Spinning out of IPAS
Sam Chapman
Ravish Bhagdev, Vitaveska Lanfranchi, Fabio Ciravegna
University of Sheffield.
Director, Knowledge Now Ltd.
s.chapman@dcs.shef.ac.uk
s.chapman@k-now.co.uk
http://www.dcs.shef.ac.uk/~sam/
http://www.k-now.co.uk
IPAS
Technology
◦ Background
◦ K-Search
◦ K-Forms
Application of Technology
K-Now
Outline of talk
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 Integrated Products And Services (IPAS) is
essential to the long-term success of businesses
in the interlinked emerging global environment.
◦ Aim: knowledge transfer between “three worlds”:
 new service design,
 new product design,
 the operation of existing products and services in the field.
 IPAS integrates and applies a number of
disparate research fields spanning
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computer science,
engineering design,
knowledge management,
manufacturing,
work psychology.
IPAS: what is IPAS?
 The objective is to develop and exploit
technologies such as
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meta-data,
semantics,
ontologies,
text mining,
search,
social interactions,
knowledge representation,
semantic web services.
 to empower the right person with the
right information at the right time.
IPAS: objectives
 Funds: about £1.5M
◦ 39% Rolls Royce plc
◦ 42% Department Trade and Industry
 Now TSB
◦ 18% other industrial partners
IPAS: participants
 Sheffield’s role
◦ Use of
 Text mining,
 Knowledge capture,
 Semantics,
 Ontologies,
 Text and image annotation,
 Knowledge storage,
 Search,
◦ To manage knowledge
 sharing and reuse between the three worlds
IPAS: Sheffield's Role
To provide the capability to query legacy document content
as if it were structured information
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Via acquisition of content metadata
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Automatically from legacy data
Manually and semi-automatically for new documents (including
images)
Empowering:
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Content based retrieval (NOT documents)
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Automatic quantitative analysis
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Automatic correlation of facts across documents and
archives
To improve the way documents are currently created,
providing tools that will make easier to semantically
“annotate” the content while writing
IPAS: Sheffield's Role
 Existing Knowledge managment solutions
rely upon combinations of basic
technologies:
◦ Simple document indexing,
◦ DataBase backed centralised views of how
knowledge should be organised, with
regimented interfaces to them,
◦ But such approaches have a myriad of issues,
 Sheffield’s role in IPAS tries to resolve these.
Technology: background
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 Keyword approaches are simple and understood
by users but…
◦ Uncertain returns/Unquantifiable
 10,145 hits but how many are relevant to query?
◦ Synonyms
 “George W Bush” vs “43rd president of the USA”
◦ Homonyms
 “Bank” (river vs financial)
◦ Meronyms
 USA doesn’t include Massachusetts
◦ Documents not “knowledge”
 Repeated information – pointer to context not information
directly
 Although techniques to mitigate these “defects”
can be added to Search Systems they are:
◦ Partial solutions (addressing some cases only)
◦ Unsuited to most knowledge discovery needs
Technology: background-Keyword 9
 Most business
knowledge needs
are not being met
by keyword
approaches alone:
◦ Business Intelligence
◦ Quantitative Analysis
◦ Trend Spotting
 Uncertain returns
are not suitable for
many users
Technology: background-Keyword10
 Other issues in keyword approaches:
◦ Repetitive textual information
 Repeated documents/formats confuses
returns
◦ Constrained Language
 “Blade” in the aerospace domain is in
>70% available documents
 Context of information is paramount to its
meaning, e.g.
◦ “Damaged Blade XYZ”
◦ “Damaged Blade YXZ housing”
Technology: background-Keyword11
 Deployment
◦ Access to a corpus of documents. (crawled or
otherwise)
◦ Creation of Inverted Index
◦ Additional features [optional]
 Synonym list(s)
 Query Suggestion
 Custom Stemming
 OneBoxtm style additions
 Federated search combinations
 etc etc
Technology: background-Keyword12
 Storing graph structured
typed knowledge rather
than index based stores.
◦ Extensible and combinable
schemas
◦ Hierarchically typed
information
Name:
<String>
Sam
Chapman
Person:
<Person>
Person3456
Residence:
Age:
<Country>
UK
<Integer>
35
◦ Precise Query Support
 Quantifiable
Technology: background-Semantics
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 Semantic approaches offer great
advantages vs keyword approaches but…
 Have shortcomings:
◦ Difficulties in obtaining structured data
 “Semantic capture”
◦ Query although powerful can be complex from
a user perspective
◦ Rigid structured organisation constrains user
interaction
 Not all possible "knowledge" is encoded into a
re-usable structured form.
Technology: background-Semantics
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 Deployment
◦ Requires the acquisition of structured
information (semantic capture)
 Two methods:
◦ Legacy and external document support
◦ Capture at point of knowledge generation
 methodology for semantic capture will
be detailed
Technology: background-Semantics
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 What is needed is a way to combine
keyword and semantic style
interactions into a single hybrid
approach to empower flexible query.
◦ Users can easily switch between, or
combine:
 Keyword approaches
◦ Simple
◦ Fast
◦ Not constrained by structured representations
 Semantic approaches
◦ Quantitative
◦ Accurate
◦ According to structured representations
Technology: K-Search - aim
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 K-Search:
◦ Server-based tool for hybrid searching and
sharing of knowledge stored in a hybrid
repository.
 Advanced Query Capabilities.
◦ Mixed Modality and flexibility
 Enabling Knowledge reuse.
 Quantitative analysis / Business Intelligence /
Trend Analysis
 Simple user interface to create queries and
visualise results.
◦ Hides the complexity of the search mechanism and
underlying storage.
Technology: K-Search
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 Semantic Capture of Legacy documents
◦ Uses:
 Document conversion (MS WORD and PDF)
 Machine Learning (K-ML based upon T-Rex)
 Rule based extraction (K-Rules based upon
Saxon)
◦ To - Extract structured knowledge using:
 Layout
 Typography
 Linguistic Context
 Etc
◦ Achieve:
 Precision = 98%
 Recall = 99%
 F-Measure (harmonic) = 98%
Technology: K-Search
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 K-Search allows the user to perform
complex queries of three types:
◦ Keyword Search: simply inserting keywords the
results are retrieved and displayed.
◦ Semantic Search: using ontology concepts to
focus through the available knowledge
 For example
◦ E.g. Identify geographic regions where staffXYZ filed
report<typed> concerning componentXYZ detailing
damageMechanismXYZ in the last 3 months.
◦ Hybrid Search: perform queries mixing
semantic and keyword approaches.
Technology: K-Search
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 A query is created via a web form interface
enabling easy graphical composition of semantic
concepts and keyword-based conditions.
◦ Keywords can be inserted into a default form field in a way
similar to most search engines;
◦ Boolean operators AND and OR can be used in their
combination.
◦ Additional conditions on conceptual knowledge can be easily
added to the query by clicking on an ontology concept.
Technology: K-Search
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 Quantitative analysis of query results enables
◦ Problem identification
◦ Trends to be plotted using conceptual information
 K-Search supports:
◦ Visualisation of quantitative information.
◦ Automatically generating graphs and charts.
◦ External support for analysis.
 Graphs and documents can be shared.
 Connection to External applications (MS OFFICE, DB and statistical
analysis packages).
Technology: K-Search
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service engineers
designers
others
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The starting point
of Search varies
from user
experience and
focus
Technology: K-Search - eval
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Engineers
Designers
Other
Different users and groups of
users use search differently.
K-Search empowers such flexibility
Technology: K-Search - eval
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Usability evaluations using - ISO DIS 9241-11
Technology: K-Search - eval
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 K-Search has been awarded runner-up
status in the Rolls-Royce, Directors
Creativity Award 2007
 It is deployed in
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RR – Event Reports
RR – Technical Variance Documents
X-Media Box and Kernal
Coming soon:
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RR – ERMS
RR – Module Condition Reports
RR - C-Sheet
RR - Event Summary Reports
RR - Modification bulletins
RR - Service bulletins
RR - SDM/Maximo
Talkback (feedback management for entertainment industry)
 Patented technology for Hybrid search
Technology: K-Search - Validation 25
 K-Search is designed to work ontop of K-Store
◦ A hybrid keyword and semantic store
 K-Search can query a variety of distributed
knowledge
K-Search
IE Capture:
legacy
Documents
Database
plugins
K-Forms
Captured
Knoweldge
K-Form
Technology: K-Search
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 Capturing Knowledge from legacy
documents requires:
◦ Custom extraction per document type.
◦ Skilled programmer involvement.
◦ A small degree of inprecision.
 K-Forms intends to provide an
alternative means for Knowledge
acquistion
◦ Capture at point of knowledge
generation
Technology: K-Forms
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 Semantic Capture of New Knowledge
◦ K-Forms
 A flexible form design/entry based application
◦ Allows dynamic form generation to capture
new knowledge at the time needed.
 Doesn’t require specialised knowledge/technical
skill.
 Users can design custom forms at any moment.
◦ Captured knowledge according to interlinked
knowledge structures (ontologies).
Technology: K-Forms
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 When a new form is designed
◦ Behind the scenes an ontology is
generated specifying the form
concepts and relations between them.
◦ Also linkages are made between
existing knowledge structures to link
knowledge regardless of capture
context.
Technology: K-Forms
FormA
Person
Name
Feedback
...
...
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FormB
FormA
Feedback
...
Person
Person
Name
...
Name
...
Project
Organisation
...
...
 Linkages develop between forms automatically creating
semantically interlinked knowledge at the point of its
capture without additional effort from users.
 In a corporate knowledge base this will provide a
complete interconnected knowledgebase automatically
through usage.
Technology: K-Forms
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 A spin-out company from the Europe's largest
NLP Research Group at the University of Sheffield
(UK)
 Commercialising Business Intelligence and
Knowledge Management software.
◦ Semantic Knowledge Capture technologies
◦ Hybrid Storage technologies
◦ Hybrid Search technologies
K-Now
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 The technology commercialised was
developed primarially within IPAS.
 K-Now is primarily based on the
combination of two main ontology
based semantic tools:
◦ K-Forms – a flexible business tool for semantic
capture of conceptual knowledge at the point of
generation.
◦ K-Search – a server-based business tool for
semantic, keyword and hybrid knowledge search.
◦ Other technologies
 K-Store, K-ML, K-Rules.
 Aim
o
Deriving business advantage for dynamic
knowledge needs across (and beyond) an
organisation.
K-Now
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 A.-S. Dadzie, R. Bhagdev, A. Chakravarthy, S. Chapman, J.
Iria, V. Lanfranchi, J. Magalhães, D. Petrelli and F.
Ciravegna: “Applying Semantic Web Technologies to
Knowledge Sharing in Aerospace Engineering” in Journal of
Intelligent Manufacturing, to appear in 2008
 Ravish Bhagdev, Sam Chapman, Fabio Ciravegna,
Vitaveska Lanfranchi and Daniela Petrelli: “Hybrid Search:
Effectively Combining Keywords and Semantic Searches” in
Proceedings of the 5th European Semantic Web
Conference, ESWC 08, Tenerife, June 2008
 Vitaveska Lanfranchi, Ravish Bhagdev, Sam Chapman,
Fabio Ciravegna, Daniela Petrelli: “Extracting and Searching
Knowledge for the Aerospace Industry” in Proceedings of
1st European Semantic Technology Conference, Vienna
May31, June 1 2007
s.chapman@k-now.co.uk
http://www.k-now.co.uk
Highlight Publications
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s.chapman@k-now.co.uk
http://www.k-now.co.uk
Questions
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