organization of materials. 2Mediated access to digital libraries containing many types of materials with powerful, ontology-enhanced search 3Intellectual property rights and access management 4A collaborative template-based authoring system with annotation facility 5Communication functionality 6Integration with the teachers total work environment, including lesson plan evaluation 7Interaction history. Customization and personalization, adaptive 8An interface that provides easy access to all this functionality 9Extensibility

14 CLASS walk-through

15 Figure 1. Opening screen for creating a lesson plan

16 Figure 2. Lesson plan outline

17 Figure 2. Lesson plan outline

18 Figure 3. Selection of applicable standards

19 Figure 4. Selection of applicable vocabulary words

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

21 Figure 6a. Browse the thesaurus

22 Figure 6b. Descriptor found in detailed information for soldiers

23 Figure 7. Display of results

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

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

26 Figure 8b. Segment Assessment. The full form

27 Figure 8b. Segment Assessment. The full form

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

29 Figure 9. Interview segment added to lesson plan module

30 1 Knowledge organization infrastructure Foster development of shared understandings among users and support organization of materials. This includes 1.1A lesson plan template that lays out the components of a lesson plan. 1.2A query template to assist in formulating queries for learning objects and other materials. 1.3A material appraisal form using the same structure as the query template, same structure as A hierarchy of educational standards from several jurisdictions. 1.4A thesaurus / classification of topics in each accessed database domain that is useful for searching and for giving teachers content ideas.

31 6 Integration with the teachers total work environment 6.1Associate lesson plans with time slots for a given class. 6.2Prepare requests for permission to use materials as needed. 6.3Have material, such as quizzes, prepared in sufficient number or arrange for electronic administration. 6.4Order equipment needed as specified in the lesson plan. 6.5Notify the school library media specialist of assignments that require the use of the school library media center. 6.6Have a function for recording grades and importing detailed standardized test scores (broken down by educational standard).

32 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 33

Beyond Search Beyond search: Users need support for the next step: making sense of and applying large quantities of information found. 34

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 35

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). 36

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. 37

Sense-making examples 38

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. 39

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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)LoopsProcessesConceptual Changes Cognitive Mechanisms Okay that was actually a very useful search. So lets still take this query and look at Algeria, cause obviously Algeria and Nigeria are very close… Loop 4Focused Search for data Comparison I understand some of the keywords in the article but I dont 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 Im not seeing any notice of that at all. Updating of knowledge Re- structuring 42

Example Concepts and Relationships Concepts/EntityRelationships in new information Relationship in existing knowledge Nigeria (entity) Political violence (concept) Nigeria Political violence AlgeriaAlgeria Nigeria Algeria Political violence ? Political stabilityAlgeria Political stability Nigeria Political violence The minister of energy OPEC meetingNigeria OPEC ? … 43

Sample Sense-making Scenario 3 Task Write a newspaper article on the role of energy policy in the presidential campaign. 44

45 Candidates positions on energy. Take 1

Candidates positions on energy. Take 2 46

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 47 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 48 Processes Sensing Making sense ActivitiesMechanisms 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 search Focused search For data For structure

An Iterative Sense-making Model 49 Data gap Structure gap Search: exploratory / focused Outcomes Updated knowledge Structure loop Data loop Identification of Gaps Searching for 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 50

Conclusions on the design of sense-making systems 51

Integration of tools Search tool Annotation / indexing tool Structure-building and visualization tools, manual and computer-assisted Writing tool 52

Functions in the integrated environment 53

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 55

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 56

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) 57

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 58

History-aware personal or group information store Anita Komlodi 59

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 60

Store everything Documents of all kinds Tasks Actions, events People and organizations Concepts, issues, frameworks (ontology) 61

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 62

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 tool 63

Relevance relationships Xiaoli Huang 64

Information arranged by role in argument 65

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 66

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 67

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 68

Relevance relationships Comparison. 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 69

Some conclusions The system needs to think with the user –Adapt to the users 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 70

Questions umd.edu 71

Leftover slides 72

Pilot Test Participants Data collection Data analysis Findings 73

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 74

Data Collection 2-hour task sessions, 6 tasks total of 17 sessions completed Participants were instructed to verbalize their thoughts as they work on the tasks Think-aloud protocols were transcribed Post-session interviews as supplemental data 75

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Data Analysis – Coding Scheme A Processes (from the model) SearchSense-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 successSense-making failure Accretion Unable to fit data into structure Tuning Re-structuring Unable to build new structure 77

Data Analysis – Coding Scheme C Cognitive Mechanisms (from the model) Inductive mechanismsDeductive mechanismsOther 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 loopResolution of conflicts New leadDisregard conflicting evidence Sense-making successCompromise Sense-making failureAccept new evidence Search failureConfusion 78

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. 79

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... 80

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. 81

Pengyi Zhang terpconnect.umd.edu/~pengyi/sensemaking/ 82 Extending Sense-Making Models with Ideas from Cognition and Learning Theories Pengyi Zhang, Dagobert Soergel, Judith Klavans, and Douglas Oard ASIST 2008, Sunday Oct. 26, Session on Sense Making. Online Proceedings, Paper 29, p. 1 – 23 [34 – 56] semaking_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 83

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. 84

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. 85

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. 86

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