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Information Made Information Made Accountable Accountable The - - PowerPoint PPT Presentation

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Information Made Information Made Accountable Accountable The Data Projection Model Michel Biezunski Infoloom mb@infoloom.com http://www.infoloom.com 1 Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010 Michel

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Information Made Accountable Information Made Accountable

The Data Projection Model Michel Biezunski

Infoloom mb@infoloom.com http://www.infoloom.com

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Michel Biezunski

IT Consultant/Innovator, dba Infoloom, based in New York. Created the Topic Maps paradigm. Initiator of the ISO/IEC 13250 standard . Major current project: TaxMap, an electronic delivery tool for IRS publications and forms.

Background: History/Philosophy of Science

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Rationale

  • The problem

– Accountability works with financial information. – Accountability doesn't work so great with non- financial information.

  • The fix

– Use accounting-like approaches for information management.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Outline Outline

Theoretical Introduction: Why bother? The Data Projection Model: Double Entry Bookkeeping for Information. Application Examples.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Luca Pacioli

The father of accounting... and of other things

Luca Pacioli, painting attributed to Jacopo de' Barbari, 1495. http://www.art-prints-on-demand.com/kunst/_1123580422666163/alg55519.jpg

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Luca Pacioli

1445-1514 (or 1517)

Worked on Perspective. “Invented” Double Entry Bookkeeping. Wrote on Accounting Ethics and Cost Accounting. Elementary Algebra Taught mathematics to Leonardo da Vinci. Wrote De Divina Proportione.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

RDF, the foundation of the Semantic Web

Object Predicate Subject (Resource) (Resource) Based on “triples”. Uses URIs to name the relationship between things (“Predicate”) as well as the 2 ends of the link.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Subject - Object

In the Quattrocento perspective system, the ideal point of localization is “the one which places as opposite, but parallel, the subject and the object.” This is called a “one point perspective” or “central perspective”. Governed by a single vanishing point. Several conceptions of perspective:

  • Science of Vision
  • Technique of Representation
  • Technique of the Measurement
  • Architecture
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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Perspective as a Science

It is among our senses, the wise men conclude, that the sight is the noblest one. That is why vulgarly said, with reason, that the eye is the first door from which the intellect understands and likes. (L. Pacioli, De Divina Proportione, f.4r) The eye, which is said is the window of the soul... (Leonardo da Vinci, Paragone)

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Flattening the world

Perspectives are defined according to projections. Perspectives express ways 3- dimensional space is rendered into 2- dimensional, i.e. projections.

Pacioli´s book De Divina Proportione illustrated by Leonardo da Vinci.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Real World Information:

Is multidimensional. Can be flattened to be processed. Binary relations correspond to 2D space Translating a world of n-ary relations into a world of binary relations is a kind of projection. The result of projecting is a graph. Perspective is what accompanies projection from n-ary relations to binary relations.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

The tree of proportions and proportionality, by Luca Pacioli.

Proportions as a Semantic Network

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Entity-Relationship Model

Can always be decomposed into binary relations.

A simple entity relationship

  • model. Http://

en.wikipedia.org/wiki/ Entity-relationship_model

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

The limitations of RDF

Or rather, the way it is used

The problem Subject – Predicate – Object: A one point perspective

  • n things.

Not contextual: Who said so? (not automatically reified) Operates in a closed world. First order logic works if everything known. The fix Nodes derive meaning from connectors (provide context) Enable multiple perspectives. Every node is an “account”. Logic is not built-in, multiple logics can be super-imposed. Still RDF, but used differently.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Introducing the Data Projection Model

  • The Data Projection model enables information systems

to become auditable. It aims at facilitating system maintenance and knowledge management. The Data Projection Model can be used to integrate information assembled from a variety of sources and to express multiple perspectives on the same information set.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

What is Data Projection for?

Tracking background of any information item for: – Search engine efficiency: Why a given item is on top of the hits? – Identity Theft Investigation: Where is the leakage – Privacy: Who knows what? Managing funding with strings attached: How is this grant being spent? Where does the money go? Accounting++ Integrating heterogeneous sources. Creating diverse views for targeted audiences.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Data Projection. How it works

Views result from linking data. Semantic is in the views. Multiple views are possible: – Filtering out unwanted information. – Focusing on details (microscopic views) – Anything in between. Views can be created after information is produced. Different people can have different perspectives of the same information set.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Information and Accounting

Accounting Transactions occur between accounts. Account statements: all transactions from or to an account. Information Any information item is always related to at least another one. Audit trail: all links from or to an information item.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Single Entry List of expenses per category List of income sources per category. Some money may be unaccounted for (although not desirable). Double Entry Organized by accounts. Each transaction affects two accounts in a way that keeps the overall system of accounts in balance. No money amount unaccounted for.

Bookkeeping made accountable

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Double Entry Bookkeeping

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Double Entry Bookkeeping & DPM

Plane Ticket to SFO PTSFO 091013 $450.00 2009-10-13 Checking Account Air Travel Expenses

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Double Entry Bookkeeping & DPM

Plane Ticket to SFO PTSFO 091013 $450.00 2009-10-13 Checking Account Air Travel Expenses

Description Date +$450.00

  • $450.00

Subaccount

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

DPM Notation

Plane Ticket to SFO PTSFO 091013 $450.00 2009-10-13 Checking Account Air Travel Expenses

Description Date +$450.00

  • $450.00

Subaccount < PTSFO_09-10-13_450.00 | date | 2009-10-13 > < PTSFO_09-10-13_450.00 | description | Plane ticket to SFO> < PTSFO_09-10-13_450.00 | 450.00 | Air > < PTSFO_09-10-13_450.00 | -450.00 | Checking Account > < Air | subaccount of | Travel > < Travel | subaccount of | Expenses >

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

A “perspector” is notated: < x | o | y > x and y are operands (order matters).

  • is an operator.

A perspector can represent a semantic relation, for example: < New York | is a | city > ( This is an instance/class relationship)

  • r < city | added to the system by | MB >

( This is usually considered metadata).

Perspector

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

From Multiple to Multiple Via One

SGML / XML

– One source, – Multiple outputs

(Ex Uno Plures)

DPM Diverse inputs, One common representation, Multiple outputs (E Pluribus Plures Via Unum)

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

DPM and RDF

RDF is based on triples that express statements: subject – object – predicate RDF connects URIs RDF statements are not automatically reified. DPM is based on triples that express operations: x

  • perand – operator – y
  • perand

DPM is not limited to URIs DPM perspectors are automatically reified.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

DPM and Topic Maps

Topic Maps is a Navigation system using topics as nodes for representing subjects. Names, Types, Occurrences are topics connected through specific relationships. DPM is a Navigation system based on nodes All nodes are related with

  • ther nodes.

Topic Maps can be considered an application of DPM.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

A Name does not identify a Subject: Variant names may be used to designate the same subject. Synonyms Typographical variations One name may identify several subjects.

Example: Name versus Subject

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Washington Washington, DC Wash D.C. George Washington Denzel Washington Washington State Wa General Washington

Names

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Names

< Washington | is an alternate name for | Wash. D.C. > < Washington | is an alternate name for | Washington, DC > < Washington | is an alternate name for | General Washington> < Washington | is an alternate name for | George Washington > < Washington | is an alternate name for | Wa > < Washington | is an alternate name for | Washington State > < Washington | is an alternate name for | Denzel Washington >

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Washington Washington, DC Wash D.C. George Washington Denzel Washington Washington State Wa General Washington

Emerging Subjects

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Strings Become Subjects

Washington Washington, DC Wash D.C. George Washington Denzel Washington Washington State Wa General Washington

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Generalization

Washington Washington, DC Wash D.C. George Washington Denzel Washingt Washington State Wa General Washington

is a name for is a name for is a name for is a name for is a name for is a name for is a name for is a name for is a name for is a name for

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Names and Subjects

< Washington | is a name for | _city_of_Washington > < Washington DC | is a name for | _city_of_Washington > < Wash. D.C. | is a name for | _city_of_Washington > < Washington | is a name for | _General_G_Washington > < General Washington | is a name for | _General_G_Washington > < George Washington | is a name for | _General_G_Washington > < Washington | is a name for | _Washington_State > < Wa | is a name for | _Washington_State > < Washington State | is a name for | _Washington_State > < Washington | is a name for | _Denzel_Washington > < Denzel Washington | is a name for | _Denzel_Washington >

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Strings as Subjects

< Washington | is in character set | UTF-8 > < Washington | is a name for | _city_of_Washington > < Washington | is a name in the language | English >

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Washington General Washington George Washington Wa Washington State Denzel Washington Washington, DC Wash D.C.

abbreviates indicates is usually called designates is the last name of is a code name for stands for is a name for represents also known as

Integration

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Diversity

< _city_of_Washington | is usually called | Washington > < Washington DC | indicates | _city_of_Washington > < Wash. D.C. | abbreviates | _city_of_Washington > < Washington | is a name for | _General_G_Washington <_General_G_Washington| also_known_as | General Washington > < George Washington | represents | _General_G_Washington < Washington | stands for | _Washington_State > < Wa | is a code name for| _Washington_State > < Washington State | is a name for | _Washington_State > < Washington | is last name of | _Denzel_Washington > < Denzel Washington | designates | _Denzel_Washington >

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Perspective on Naming

< _city_of_Washington | is named | Washington > < Washington DC | is a name for | _city_of_Washington > < Wash. D.C. | is a name for | _city_of_Washington > < Washington | is a name for | _General_G_Washington > <_General_G_Washington| is named | General Washington > < George Washington | is a name for | _General_G_Washington > < Washington | is a name for | _Washington_State > < Wa | is a name for | _Washington_State > < Washington State | is a name for | _Washington_State > < Washington | is a name for | _Denzel_Washington > < Denzel Washington | is a name for | _Denzel_Washington >

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Multidimensional Information

< New York | is a name for | _New_York_City > < New York | is a name for | _New_York_State > < New York | is a name for | _New_York_County > < New York | is a name for | _Manhattan > < New York | is a name for | _Wall_Street > < New York | is an old name for | _Manhattan > < Nueva York | is a name for | _New_York_City > < קרו׳ ונ | is a name for | _New_York_City > < New York | is a name in the language | _English > < Nueva York | is a name in the language | _Spanish > < New York | is a name in the language | _French > < English | is a name for | _English > < English | is a name in the language | _English > < Anglais | is a name for | _English > < Anglais | is a name in the language | _French > < Inglés | is a name for | _English > < Inglés | is a name in the language | _Spanish >

etc., etc., etc., etc., etc., etc., etc., etc., etc., etc., etc., etc., etc.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Why was the DPM created?

TaxMap is a topic map-based application, published every

  • ther week, containing IRS publications, forms, and FAQs,

accessible by topics.

– Topics are extracted from XML document structure. – Relations between topics are created using rules and

by applying a semantic layer authored by tax experts.

  • Rendition hides the creation process. Hence the question

(among others): why are topics a and b related?

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

TaxMap is built by a combination of automatic and manual processes. Names are added, modified, sometimes deleted, or regarded as synonyms. It's hard to know where a topic name comes from.

Operations on Names

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

TaxMap audited

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Tax Map Audited

Living Abroad

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Where does “Living Abroad” come from?

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Containment Rule Results

If one topic name is entirely contained into another one, they get automatically related.

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Synonyms created by Tax Experts

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Michel Biezunski, Infoloom Semantic Conference, San Francisco, 6/23/2010

Demos, other presentations available at: http://www.infoloom.com

Michel Biezunski Infoloom (718) 921-0901 mb@infoloom.com

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