Conceptual Modelling in the Time of the Revolution: Part II · Conceptual Modelling in the Time of the Revolution: Part II ER’09, Gramado, Brazil November 11, 2009 ... focusing

©2009 John Mylopoulos ER’09 -- 1

Conceptual Modelling in the Time of the

Revolution: Part II

ER’09, Gramado, Brazil November 11, 2009


Abstract   Conceptual Modeling (CM) was a marginal research area at the very

fringes of Computer Science (CS) in the 60s and 70s, when the discipline was dominated by topics focusing on programs, systems and hardware architectures. Over the years, however, This has changed over the past three decades, with CM playing a central role in CS research and practice in diverse areas, such as Software Engineering (SE), Databases (DB), Information Systems (IS), the Semantic Web (SW), Business Process Management (BPM), Service-Oriented Computing, Knowledge Management (KM), and more. The transformation was greatly aided by the adoption of standards for modeling languages (e.g., UML), and model-based methodologies (e.g., Model-Driven Architectures) by the Object Management Group (OMG), W3C, and other standards organizations.

  We briefly review the history of the field over the past 40 years, focusing on the evolution of key ideas. We then note some open challenges, covering topics such as modelling businesses, cultural objects and laws.


Acknowledgements   I am grateful to my colleagues and students whose ideas

are represented (… modelled!) in these slides.   I am particularly grateful to three long-time

collaborators and friends: Alex Borgida who showed me the way on a formal grounding for conceptual modelling languages; Nicola Guarino who taught me the basics of ontological analysis; and Joachim Schmidt, who pointed me to a future for Conceptual Modelling.


… Twelve Years ago …


Conceptual Models ➥  Use domain-oriented concepts (e.g., entity,

relationship, goal, actor, …) and are structured according to cognitive principles (e.g., generalization, aggregation, classification, …).

➥  Adopt an associationist viewpoint: models consist of nodes that represent concepts and associations/links that represent semantic/episodic/other relationships between concepts.

➥  Associationism has a long (and illustrious!) history in Philosophy and Psychology that goes back to Plato and Aristotle.


Origins in Computer Science


Conceptual Models

eats

has

isa

isa

isa

isa isa

M 1

M

M

Buy Supplies

Cultivate

Extract Seeds

Seed & Vegie Prices

Plan & Budget Weather

Plan Budget

Fertilizer

Seeds Plants

Vegetables

Pick Produce Vegetables

Grow Vegetables

Money


Defining Moment for Conceptual Modelling (CM)

"...The entity-relationship model adopts ... the natural view that the real world consists of

entities and relationships... (The entity-relationship model) incorporates some of the

important semantic information about the real world...”

[Chen75], VLDB’75


1975-1997 – Exploring the Frontier ➥ Many applications

  Design models for databases and software (DB, IS, SE)   Knowledge-based systems (AI, IS)   Knowledge management (AI, IS)

➥ Many modelling languages   Dozens of proposals for semantic network-based languages, frame-based languages, description logics, …   More dozens for semantic data models …   Box-and-arrow notations in SE


Basic Ontologies

[Mylopoulos97]


Research Methodology I   … The meaning triangle

Sign

Concept

Referent Tulips

1,2,3,4,5,6

Flower


Research Methodology II  New concepts for modelling applications; e.g.,

the use of the concept of goal to model software requirements in SE.

  Formal semantics for modelling languages, and automated reasoning support for models.

 Note: Throughout the ‘70s and ‘80s CM was a fringe research area in Computer Science.


Pitfalls of Informal Models


What Happened Next?   The Semantic Web (Tim Berners-Lee et al) – web data

need to be encapsulated with their semantics, so that they can be processed automatically.

  Model-Driven Software Engineering (OMG) – Software development consists of processes that create and manipulate chains of models ranging from problem-oriented, platform-independent ones to machine-oriented, platform-dependent ones.

  Model Management (Phil Bernstein et al) – to cope with data complexity we need models (schemas, ontologies, …)

  Ontological Analysis (Nicola Guarino et al) – “… content must be analyzed independently of the way it is represented …”


The Semantic Web   Use semantically rich ontologies to capture the semantics

of concepts and roles/relationships.   This is accomplished by adopting Description Logics as

ontology modelling languages.   Annotate web data with the concepts they instantiate, eg,

  Some concerns: Capturing semantics through more expressive languages vs capturing semantics through a richer collection of primitive concepts [Borgida04].

  More concerns: There is a lot more to making web data “machine processable” than semantic annotations, see data integration framework (DBs).

  Even more concerns: Usability, scalability, …

<person> Paolo Buono <residence> lives in Trento </residence> and works at the <work> University of Trento </work> </person>


“Far Side” take on the

Semantic Web


Ontological Analysis (OA)   Consider

  2000 Presidential election: Is there a hole?   2001 World Trade Centre catastrophe: How many

events? Ontological analysis can answer these questions

Book by Roberto Casati and Achille C. Varzi (MIT Press):

•  Holes and other superficialities

•  Parts and places

[Guarino09]


The Formal Tools of OA   Theory of Essence and Identity   Theory of Parts (Mereology)   Theory of Unity and Plurality   Theory of Dependence   Theory of Composition and Constitution   Theory of Properties and Qualities   …

OA is to CM what Sub-atomic Physics is to Physics


The State-of-the-Art in CM   A large collection of modelling languages,

ranging from Description Logics, to the EER model and UML class diagrams (cum OCL).

  Specialized languages for requirements, software architectures, various domains, …

 Ontological Analysis.   A growing number of relevant communities: ER,

KRR, FOIS, SemWeb, Models, CAiSE, RE, AAMAS, …

No longer a fringe research area within CS!


Looking Forward  We understand very well static and dynamic

ontologies, pretty well intentional and social ones.

  There are many applications out there that aren’t being served well with what we have so far …  Business worlds  Cultural worlds  Legal worlds


Business Worlds   There are many business modelling languages

(eg, UML extensions), business process modelling languages, business rule languages, …

 We are interested in a language intended for governance -- i.e., a language that would allow a business to model its objectives, trends, threats, opportunities, etc., and monitor its daily activities to ensure compliance.


An excellent Starting Point: A Business Ontology

  There is an OMG standard (as of 2007) -- called the Business Motivation Model (BMM) -- intended precisely for business governance.

  The standard includes a large number of concepts, ranging from {visions, objectives, goals} to {means strategies, plans}, to {metrics, indicators}, to {strengths, weaknesses, threats, vulnerabilities, opportunities).

  But BMM is weak with respect the state-of-the-art on modelling languages (OA, DL-like definition of concepts, …)


Example: Strategic Goals Largest

auto maker

Maintain status quo

Best auto maker

XOR Max customer

satisfaction

Happy customer

Quality product

Quality service

AND

Fuel prices

New technology

influences

influences


Governance according to BMM   From control to governance

Means Ends

Influencers Assessments


Cultural Worlds   Art uses very rich

symbols, compared to those used in Science …

  Science models rely on formalization for interpretation; art models depend on form & style for interpretation


Art vs Science on Modelling “… In Kant’s expression, the natural sciences teach us to ‘spell out phenomena in order to read them off as experiences’; the science of culture teaches us to interpret symbols in order to decipher their hidden meaning, in order to make the life from which they originally emerged visible again …”

[Cassirer42, p.86]


The Meaning Triangle Revisited

Flowers

Artist’s World Artifact

Intention


The Meaning of Art Symbols   Another excellent starting point: Artistic meaning has to

be understood at different levels of abstraction [Panofsky55]   0. Individual (existence level): plain media view (image,

text, speech, ...)   1. Characteristics (description level, pre-iconographic):

color, sizes, age,...; artists, periods, regions,…; content -- humans, animals, fruits, trees, ...

  2. Iconography (meaning level): paradise, seducing Eve, curious Adam, tempting apple, sinful snake, ...

  3. Iconology (effect level): Jewish and Christian ethics and legal systems, their origins and consequences, ...

[Schmidt09]


Legal Worlds   Laws are notoriously difficult to understand

and use for purposes of law practice, as well as compliance.

  Conceptual models of laws could be used to put on a more systematic footing software system & business process compliance.

  Such models could also be used by lawyers and others who need to interpret and understand law.

  For this domain too, there is an excellent starting point …


Hohfeld’s Legal Ontology

  Proposed almost a century ago [Hohfeld13].  Milestone in jurisprudence literature.

30


Summary  We are concerned with the design of conceptual

modelling languages and their use in building models for diverse domains.

  Conceptual models are useful artifacts for purposes of understanding, communication, design, management, and more.

  There has been much progress in spelling out the principles that underlie such languages …

…but much remains to be done.


“Move away from any narrow interpretation of databases and expand its focus to the hard problems faced by broad visions of data, information, and knowledge management”

Motto 12th International Joint Conference on Extending Database

Technology and Database Theory, Saint-Petersburg,

2009


[BMM07] Business Rules Group, “The Business Mo8va8on Model: BusinessGovernanceinaVola8leWorld”,Release1.3,September2007.[Borgida04]Borgida,A.,Mylopoulos,J.,“DataSeman8csRevisited”,VLDBWorkshopontheSeman8cWebandDatabases(SWDB’04),August2004,SpringerLNCS,9‐26.[Cassirer42],Cassirer,E., Zur Logik der Kulturwissenscha6en, Gšteborg, 1942;seealso:The Logic of the Cultural Sciences,YaleUniversityPress,2000.[Chen76]Chen,P.,“TheEn8ty‐Rela8onshipModel–TowardsaUnifiedViewofData”,ACM Transac@ons on Database Systems 1(1),1976.[Guarino09]Guarino,N. “Introduc8on toOntological Analysis”, Lecture notes for aPhDcoursegivenattheUniversityofTrento,May2009.[Hohfeld13] Hohfeld, N., “Fundamental Legal Concep8ons as Applied in JudicialReasoning”.Yale Law Journal 23(1),1913.[Mylopoulos97] Mylopoulos, J., “Informa8on Modeling in the Time of theRevolu8on”,Informa@on Systems 23(3‐4),June1998,127‐156.[Panofsky55] Panofsky, E., “Iconography and Iconology: An Introduc8on into theStudyofRenaissanceArt”,inMeaning in the Visual Arts.Doubleday,1955.[Schmidt09] Schmidt, J., “On Conceptual Content Management: InterdisciplinaryInsights beyond Computa8onal Data”, in Borgida, A., et al (eds.) Conceptual Modeling: Founda@ons and Applica@ons SpringerLNCSno.5600,June2009,153‐172.

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Conceptual Modelling in the Time of the Revolution: Part II · Conceptual Modelling in the Time of the Revolution: Part II ER’09, Gramado, Brazil November 11, 2009 ... focusing

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