=Paper=
{{Paper
|id=Vol-161/paper-14
|storemode=property
|title=Natural-Language Semantics for Associations
|pdfUrl=https://ceur-ws.org/Vol-161/FORUM_13.pdf
|volume=Vol-161
|dblpUrl=https://dblp.org/rec/conf/caise/Evermann05a
}}
==Natural-Language Semantics for Associations==
https://ceur-ws.org/Vol-161/FORUM_13.pdf
77
Natural-Language Semantics for Associations
Joerg Evermann
School of Information Management, Victoria University, Wellington, New Zealand
joerg.evermann@mcs.vuw.ac.nz
1 Introduction
Conceptual models describe an application domain to further communication
and understanding, and serve as the basis for subsequent software design and
implementation. For a language to be used for conceptual modelling, the seman-
tics of its constructs must be well-defined w.r.t. the application domain.
The semantics of the association construct1 , central to object-oriented mod-
elling languages, are problematic from the software perspective [1, 2], as well as
in conceptual modelling. The definitions in the literature often obscure, rather
than clarify the meaning of the construct.
Prior research interpreted associations ontologically as mutual properties [3],
and classified them according to linguistic and cognitive considerations [4]. The
ontological interpretation confounds properties and interaction, while the latter
does not explain the meaning of associations. Relationships and associations
have been interpreted as relations, i.e. sets of tuples [2, 5] and in terms of their
meaning for subsequent system implementation and programming [1].
The semantics of a language construct are defined by its semantic mapping to
an element of the semantic domain [6]. For purposes of conceptual modelling, the
semantic domain consists of those concepts in which we perceive the application
domain, with which we think and reason about the domain. These concepts are
human cognitive concepts.
Significance of Cognitive Linguistics Research in cognitive linguistics has demon-
strated that the most fundamental cognitive concepts are those that are encoded
syntactically or morphologically in natural language (e.g. [7, 8]). Cross-linguistic
research shows that variations in syntactic features correspond to variations in
cognition, confirming the close relationship between the two. Studies have shown
evidence of such a relationship in a number of domains such as color categoriza-
tion, spatial reasoning, gender systems, etc. Developmental research examines
how the development of cognitive structures influence the development of lin-
guistic competence, or vice-versa. Either direction of influence confirms the re-
lationship between language and cognition.
1
Composition and aggregation associations are outside the scope of this paper.
Proceedings of the CAiSE'05 Forum - O. Belo, J. Eder, J. Falcão e Cunha, O. Pastor (Eds.)
© Faculdade de Engenharia da Universidade do Porto, Portugal 2005 - ISBN 972-752-078-2
78 Joerg Evermann
2 Natural Language Semantics for Associations
Noting the structure of associations, Embley hints at the possible semantics of
the association: ”Relationships associate one object with another, similar to the
way verbs and verb phrases relate one noun or noun phrase to another” [9, p. 18].
Hence, identifying the semantics of verbs can be used to define and clarify the
semantics of associations. A UML profile [10] is used to formalize the proposed
semantics.
Verb Semantics The most fundamental distinction made in cognitive linguistics
is between spatial entities, such as things, places and paths, and temporal en-
tities, such as events and states. The former are expressed by nouns and noun
phrases, the latter are expressed by verbs2 [11–13]. The temporal domain consists
of two concepts: states and events [11–16].
Consequently, we suggest that associations represent two types of concepts:
states and events. For events, the main verb usually expresses dynamic action
or activity, e.g. ’Customer has ordered product’, ’Supplier will ship product’. In
contrast, a state expresses static conditions that hold between associated objects.
No change occurs in the objects and states are not commonly associated with
activity. In English, they are generally expressed by the verb ”be”, e.g. ’Professor
is member of faculty’, ’Product is located in warehouse’.
Properties of Events As events and states are expressed by verbs, they possess all
of the semantic concepts that natural languages mark on verbs or verb phrases.
The upper part of Table 3 summarizes the set of such concepts proposed by cross-
linguistic research [14–19] and research in cognitive linguistics [11–13, 18, 19], and
gives explanations and examples. The table also shows how these distinctions
are formally realized in the proposed UML profile.
Causation Beyond the semantic concepts for all events, natural languages mark
a further set of semantic concepts for causal events [11]: Directness, Immedi-
acy, Coextensiveness, and Resistance. They are shown, with explanation and
examples, in the second part of Table 3.
Event Participants Events are expressed by verbs, which in turn possess one
or more arguments [14–16]. As verbal arguments play thematic roles, so the
participating classes or objects in associations must play thematic roles. Table 1
shows the roles proposed by [11, 14–17].
3 Example
Consider an association without the proposed semantics attached: A Shipping
Clerk participates in a ”shipping” association with a Customer and a Package.
This model is ambiguous w.r.t. the semantic notions described in Sec. 2. For
2
But see ’temporalization’ and ’reification’ in [11].
79
Role Description
Agent The performer of an action
Patient To whom something is done, who undergoes an action
Object To what something is done
Theme The topic of the event
Experiencer Who experiences (listens, sees, etc.) something
Beneficiary Who undergoes an action with a benefit
Locative The location of an event
Perceiver The perceiver who sees, feels, etc. an action
Instrumental The instrument by which the action is performed
Source The source of the action (generally of a motion action)
Goal The goal of the action
Reason The reason for the action
Purpose The purpose of the action
Author The speaker or write (for communicative actions)
Recipient Who receives something by means of the action
Comitative Something that accompanies the action
Table 1. Thematic roles (cases) marked on verbs
Stereo type Base Class Parent Description (Additional Tags
Semantics)
State Associa- Association N/A A static condition involving Tense
tion two or more objects
Event Associ- Association N/A A dynamic interaction be- Tense, Aspect, Progres-
ation tween two or more objects sivity, Iterativity, Punc-
tuality, Telicity, Modal-
ity, Volitionality, Oppo-
sition, Success
Causal Event Association Event As- An event association where Directness, Immediacy,
Association sociation the dynamic interaction is Coextensiveness, Resis-
caused by an object (or
event) tance
Event Partici- Association N/A An association end linked to Thematic Role
pant End either a state or event as-
sociation, and linked to an
object or event participat-
ing in this association
Table 2. Stereotypes for the Natural Language Semantics Profile
example, we don’t know whether the association represents planned shipments,
shipments in progress, past shipments or recurring (standing) shipping orders.
To explicate the intended semantics, we employ the proposed profile (Fig. 1).
The model now shows the roles of the participants: The shipping clerk is the
agent, the packages are the objects, and the customer plays the locative role.
This indicates that the packages are shipped to or from the customer, rather
than for the customer (i.e. at customer’s cost/on the customer’s account).
The explicit tags show that the association expresses past (Tense), completed
(Aspect), shipping events, not for example current, in-progress shipping. Ship-
Name Explanation Examples Type Multi Values
plicity
Tense Relative temporal posi- Order was taken (past tense), order is taken (present Enumeration 1..1 Past, Present, Future
tion of activity tense)
Aspect State of completion of Order has been processed (imperfective), order had been Enumeration 1..1 Perfective, Imperfective
activity processed (perfective)
Progressivity Does the activity have a Shipper delivers product (final state), factory manufac- Boolean 1..1
80 Joerg Evermann
final state? tures products (keeps manufacturing, no final state))
Iterativity Is the activity repeti- Customer picks up orders on Wednesday morning (re- Boolean 1..1
tious? peats every Wednesday), customer picks up the order
next Wednesday morning (once only)
Punctuality Temporal distribution or Product leaves assembly (punctual), product is being Enumeration 1..1 Punctual, Durative
interval painted (durative)
Telicity Does the activity have a Inventory is reduced (accidentally), inventory is cleared Boolean 1..1
goal? (purposefully, goal-driven)
Modality Permission, ability, obli- Customer (can) pick up order (Possible), Customer Enumeration 1..* Actual, Desirable, Pre-
gation, etc. (must) pick up order (Obligatory) dicted, Obligatory, Possi-
ble, Impossible, Optional,
Permissible, Forbidden
Volitionality Is the activity willful? Machinist repaired the machine (neutral), machinist was Boolean 0..1
made to repair the machine (willful)
Opposition Positive or negative ef- Customer defrauds business (negative), customer refunds Enumeration 0..1 Negative, Positive
fects money owing (positive)
Success Is success the effect or Staff enters area (prevention), staff enters area (effect) Enumeration 0..1 Effect, Prevent
prevention of change?
Directness Number of links in causal Machine damaged product (1), machine caused profits to Integer 1..1
chain drop (> 1)
Immediacy Temporal continuity in Shipping product reduces inventory (continuous), Ship- Enumeration 1..1 Continuous, Discontinuous
causal chain ping product increases profits (discontinuous, effect may
be delayed)
Coextensiveness Temporal overlap of Breaking the machine caused faulty products (Onset, Enumeration 1..1 Onset, Extended
cause and effect cause does not need to be maintained), lowering the tem-
perature to harden the product (Extended, cause must be
maintained)
Resistance Effectuating or enabling Using the forklift to unload goods (Effectuating), opening Enumeration 1..1 Effectuating, Enabling
causation the valves to unload goods (Enabling, removing blockage)
Thematic Role Enumeration 1..1 ref. Tab. 1
Table 3. Tag Definition and Tag Values for the Natural Language Semantics Profile
81
ping progressed towards a goal (Progressivity, the delivery of the package) and
occurred once only, not repeatedly. Shipping was durative, i.e. it took some time,
and was the effort of some agent (telicity). The association represents actual
shipping events of the past, rather than past plans, abilities, etc. (modality).
Stereotyping the association as a ’Causal Event Association’ makes it clear
that the shipping clerk caused the object to the be shipped. The causation
is indirect: The shipping clerk is twice removed (directness of degree 3), she
did not ship the packages herself, nor did she herself cause the courier to ship
the packages. Instead, she had the courier ship the packages. The immediacy
indicates that is a discontinuous causation, i.e. there is a time lag between the
cause and the effect. Perhaps the shipping order takes some time to be processed
by the courier. The event is a type of onset causation, as the shipping clerk does
not have to maintain any action to sustain the shipping activity. Finally, the
event is caused by enabling it, rather than effecting it. For example, shipping
orders may already be issued but need to be approved by the shipping clerk. The
approval removes the blockage and the event can proceed. In contrast, for an
effectuating causation, the agent issues the shipping orders, rather than remove
a hindrance.
<>
{Tense=Past, Aspect=Perfective, Progressivity=True, Iterativity=False, Punctuality=Durative, Telicity=True, Modality=Actual,
Directness=3, Immediacy=Discontinuous,Coextensiveness=Onset, Resistance=Enabling}
Agent Object
Shipping Clerk Shipping Package
Locative
Customer
Fig. 1. Example association representing causation using the proposed profile
Without the proposed profile, the example could be interpreted in many
different ways. Semantic distinctions are often implicit and based on domain
or background knowledge. When this knowledge is not shared among modeller
and model interpreter, the model may be interpreted incorrectly. The proposed
profile forces the modeller to explicate the possible semantic distinctions and
rely less on assumed background knowledge. Hence, it leads to more accurate
model interpretations.
4 Discussion and Conclusion
Especially in the context of MDA, we need to consider not only conceptual mod-
elling, but also implementation concerns. This proposal does not introduce new
82 Joerg Evermann
constructs, nor does it constrain the use of constructs. It has therefore no con-
sequences for IS implementation. We believe that disambiguating the semantics
of associations is a valuable contribution by itself.
The fact that some distinctions may appear to be not applicable in some
situations does not indicate a shortcoming of the present proposal. The cogni-
tive linguistics research on which this proposal is based, suggests that, while not
all languages make all distinctions, every distinction is grammaticized in some
natural languages. Instead of dismissing concepts such as ’opposition’ or ’suc-
cess’ as not relevant, they can offer insights into the application domain and its
dynamics which may be hidden and require further exploration. They may also
have significance in cross-cultural or cross-linguistic IS development contexts.
Finally, the fact that events may be represented as classes instead of as-
sociations, e.g. ’Shipment’, ’Enrollment’, ’Use’, etc. shows the need for further
exploration of this research. The present paper is intended to clarify the seman-
tics of associations, rather than the representation of events and states.
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