Deliberating on Ontologies: The Present Situation. Simon Milton Department of Information Systems, The University of Melbourne

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1 Deliberating on Ontologies: The Present Situation Simon Milton Department of, The University of Melbourne

2 1. Helping data models better map the world 2. Finding the role of ontology where theories of agency form the theoretical basis of IS design

3 Research Questions 1. How well do data models represent reality? 2. What are the similarities and differences between data modelling languages?

4 Philosophical ontology vs... Ontology, understood as a branch of metaphysics, is the science of being in general, embracing such issues as the nature of existence and the categorial structure of reality. Different systems of ontology propose alternative categorial schemes. A categorial scheme typically exhibits a hierarchical structure, with being or entity as the topmost category, embracing everything that exists Honderich, 1995

5 ...artificial intelligence, CS, & EDI On the other hand, in its most prevalent use in AI, an ontology refers to an engineering artefact, constituted by a specific vocabulary used to describe a certain reality, plus a set of explicit assumptions regarding the intended meaning of the vocabulary words (Guarino, 1998)

6 Reference ontology Domain-specific ontology Most general categories of what there is in the world (thing, individual, property). Data modelling languages such as ER are at this level (Basic Formal Ontology, DOLCE) Early CS/AI focus is at this level (see Guarino 1998). Semantic Web now attempting to generalise the plethora of these (SUO and WordNet). Data models (University, NAB, etc) are at this level Specific reality Where we are (most of the time) Implemented databases are at this level

7 Question 1: Method Conceptual Evaluation Reference ontology Data model ontology Conceptual Evaluation { 1. Indicator of coverage (semiotics) 2. Description of relevance

8 Parameters of the study Ontology plethora of choice past use (Bunge (1977) /Wand/Weber (1989) pragmatic selection Data modelling languages dominant in industry plethora of choice representative range (er, fdm, sdm, niam, omt)

9 Reference ontology: Chisholm s (1996) Entity Contingent Necessary States Individuals States Nonstates Events Boundaries Substances Attributes Substance

10 Constitution of an ontology 1. Categorisation - implicit or explicit (previous slide) 2. Terms defined by concepts to describe what there is 3. Fundamental terms from the categorisation used to define the terms in 2.

11 Static Attribute * Individual * Relation Class/set Dynamic State * Event * Homeless Fictitious objects Intentionality Appearances * fundamental term (from categories)

12 Q1 - Representing Reality All modelling languages support: 1. Individuals that are ontologically independent 2. Attributes that are exemplified by individuals 3. Part-whole relationships between individuals (mereology) 4. Relations between individuals 5. Classes of individuals selected on the attributes exemplified High degree of overlap with an ontology with this consensus of common-sense realism, and so can represent reality well

13 Q2 - Differences...between modelling languages: Attribute/individual separation (FDM and NIAM provide this) Class membership (heterogeneous classes + simultaneous class-membership =OMT + SDM) Level of sophistication for part-whole structures (ER is at class level vs. OMT and SDM with increased sophistication)

14 1. Helping data models better map the world 2. Finding the role of ontology where theories of agency form the theoretical basis of IS design

15 For studying information system design, theories of agency are more fundamental than ontologies Ontology is about what is in the world and how to represent it Agency is about how to make a system that does something Proposition

16 Agency How an agent can obtain data about (sense) the world in which it acts How an agent can represent the world it senses How an agent can use this representation to select actions which will allow it to close on its goal How actions are effected

17 Deliberative action Shakey (Nilsson)

18 Effectors World Model Sensors World

19 Goal State Plan EXECUTE Agent Actions EFFECT Intended Goal REASON World Model: Entities, Attributes, Relations, Laws. Current State AGENT WORLD Current State of World MODEL REACT World: Things, Properties, Relations, Laws Sense Data SENSE Reaction on Agent Deliberative agent

20 Data Model (domain-specific) ecision Making Goal State Plan EXECUTE Agent Actions EFFECT Intended Goal REASON World Model: Entities, Attributes, Relations, Laws. Current State Database AGENT WORLD Current State of World MODEL REACT World: Things, Properties, Relations, Laws SENSE Transaction Processing Sense Data Reaction on Agent Socio-technical system as agent

21 Ontology supporting Deliberative Theory of Agency Ontological Construct Object Property Relation Class State Action Plan Explanation / Informal Definition Objects are distinguishable particulars in the world. Objects may be primitive (simple) or composite (made up of other objects). Objects possess properties. Objects may be related to other objects. An object, through the properties exhibited, may be a member of classes. Objects have a state through the properties they exhibit. A deliberate change in state. A collection of actions forms a plan taking the agent from the current state to the goal state.

22 Sensors Actuators Traditional decomposition of a mobile robot control system into functional modules. Sensors reason about behavior of objects plan changes to the world identify objects monitor changes build maps explore wander avoid objects Actuators Brookes Decomposition of a mobile robot control system based on taskachieving behaviors.

23 Situated action

24 Pengi (Agre)

25 Ontology supporting Situated Theory of Agency Ontological Construct Aspect of Situation Situation Action Response Explanation / Informal Definition An aspect of a situation is a relationship between an agent and its environment that the agent perceives from the environment and is tuned to noticing. An aspect, as perceived by an agent, has a structure that mirrors the things, relations, and properties that the agent perceives through surfaces shown to the agent and in turn show an aspect of a situation. A situation is a set of aspects. An agent is said to be in a situation when the set of aspects of the situation is present in the environment as perceived by the agent. An agent notices a situation is when the set of aspects is present in the environment. Each aspect is the instantiation of things properties and relations through slots automatically sensed. One of the possible physical processes an agent can initiate to change its relation to the environment (along certain physical dimension notably position). An action includes speech acts. A response is a pairing of a situation with an action. For a given situation the only action taken is the action to which it is paired in a response. An action may be to begin a new activity or to end an activity.

26 (philosophical) reference ontologies used in Bunge s ontology (1977 & 79) applied in IS by Yair Wand and Ron Weber Chisholm s ontology (1996) Which ones cover the extremes? Which one gracefully moves between the extremes?

27 Construct Thing Property State Event Lawful transformation History Coupling System System structure Sub-system Ontologies in IS: Bunge s Ontology Explanation / Informal Definition Elementary item in ontological world. Assume that the world is composed of things. Things may be primitive (simple) or composite (made up of other things). Things possess properties. Properties are modelled using attribute functions that map things into values. Properties are either composite (belonging to a component thing) emergent (emerging from the thing itself) or shared (mutual). The vector of values of all attribute functions of a thing is the state of the thing. (laws and state spaces may be defined) Change of state of a thing, effected via a transformation. A lawful mapping from a domain of states to a co-domain of states. Chronologically ordered states over the lifetime (or history) of a thing. Two things are coupled if the existence of one thing affects the history of the other thing. A system is a set of things, such that for any partitioning into two sets, coupling exists between things in opposing sets. System composition and envrionment can be defined. External and internal events can be defined with respect to systems. A system structure is the set of couplings that exist among things in the system, and the interactions with things in the system environment. A subsystem is a system whose composition and structure are subsets of composition and structure of another system

28 Ontologies in IS: Chisholm s Ontology Construct Explanation / Informal Definition Individual Attribute Chisholm allows for discernible and transient objects. These are called individuals. Each individual is identified by attribute(s). Individuals may have constituents. These are either other individuals (known as parts) or boundaries (the other constituents). Attributes are exhibited by individuals. Attributes may be simple or complex. Complex attributes are combinations of either simple or other complex attributes. The mechanism suggested by Chisholm is one involving conjunction and disjunction of attributes. He feels there may be other ways of providing for this complexity. Relation Class / Set State / event Individuals may be related. Specifically, relations are attributes (an ordered pair). The ontology requires that attributes that identify the participating individuals are required. Further, that the relations are unidirectional (not bidirectional). Classes and sets are provided using attributes. It is through the attributes that membership of classes is determined. Individuals exemplify attributes and that exemplification is a state. Events represent changes in state. Individuals, and states are contingent and therefore cease to exist, and come into being. A state consists of a substrate (a substance) and a content (an attribute). Both of these can be divided further into parts according to need. Appearance the sensing of appearances is subjective in being dependent for its existence on the existence of the sensing subject of experience.

29 Covering ontology for theories of agency Deliberative Situated Ontological Construct Bunge Chisholm Ontological Construct Bunge Chisholm Object 4 4 Aspect of Situation 7 4 Property 4 4 Relation 4 4 Class 4 4 Situation 7 4 Action 4 4 Response 7 4 State 4 4 Action 4 4 Plan 4 4

30 Bunge s ontology Naturalism:... [O]ntological naturalism is supported by the success of natural science, and success is success in recognizing what is real, it would do best to define natural as what is recognized by natural science (Kim & Sosa, 1995) Chisholm s ontology Common-sense realism: [C]ommon-sense is not, in spite of its reputation, naïve; it draws a systematic distinction between reality and appearance, or in other words between the way the world is and the way the world seems or appears via one or other of the sensory modalities and from the perspective of one or other perceiving subject in one or other context. The thesis that there is only one world towards which natural cognition relates must thus be understood as being compatible with the thesis that there are many different ways in which the world can appear to human subjects in different sorts of circumstances. (Smith, 1995) The common-sense realist must confront the question of the relation between the commonsense world and the world that is described in the textbooks of standard physics. Here again a number of different philosophical alternatives have been mapped out in the course of philosophical time, including that view that it is the common-sense world that is truly autonomous while the world of physics is to be awarded the status of a cultural artifact. Here in contrast, we assume a thesis to the effect that the common-sense world overlaps substantially with physical reality in the more standard sense. (Smith, 1995)

31 For my part, I have been unable to identify alternative ontologies that are as rigorous and compelling as Bunge s. I accept, of course, that such ontologies might exist. If so, we can clearly debate their relative merits at a philosophical level, but I suspect it will be more productive if we focus instead on how well they inform conceptual modelling practice and the design of conceptual modelling grammars (Weber, 2003)

3. The reality of information systems research

3. The reality of information systems research John Lamp, School of Information Systems, Deakin University Simon Milton, Department of Information Systems, University of Melbourne Abstract The examination