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
SuppliesBuy
Cultivate
Extract Seeds Seed & Vegie
Prices
Plan &
Budget Weather
Plan Budget
Fertilizer
Seeds
Plants
Vegetables
ProducePick 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).
<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
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, …
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
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.
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: Business Governance in a Vola8le World”, Release 1.3, September 2007.
[Borgida04] Borgida, A., Mylopoulos, J., “Data Seman8cs Revisited”, VLDB Workshop on the Seman8c Web and Databases (SWDB’04), August 2004, Springer LNCS, 9‐26.
[Cassirer42], Cassirer, E., Zur Logik der Kulturwissenscha6en, Gšteborg, 1942; see also: The Logic of the Cultural Sciences, Yale University Press, 2000.
[Chen76] Chen, P., “The En8ty‐Rela8onship Model – Towards a Unified View of Data”, ACM Transac@ons on Database Systems 1(1), 1976.
[Guarino09] Guarino, N. “Introduc8on to Ontological Analysis”, Lecture notes for a PhD course given at the University of Trento, May 2009.
[Hohfeld13] Hohfeld, N., “Fundamental Legal Concep8ons as Applied in Judicial Reasoning”. Yale Law Journal 23(1), 1913.
[Mylopoulos97] Mylopoulos, J., “Informa8on Modeling in the Time of the Revolu8on”, Informa@on Systems 23(3‐4), June 1998, 127‐156.
[Panofsky55] Panofsky, E., “Iconography and Iconology: An Introduc8on into the Study of Renaissance Art”, in Meaning in the Visual Arts. Doubleday, 1955.
[Schmidt09] Schmidt, J., “On Conceptual Content Management: Interdisciplinary
