The KiWi Vision

ReasoningWeb Summer School 2008
Venice, September 2008

Dr. Sebastian Schaffert
Salzburg Research Forschungsgesellschaft

sebastian.schaffert@salzburgresearch.at
http://www.kiwi-project.eu http://planet.kiwi-project.eu

KiWi Vision

ReasoningWeb Summer School, Venice, © 2008,Sebastian Schaffert, Salzburg Research

KiWi Vision

Knowledge Management

| many different kinds of rich content
(text, images, audio, video, software, processes, …)
| user and domain specific workflows and processes
| sharing of content and collaboration of users


KiWi Vision

Knowledge Management (traditional)

| „knowledge acquisition systems“
| form-based, predefined processes, part of quality
management, „make people replaceable“
| people are aligned with technology and organisation


KiWi Vision

Knowledge Management (KiWi Way)

| instead: technology and organisation should be alignable
with people!
| KiWi: Semantic CMS the Wiki-Way


“share, give it away, make it easy, because the more
people know your idea the more powerful it becomes”

– Garr Reynolds, Presentation Zen


Online Communities

… are successful in
sharing even now

… there is no reason
why this shouldn‘t
work in the enterprise


KiWi Vision

Knowledge Management (Wikis)

| Wikis are...
| simple to use (low technological barrier)
| flexible: from a short notice over documentation to collaborative
authoring of documents
| do not impose a predefined workflow (no dictate of the system)
| adjust to the necessities of users


Wikis:
like a piece of paper!
… you can write on it

… you can
connect things

… you can draw on it


Wikis: like a piece of paper

| workflows only by “social convention”
| there are rules, but it is possible to deviate from them if
necessary (new situations, better solutions, …)


KiWi Vision

Knowledge Management (Wikis)

| but: Wikis are rather like an empty piece of paper
| well suited for creative and/or well-known tasks
| no support whatsoever for users
| nobody would fill his tax return on an empty piece of paper!
| forms and workflows have (originally) been developed as
support!
| with growing amount of content it becomes also
increasingly difficult to find the necessary information


KiWi Vision

Knowledge Management
+ Wiki-Philosophy
+ Semantic Web
= KiWi

| machine readable linking of content
| adaption of presentation and input
| to personal preferences
| to user and content context
| to different kinds of content
| examples:
| kinds of content: meeting minutes, resource plans, persons,
tasks, reports, ideas, ...
| presentation/input: meeting minute editor, gantt diagram, user
profile, report template, ...


KiWi Use Cases: Requirements
KiWi Use Cases


Software Knowledge Management
KiWi Use Cases

Sun Microsystems (Prague)
| knowledge in and about software systems involves many different
kinds of (rich) content:
| source code
| documentation
| minutes of group meetings
| bug reports,
| ...
| development of Sun’s Netbeans IDE is developed by a large
community with a team of 150 core developers employed by Sun
Prague
| knowledge gathered and developed during the software
development process is distributed over many different kinds of
systems (wikis, bug trackers, blogs, discussion forums, mailing
lists)


KiWi Use Cases

Sun Microsystems (Prague)

| goal: to develop a platform for managing and sharing
knowledge in virtual software development communities


KiWi Use Cases

Pilot Roles

| designer: responsible for requirements and use
cases, usability (user researcher), as well as
system behaviour (interaction designer) and user
interface (visual designer)

| developer: responsible for actual
implementation of planned features defined by
planners and further specified by designers


KiWi Use Cases

Designer – KM Tasks

| write UI specification and enter related bugs to the bug
repository

| understand high-level relationships between various
documents

| relate document or its part to an existing concept

| define non-existing entity while writing a text document


Project Knowledge Management
KiWi Use Cases

Logica Denmark

| Logica DK: provider of IT solutions and services to public
and private sector
| currently undergoes CMMI ML2 certification for their project
management processes as a requirement by customers


KiWi Use Cases

CMMI ML 2

| CMMI ML2:
“Capability Maturity Model Integration Maturity Level 2”

means:

| standardising work processes among projects
| documenting and managing work processes
| following certain workflows
| sharing process knowledge between projects


KiWi Use Cases

Logica Denmark

| goal: to develop a platform documenting and supporting
processes in project management conforming to CMMI ML 2
and exchange relevant knowledge


KiWi Use Cases

Roles

| process engineer: works on process for the company in
general and teams in particular; all process changes have to
be acknowledged by process engineer

| project manager: mediator between customer and the
team; discusses project, implementation, and goals with
both, customer and team members

| team member: software developers, architects, testers,
etc.; experienced in their respective tools and knows his
peers


KiWi Use Cases

Required KiWi Features

| collaborative environment where knowledge and experience
can be easily written both at the project and process level

| support for semantic tagging and annotation with semantic
meta-data

| support for semantic search over process descriptions,
project documentation, and reported experience

| support for identification of well-defined inconsistencies
between selected work products


KiWi Use Cases

KiWi Photo Stories


KiWi Concepts
KiWi Core Concepts


KiWi Core Concepts

Core Usage Concepts

| content items: single units of information, associated with
a URI (for machine-readable data) and some content
(human-readable)

| tags: textual annotations of content items; can be lifted to
semantic concepts, thus embedding content items in a
knowledge base

| triples: relate two content items; stored in RDF; predicates
may be further specified in ontologies


KiWi Core Concepts

Core Usage Concepts

| users: play an important role for personalisation, social
networking, and reputation; have a unique URI and thus a
content item

| roles: in every social interaction, users take specific roles
which they can explicitly switch (e.g. “at home”, “at work”)

| revisions: represent logical changes to the system and are
explicitly committed by the user (by clicking “save”); a
revision may contain of several atomic updates


KiWi Advanced Concepts

Advanced Usage Concepts

| rules: define rule-based deductive knowledge; rules can
work on content items, tags, and triples and can add
additional triples as well as influence the layout and
presentation of the content

| structured tags: allow to define more complex tagging
structures, e.g. hotel(3stars), author(sebastian), etc.


KiWi User Interface
KiWi User Interface


Presenting Content Items

| principle: content items are presented in a way that is as
suitable as possible for the user

| generic presentation:
| wiki page (known from other wikis)
| incoming/outgoing references (known from IkeWiki)
| tags
| triple context (graph visualisation)
| specific presentation:
| custom layouts and widgets for certain kinds of content
items that display the content in a manner appropriate for the
domain and personal preferences


Widgets & Layouts

| Task 1: allow the adaptation of the presentation and
editing of content to current context and personal
preferences
Layouts
| Task 2: allow (advanced) users to define custom
components („widgets“) for presenting and editing content
(and meta-data!)
Widgets

| Requirement:
| allow rules and reasoning to influence the layout using user-
defined rules


Custom Layouts & Widgets
context specific content layout


Custom Layouts & Widgets
context specific visualisations


Editing Content Items

| principle: allow everyone to enter data as she wants
| simple editor for simple tasks
| advanced access to the system if so desired
| guide users from very simple but restricted to full access


Annotations Editor

| generic way of annotating content items
| the annotations editor is a native RDF editor that builds
directly on top of the RDF model!
| allows to associate wiki pages (i.e. content items) and links
between pages with types

| generic and flexible, but (as experience with IkeWiki shows)
too complicated for most users!


Annotations Editor


Semantic Forms

| application and context specific forms for entering the
relevant data, supporting the user e.g. by drop down
selections, range and type restrictions, …
| selected based on personalisation/context adaptation (e.g.:
when displaying a user profile, provide a user profile form)
| may allow to change both, content and meta-data
(depending on processing instructions)
| defined by (advanced) users using widgets
(actually: a special kind of widget that allows editing data)


Semantic Forms (Slide: Denny Vrandecic / ACTIVE project)


Semantic Forms

| arguably the most simple and user-centred way of editing
content and meta-data
| requires a priori definition of forms
| mostly specific to the application domain


Browsing Content Items

| two ways of finding relevant content
| search (active)
| navigation (passive)
| search in KiWi:
| always start with a keyword or keywords, used for full-text as
well as semantic search (over RDF, Tags)
| refine search results via facetted browsing, adding additional
concepts, relations, tags
| navigation in KiWi:
| incoming/outgoing references for a content item
| navigation ontology that is rendered appropriately


KiWi Technology
KiWi Technology


Technology Stack and Frameworks (Server)

| Java EE 5: provides core technology for persistence and
data management
| JBoss AS: Java EE web and application server, supporting
clustering, persistence, …
| Sesame 2: RDF triple store supporting easy extension with
custom reasoners
| JBoss Seam: web application framework building on and
extending Java EE; provides core web application
functionalities (user management, MVC separation &
inversion of control, data access, …), Lucene integration,
allows easy extension with e.g. Web Services
| TestNG: advanced unit testing environment for web
applications


Technology Stack and Frameworks (Client)

| JSF/RichFaces: provides core user interface concepts for
web applications and supports interaction with server using
AJAX
| XHTML: for rendering presentation in the browser and for
storing conent item content
| TinyMCE: as extensible browser-based WYSIWYG editor for
XHTML


Model: Persistence & Data Model

| object oriented model for accessing frequently used core
concepts of the system (“content item”, “user”, “role”, …)
| objects mapped partly to a relational database (using JPA/
Hibernate) and partly to the triple store (Sesame);
developers don’t need to care


Controller: Seam, EJB, and the KiWi API
Controller Services (1)

| unified access to objects of the data model, regardless
how they are persisted
| direct access to the XML and RDF data of content items
and meta-data
| querying facilities for textual and meta-data querying,
later extended by the KiWi reasoning and querying
language
| web service endpoint for accessing the system from
external applications


Controller: Seam, EJB, and the KiWi API
Controller Services (2)

| widget execution environment: an environment that
provides relevant functionalities to widgets, e.g. the current
context, the data model, etc.
| personalisation: management of the user model and
automatic selection of appropriate layouts and widgets
| information extraction: allows automatic analysis of
textual content for tag and annotation recommendation
| reasoning: allows the management and execution of
predefined or user-defined rules that work on both, the
content and the meta-data


View: the KiWi user interface

| browser-based, i.e. XHTML + JavaScript
| implemented as a specific configuration of layouts and
widgets for different applications
| widgets may be implemented in different languages, e.g.
JSF, Groovy, XHTML, JavaScript, …


KiWi Research
KiWi Research


KiWi Enabling Technologies

| Reasoning: a rule-based language capable of working with
both data (content items) and meta-data (triples, tags)

| Reason Maintenance: tracking justifications for
derivations for presentation to the user and for efficiency

| Information Extraction: supporting the user by
suggesting appropriate annotations based on human-
readable content

| Personalisation: adapting the presentation to the user
based on context and user model


Reasoning
Reasoning

Motivation

what should reasoning in such a setting look like?


Reasoning
Reasoning

Motivation

| … consistency checking?
| … instance checking?

(this is what the current Semantic Web Reasoning offers)


Reasoning
Reasoning

Motivation

or rather …
| … representing rule knowledge (processes, workflows)
| when something is tagged “bug” it should also be tagged with
“todo”
| … personalising user experience?
| list of all pages tagged “todo” that are relevant to me
| … defining reactive behaviour?
| when someone clicks on this button, add that tag
| … defining policies?
| Piero can give you the best examples

(this is what rule-based reasoning could offer)

Rule-based Reasoning
Reasoning

Motivation

| semantic wikis require different support for reasoning and
querying
| rule-based instead of DL-based
| deviations instead of consistency checking
| user-defined rules, e.g. for personalisation
| current systems (like Jena and Sesame) do not offer these
kinds of reasoning and are slow due to complex DL
reasoning tasks


Reason Maintenance
Reasoning

Motivation

| if reasoning is supposed to be used for personalisation and
recommendations …
| users must be able to get explanations and justifications for
what is recommended and personalised
| users must be able to manually override / disable certain rules
| reason maintenance is the science about how to address
these two issues, e.g. by tracking the justifications for each
derived fact
| it also helps to increase efficiency, particularly for updates,
which is essential for wiki-like systems
| it is also useful for versioning


Reasoning & Reason Maintenance
Reasoning

Goals

| to develop a rule-based language that
| can be used by users of the wiki to query and to specify
derivation (and possible action) rules
| capable to query content and knowledge base in a unified
fashion
| with a simple and intuitive way to specify such queries and
rules
| to develop a reason maintenance component for this
language that
| gives users a way to understand why certain
derivations exist,
| allows versioning of updates to the knowledge
base
| allows easy updates


Information extraction
Information Extraction

Motivation

| current semantic wikis provide no support for annotating content
(besides providing an easy-to-use interface to enter annotations)
| content of wiki pages usually already contains much of the
knowledge, albeit in natural language text
| annotation is for many users a very daunting task, because it
requires understanding of the underlying knowledge models and
concepts behind them
| existing natural language technologies can partly extract meta-data
out of the natural language text and use this information to
interactively guide users in the annotation
process (e.g. by providing custom wizards, or
even by simple reordering of the offered
concepts)


Information extraction
Information Extraction

Goals

| to develop an information extraction component that semi-
automatically extracts meta-data out of wiki pages to
interactively guide the user through the annotation task


Personalisation
Personalisation

Motivation

| different users have different roles in knowledge management
processes (e.g. developer, tester, documentation writer, customer)
| different users might also have different tasks and/or preferences
| in both cases, personalised presentation of content and user
interface provides support for the user
| a semantic wiki can support this by storing user/group models in
the knowledge base, by offering a reasoning component, and by
offering advanced visualisations and editors that implement
personalised access to the wiki


Personalisation
Personalisation

Goals

| develop appropriate user and group models for representing
properties and preferences of groups wrt. the KIWI system
| automatically track the usage of single users and user groups in
order to refine user and group models
| develop rules that dynamically adapt the presentation of the
content and user interface to the user


What I believe made KIWI a success
(Scientific & Technological Content)

| KIWI has a concrete, graspable, clearly defined
outcome
| at the end of the project, we will have a single,
demonstrable system that actually implements what we
promise
| theory touching the ground!
| KIWI addresses real-world problems identified by its industrial
partners
| no blue sky research, application oriented
| KIWI improves already existing technology where it has deficiencies
| IkeWiki
| KIWI builds upon previous EU and national projects and takes their
outcomes to the next level
| REWERSE, QVIZ, Dynamont


(Project Setup)

| The KIWI consortium is well balanced
| complementary expertise, exactly as needed for the project
| 3 universities, 1 research centre, 3 industrial partners (1 SME)
| 4 countries (Austria, Germany, Czech Republic, Denmark)
| Significant effort goes into Dissemination & Demonstration
| 64 out of 388 PM (= 18,5%)
| the importance of marketing research results is often
underestimated
| The project workplan and budget was planned as if it was
already a description of work
| thorough and realistic calculation


(Social Dynamics)

| We had a proposal kick-off meeting with all
partners 2 months before the submission
deadline
| people learn to know and trust each other
| a rough project workplan was set up (Gantt diagram)
| social bonds are important!
| I tried to collect issues and send out email to the partners only
once a week (“KIWI weekly update”)
| no email overload at the partners (be nice to them!)
| clear definition of who is supposed to do what by which date (make it
easy for them!)
| We communicated often via Skype to clarify issues and tasks
| keep in touch with them (but not by email)
| We signed Letters of Intent as part of the proposal
| demonstrates commitment
| makes management aware of the project


(Formalia)

| We made use of the pre-proposal check service offered by
the Commission, which gave valuable feedback
| We tried to take into account all available background
material:
| work programme and guide for proposers
| background material specific to the unit/strategic objective
| slides of the Call 1 Information Days
| general EU policy documents


Challenges


Challenges KIWI won’t solve
(but it would profit from solutions!)

| efficient, distributed content and knowledge repository
| with reasoning support (rule-based as well as other)
| with support for both content and metadata
| and with support for billions of information items (content,
triples, ...)
| KIWI will only provide a “small and specific” solution!
| non-textual content
| (semi-)automatic metadata extraction
| annotation
| breaking the “format lock”
| and of course lots of other topics that are outside the direct
project scope (e.g. other Semantic Social Software) ...

08.09.2008 KIWI Project Presentation © 2008, Sebastian Schaffert, Salzburg Research

Conclusion
Conclusion


Conclusion

Summary (1)

| KiWi is concerned with knowledge management following
the wiki philosophy and applying semantic web technologies
| KiWi is evaluated in two use cases, software knowledge
management (Sun Microsystems) and project knowledge
management (Logica)
| KiWi core concepts are content items, tags, triples, users,
roles, and context
| the KiWi user interface builds on flexible, customisable
layouts and widgets
| KiWi is implemented using legacy Java EE technology using
a model-view-controller paradigm


Conclusion

Summary (2)

| KiWi also addresses novel research issues in the areas of
reasoning, reason maintenance, information extraction and
personalisation


The KiWi Vision

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