© keith g jeffery, anne g s asserson iwircris copenhagen 200620061109 1 exploring the cloud of...

© Keith G Jeffery, Anne G S Asserson IWIRCRIS Copenhagen 2006 20061109 1

Exploring the Cloud of Research Information Systematically

Keith G Jeffery

Director, IT & International Strategy

CCLRC

[email protected]

Anne G S Asserson

Research Department

University of Bergen

[email protected]


Structure

• The Problem, Proposition, Requirement

• Past Work, Analysis, Conclusion

• Solution

• Additional Aspects

• Future Work

• Conclusion


university

Funding agency

Research chemist

doctor

synchrotron PhD thesis

Journal paper

presentation

conference

industry

entrepreneur

Classification scheme protein

DNA

The universe of information of relevance

What I want

‘magic’ system

The Problem


The Problem: The Universe of Relevant Information

• Unstructured, semistructured– Policy papers– Research proposals (part)– Publications

• Structured– Research proposals (part)– Research information records

(commonly metadata)– Research datasets

• Heterogeneity– Character set– Language– Syntax– Semantics

Highly relevant

relevant

Partially relevant


RelationshipUsual Relation

person

OrgUnit

PERSON

OrgUnit

ORGUNIT

PK PKFK


Project

OrgUnitPerson

Structured Approach

Linking relations expressing semantics

Pr-Pe Pr-OU

Pe-Pe

Pe-OU

OU-OU


The research project aims to discover the particular properties of the umouzo worm that cause it to provide local tequila with

its characteristic taste. The method will involve chemical analysis of the worm at various stages of its lifecycle related

to the tequila: its early life in its usual forest habitat, its middle life when collected and held in artificial conditions as stock

before it is placed into the bottle of tequila and finally within thetequila environment at various stages of maturity of the tequila. The research will be conducted by Professor Quoaxocoatl andhis Analytical Biochemistry team, belonging to the Departments

of Chemistry and Biology at the University of Chicken Itza, Mexico using the latest spectroscopic analysis techniques. The

beneficiaries of the research will be the Chicken Itza Tequila company which expect to improve the flavour by understanding

the chemical changes in the worms. The research will be conducted following the ethical code for experimentation on

animals. The funding requested is 1,000,000 US Dollars over 3 years.

Research Text: un- or semi-structured text


The research project aims to discover the particular properties of the umouzo worm that cause it to provide local tequila with

its characteristic taste. The method will involve chemical analysis of the worm at various stages of its lifecycle related

to the tequila: its early life in its usual forest habitat, its middle life when collected and held in artificial conditions as stock

before it is placed into the bottle of tequila and finally within thetequila environment at various stages of maturity of the tequila. The research will be conducted by Professor Quoaxocoatl andhis Analytical Biochemistry team, belonging to the Departments

of Chemistry and Biology at the University of Chicken Itza, Mexico using the latest spectroscopic analysis techniques. The

beneficiaries of the research will be the Chicken Itza Tequila company which expect to improve the flavour by understanding

the chemical changes in the worms. The research will be conducted following the ethical code for experimentation on animals. The funding requested is 1,000,000 US Dollars

over 3 years.

Research Text: finding / classifying syntax


The research <project> aims to discover the particular properties of the umouzo worm that cause it to provide local tequila with its characteristic taste. </project> <abstract> the method will involve chemical analysis of the worm at various stages of its lifecycle related to the tequila: its early life in its usual forest habitat, its middle life when collected and held in artificial conditions as stock before it is placed into the bottle of tequila and finally within the tequila environment at various stages of maturity of the tequila. </abstract><person> The research will be <relationship> conducted by </relationship> Professor Quoaxocoatl </person> And <relationship> his </relationship> <orgunit> Analytical Biochemistry team </orggunit> <relationship> belonging to the </relationship> <orgunit> Departments of Chemistry </orgunit> and <orgunit> Biology </orgunit> <relationship> at the </relationship> <orgunit> University of Chicken Itza, Mexico </orgunit><abstract> using the latest spectroscopic analysis techniques. </abstract> The <relationship> beneficiaries </relationship> of the research will be the <orgunit> Chicken Itza Tequila company </orgunit>which expect to <abstract> improve the flavour by understanding the chemical changes in the worms. </abstract> The research will be conducted <condition> following the ethical code for experimentation on Animals. </condition> The funding requested is <funding> 1,000,000 US Dollars </funding>over <duration> 3 years. </duration>

Parsing to a defined schema for research information


Problem: How to build the ‘magic system’

• (1) how to assure quality data – so that results are accurate;

• (2) how to assist the end-user in formulating correctly the query – to obtain the expected

results; • (3) how to structure the

data to obtain the optimal response – in terms of performance,

recall and relevance including taming heterogeneity.

What I want

‘magic’ system


Proposition

• 3 questions are related

• Solution based on– Structured data (and using it as metadata)– Formal logic (reproducible)– Knowledge engineering techniques

• Exposed to end user with– User-friendly assistant– Graphic metaphors


Requirement• The end-user wants a

homogeneous response to a query – which may involve

functional processing in addition to a simple retrieval.

– response in a reasonable time

– with all relevant information (recall)

– some indication of relevance (how closely he answer matches the query).

• Example:

• Total amount of funding by year by university spent on biomedical research

• For last 10 years by midday today

• Ensuring all universities are included

• With computed distance score from ‘biomedical’ in classification scheme


Requirement: Data Quality• Real world Decision-

making based on the computer system

• Decision-making quality depends critically on the data quality

• end-user presented with graphical representations of statistically-reduced or model-enhanced data


Requirement: User Query

• How does an end-user express this in a structured query

• or find the (relevant, complete) information via Google?

– Are the years calendar or financial?

– Are the patents measured by number or value of licences granted?

– Only publications in peer-reviewed journals?

– Universities in which countries

“compare the research performance of my university against others across a range of metrics (products, patents, publications) over years”


Requirement: Data Structure

• Timeliness – when required, usually

immediately

• Relevance – Of results to query

• Recall – Completeness of results

• All require structured data• Or structured metadata

describing semi-structured or unstructured data

• What is the SQL for I want it now?

• How to match complex logic of query (with processing) to result set

• How to know what is in the ‘universe’

• For precise matching / measurement / calculation


Structure



• Solution


• Future Work

• Conclusion


Related Work

• Generally relevant– Information management, user interfaces,

performance….. • Copious

– Related directly to CRIS • limited and mainly in CRIS conferences


Related Work - analysis• integrating heterogeneous

distributed databases of CRIS information including

– open access institutional repositories

– research datasets

– represented by structured metadata

• into a homogeneous canonical form;

• harvesting semistructured information from the web to create a structured metadata index – with limited information in a

structured form– May be only term and URL

• which points to the original semistructured data in its native form;


Related Work: Conclusions• unstructured or semistructured data is valuable only when

indexed by structured metadata • satisfactory responses to end-user queries only produced

when the data are structured, or indexed by structured metadata;

• satisfactory response to end-user queries over heterogeneous distributed data requires knowledge-based techniques (And KB techniques rely on structured data)

• semistructured harvesting/browsing techniques require much human effort and so do not scale

• Structured data-based techniques allow for further processing, automating and scaling but more R&D is needed to make it work


Related Work: Conclusion

• Semistructured data indexed

• Satisfactory user query• Basis for advanced KB

techniques• Scalability• Further processing


Project

OrgUnitPerson

Structured Approach

Linking relations expressing semantics

Pr-Pe Pr-OU

Pe-Pe

Pe-OU

OU-OU


CERIF-CRIS

Publtext

Scidata

ProjDesc

CVOrg

Desc

Publtext

Scidata

OrgDescCV

ProjDesc

Much human effort in selecting and integrating

Harvesting / browsing

Human effort spent on decision-making


Structure



• Solution


• Future Work

• Conclusion


Solution: Quality Structured Information: Data

• Data quality can only be assured if the input or edit of data attribute values is validated and – if necessary – supported with explanation of valid values

• To achieve this requires structured data i.e. data arranged as attribute values in a structure to form information

• validation techniques applicable to each data attribute and relationships between attributes [GoGlJe93].

• validation relies on first order logic and Boolean algebra. This demands structured data (information).

• For CRIS: CERIF – data structured formally as information– Attribute values in controlled vocabularies (domain ontologies)


Solution: Quality Structured Information: Metadata

• CERIF can act as metadata to other structured, semistructured and unstructured information

• Examples: OA IRs and research datasets and software. Being extended to financial, project and personnel information

• ensures such associated datasets are validated, retrieved and interpreted in a structured and logical context.

Repository

CERIFas metadata

Object e.g. document


Solution: Expert Advisor and Query Assistant

• Much past work on this area. Process is:– Interact with user to discover not ‘what they say’ but ‘what

they mean’

– Enhance the query appropriately for relevance, recall, performance

– Replay (in natural language) query to user for confirmation

– Execute query including taming heterogeneity

– Provide user with answer integrated (automatically according to user preference) in preferred

• Character set, Language, Syntax, Semantics, Media / Mode


Solution: Homogeneous View through Knowledge-Based Information Integration• Several known techniques for providing a homogeneous

information (syntactic) & knowledge (semantic) view over heterogeneous data

– http://epubs.cclrc.ac.uk/work-details?w=33728

• May be a stored view (data warehouse)– Problem of currency

• Or on-demand view– Problem of performance

• In any case requires– Schema matching– Query rewriting and distribution– Answer conversion and integration


Solution: Explanation

• When user receives answer– Even if translated to canonical (CERIF) form

• It may not be understandable so need explanation• This is done using knowledge-based techniques

– The ‘reverse’ of query assistance / improvement

– Using domain ontologies to associate additional information to the answer to explain


Structure



• Solution


• Future Work

• Conclusion


Analysis, Modelling, Visualisation

• Decision making today is complex– Multiple parameters– Huge volumes of data, sometimes structured as

information– Information of varying reliability

• So typically the data is processed to represent in a succinct fashion (eg graph, map, video)

• And computer models are used to produce predicted results for comparison with the recorded data

• This cannot be done without structured information which is somehow made consistent


Push Technology

• Push technology relies on a standard query which is executed when triggered by a (relevant) change in the observed database or an externally defined condition

• Therefore it requires structured data (or structured metadata representing un- or semi-structured data)


Now, recall the example query

• “compare the research performance of my university against others across a range of metrics (products, patents, publications) over years”

• And let us imagine how it would be handled by the proposed system environment


Utilising the Solution: The Researcher

• The researcher would find – an easy-to-use assisted interface;

– behind which integration of information from multiple sources would be performed automatically,

– even bringing into a homogeneous context semistructured information (notably publications and research datasets with associated software) via the structured metadata.

• Thus the cloud of research information becomes a well-formed set of quality information.


Utilising the Solution: The Research Manager

• The research manager in a funding organisation would find – an easy-to-use assisted interface with the required information

from multiple heterogeneous sources integrated. – The interface provides appropriate functions for comparing the

funding; the explanation engine provides background information on the way in which funding is calculated in each country.

– quality information structured in context and with appropriate explanation to assist in interpretation.

– interface assistance to formulate the query to ensure the correct year(s) and departments are selected and the count of publications is done correctly according to the criteria (e.g. against a list of acceptable peer-reviewed publication channels).


Utilising the Solution: The Innovative Entrepreneur

• The innovative entrepreneur utilises the advanced interface – to formulate a homogeneous query over the

heterogeneous national sources of information. – The query is improved by the inference engine and

domain ontology to overcome the fact that terminology differs from country to country and she wishes to compare like with like in terms of patents, products and their generated wealth through licences or sales.

– More complex is to compare track records in wealth creation of research groups; a graphical representation of value against time is used with annotation to explain the basis of the reasoning leading to the inclusion or exclusion of certain research outputs.


Structure



• Solution


• Future Work

• Conclusion


Future Work

• As indicated more R&D required:– Faster and more effective schema integration– generation of software to then automatically

manage the distributed heterogeneous queries and the answer homogenisation

– Faster and more effective tools for building and maintaining domain ontologies (including consistency checking)

– Tools for managing multiple languages and character sets


Structure



• Solution


• Future Work

• Conclusion


Conclusion

Keith G JefferyDirector, IT & International [email protected]

Anne G S AssersonResearch DepartmentUniversity of Bergen

[email protected]

The widespread use of web browsers gives the impression that information for decision makers is readily available.

It is, but is of varying quality and requires heavy human involvement to use it. This does not scale.

Structured data, or metadata representing un- or semi-structured data is the only reliable foundation.

Upon this, advanced CRIS systems for decision-making can be built.


Entity represented by a relation or table

One instance

Oneattribute

Another Entity

relationship

Structured Approach

© keith g jeffery, anne g s asserson iwircris copenhagen 200620061109 1 exploring the cloud of...

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