Conceptual Metaphors: Ontology-based representation and corpora
driven Mapping Principles
Kathleen Ahrens
National Taiwan University
kathleenahrens@yahoo.com
Siaw Fong Chung
National Taiwan University
claricefong6376@hotmail.com
Chu-Ren Huang
Academia Sinica
churen@sinica.edu.tw
Abstract
The goal of this paper is to integrate the
Conceptual Mapping Model with an on-
tology-based knowledge representation
(i.e. Suggested Upper Merged Ontology
(SUMO)) in order to demonstrate that
conceptual metaphor analysis can be re-
stricted and eventually, automated. In
particular, we will propose a corpora-
based operational definition for Mapping
Principles, which are explanations of why
a conventional conceptual metaphor has a
particular source-target domain pairing.
This paper will examine 2000 random ex-
amples of ‘economy’ (jingji) in Mandarin
Chinese and postulate Mapping Principles
based frequency and delimited with
SUMO.
1 Introduction
A theory of metaphor has been the focus of study
on lexical and figurative meaning for the past two
decades. Are conventional conceptual metaphors a
cognitive rather than a linguistic phenomenon?
Work within Cognitive Linguistics would seem to
say that this is the case. For example, Lakoff
(1993) writes with respect to the source-target do-
main mapping of the conventional conceptual
metaphor LOVE IS A JOURNEY:
Is there a general principle govern-
ing how these linguistic expressions
about journeys are used to charac-
terize love…. [Yes], but it is a gen-
eral principle that is neither part of
the grammar of English, nor the
English lexicon. Rather it is part of
the conceptual system underlying
English…. (page 306, italics added)
Thus, the onus of dealing with metaphorical
meaning in the lexicon is not necessary. Metaphor
must be treated at a different (i.e. higher) cognitive
level.
But is it really the case that there are no
general principles that can be extracted and pro-
posed at the lexical level? The Conceptual Map-
ping (CM) Model (Ahrens 2002) was proposed to
constrain the Contemporary Theory of Metaphor
(Lakoff 1993). This model analyzes the linguistic
correspondences between a source and target
(knowledge) domain in order to determine the un-
derlying reason for the source-target pairings. The
underlying reason is formulated in terms of a Map-
ping Principle. The theory also postulates a Map-
ping Principle Constraint, which says that a target
domain will select only source domains that in-
volve unique mapping principles. For example, the
target domain of IDEA uses the source domains of
BUILDING and FOOD, but it does so for different
reasons (as we will discuss in the next section).
With the addition of this constraint, the CM model
is able to explicate the polysemy inherent in a
given target domain. In addition, the CM Model
presupposes that Mapping Principles are conven-
tionalized linguistically but not conceptualized a
priori. This model is supported in psycholinguistic
experiments because it correctly predicted the
processing differences involved between conven-
tional and novel metaphors (Ahrens 2002). In this
paper, we propose a new approach to conceptual
metaphors that incorporates two computationally
trackable elements. First, the data analysis is cor-
pus-based, following the example of MetaBank
(Martin 1992). Second, the representation is ontol-
ogy-based. Both elements strengthen the empirical
basis of the account.
In this paper, we propose that the most
frequent mapping instance within a source domain
indicates the basis of the reason for the source-
target domain pairing, i.e. the mapping principle.
We test this empirical prototype (EP) hypothesis
by running extracting a dataset of 2000 examples
of jingji ‘economy’ from the Academia Sinica
Balanced Corpus. We hypothesize that each
source-target domain pairing will have a proto-
typical instance of mapping as evidenced by an
individual lexical item that is highly frequent as
compared with other mappings. In addition, we
propose using an ontological-based knowledge
representation, such as SUMO, to define and de-
limit the source domain knowledge in the CM
Model.  This has the advantage of using SUMO to
infer knowledge through automatic reasoning, and
as well as constraining the scope and falsifiablity
of the conceptual metaphor.
2 The Conceptual Mapping Model and
Ontology
Ahrens (2002) proposed that the question asked by
Lakoff above (‘Is there a general principle gov-
erning how these linguistic expressions about jour-
neys are used to characterize love?’) should be
answered by examining the lexical correspon-
dences that exist between a source and target do-
main. She proposes that the linguistic expressions
that are used metaphorically can be analyzed in
terms of the entities, qualities and functions that
can map between a source and a target domain.
When these conventionalized metaphorical expres-
sions have been analyzed, they are compared with
the real world knowledge that the source domain
entails, and an underlying reason for these map-
pings is then postulated.
For example, she points out that in the
conceptual metaphor IDEA IS BUILDING in
Mandarin, the linguistic expressions relating to the
concept of foundation, stability and construction
were mapped (i.e. are conventional linguistic ex-
amples) while concepts relating to position of the
building, internal wiring and plumbing, the exterior
of the building, windows and doors were not (and
these are the concepts that are in the real world
knowledge of the source domain). Thus she postu-
lated that the target domain of IDEA uses the
source domain of BUILDING in order to empha-
size the concept of structure. Thus, when someone
talks about ideas and want to express positive no-
tions concerning organization, they use the source
domain of BUILDING. The Mapping Principle
formulated in this case was therefore the follow-
ing:
(1) Mapping principle for IDEA IS BUILDING:
Idea is understood as building because buildings
involve a (physical) structure and ideas involve
an (abstract) structure. (Ahrens 2002)
When IDEA is talked about in terms of
FOOD, however, the expressions that are mapped
are ‘ingredient’, ‘spoil’, ‘flavorless’, ‘full’, ‘taste’,
‘chew’, ‘digest’ and ‘absorb’. Mandarin Chinese,
in contrast with English, does not have conven-
tional expressions relating to ‘cooking’ or ‘stew-
ing’ of ideas. Thus, the postulated Mapping
Principle is: Idea is understood as food because
food involves being eaten and digested (by the
body) and ideas involved being taken in and
processed (by the mind) (Ahrens 2002).
Thus, IDEA uses the source domains of
BUILDING and FOOD for different reasons,
namely to convey information related to ‘structure’
or ‘processing’ (i.e. ‘understanding’) respectively.
Thus, it is similar to the Contemporary Theory of
metaphor in that it supposes that there are system-
atic mappings between a source and target domain,
but it goes a step further in postulating an under-
lying reason for that mapping. The CM Model pre-
dicts that conventional metaphors, novel metaphors
that follow the mapping principle and novel meta-
phors that don’t follow the mapping principle will
be rated differently on interpretability and accept-
ability scales when other factors, such as frequency
are controlled for. This was, in fact, found to be the
case (Ahrens 2002). Other theories of metaphor
processing such as Gentner’s Structure Mapping
Model (Gentner and Wolff 2000), or the Attribu-
tive Categorization Hypothesis (McGlone 1996) do
not distinguish between novel and conventional
metaphors, nor do they suppose that there might be
different types of novel metaphors.
The CM model of metaphor presupposed
structured shared source domain knowledge. For a
mapping to be conventionalized and understood by
speakers, the content and structure of the source
domain knowledge must be a priori knowledge
and should not have to be acquired. How to define
and verify such structured knowledge is a chal-
lenge to this theory. We attempt to meet this chal-
lenge in two ways: first, by assuming that source
domain knowledge representation is instantiated by
a shared upper ontology, such as SUMO. If the
source domain knowledge representation is indeed
ontology-based, we can adopt the null hypothesis
that the mapping principle is based on one of the
inference rules encoded on that particular concep-
tual node. In consequence, we can take the second
step by examining actual mappings of linguistic
expressions in corpora, and extract the most fre-
quent mappings to verify the null hypothesis. This
will also allow us to investigate if it is the case that
frequency of use in a newspaper corpora necessar-
ily reflects the underlying mapping principle, an
issue which is currently open to interpretation.
The integration of an upper ontology to the
CM model has the following theoretical implica-
tions:
First, the source domain knowledge repre-
sentation is now pre-defined and constrained. Sec-
ond, the validity of such hypothesis will in turn
support the robustness and universality of the pro-
posed upper ontology.
3 SUMO
SUMO (Suggested Upper Merged Ontology –
http://ontology.teknowledge.com) is a shared upper
ontology developed by the IEEE sanctioned IEEE
Standard Upper Ontology Working Group. It is a
theory in first-order logic that consists of approxi-
mately one thousand concepts and 4000 axioms. Its
purpose is to be a shared and inter-operable upper
ontology (Niles and Pease 2001, Pease and Niles
2002, Sevcenko 2003)  Since ontologies are for-
malized descriptions of the structure of knowledge
bases, SUMO can also be viewed as a proposed
representation of shared human knowledge, and
thus a good candidate for mapping information
about the source domain to the target domain.
What we will look at below is whether the SUMO
conceptual terms and inferences are candidates for
knowledge representation in the source domain. In
order to analyze this, we first need to extract from
a corpora the linguistic terms that are used for
mappings between a source and a target domain.
The application of SUMO in NLP and in proc-
essing of lexical meaning is facilitated by its inter-
face with WordNet. The SUMO interface allows
users to search and map each English lexical
meaning defined in WordNet to a concept node on
the SUMO ontology. Similarly, one can also
search for a Chinese lexical meaning and map it to
a SUMO concept node through a Chinese-English
bilingual translation equivalents database
(http://ckip.iis.sinica.edu.tw/CKIP/ontology/).
4 Corpora Data
In order to test the feasibility of using SUMO to
aid the analysis of Mapping Principles within the
framework of the CM Model, we searched the
Academia Sinica Balanced Corpus, a tagged cor-
pus of over 5 million words of modern Mandarin
usage in Taiwan (available on the Internet:
http://www.sinica.edu.tw/SinicaCorpus/). The
maximum number of responses (i.e. 2000) was
obtained for the word ‘jingji’ (economy) in Man-
darin Chinese. Each of these 2000 was examined
and all metaphorical instances were marked. (A
metaphorical instance is defined as when an ab-
stract concept such as ‘economy’  is discussed in
terms of a concrete concept, such as ‘building’ .)
All instances of concrete concepts were then
grouped into source domains. All source-target
domain pairings that had more than 20 instances
were then examined. In Tables 1-4 below we show
the source domains that were found for jingji
‘economy’  and we give the total number of in-
stances and the number of tokens for each meta-
phor, as well as a proposed mapping principle
based. Also note that the following mappings were
manually analyzed and classified.
We first note that the EP (empirical proto-
type) hypothesis holds up since in all source-target
domain pairings except for in ECONOMY IS
WAR in Table 4. In the remaining three meta-
phors, there is one or two lexical items that is/are
obviously more frequent than the others.
Table 1: ECONOMY IS A PERSON (121 instances)
M.P.: Economy is person because people have a life
cycle and economy has growth cycle.
Metaphor Freq.
Entities Chen2zhang3 (growth) 67
Shuai1tui4 (regres-
sion/decay)
8
Chen2zhang3chi2 (growth
period)
2
Bing4zhuang4 (symptoms) 1
Ming4ma4i (lifeblood) 2
Quality Shuai1tui2 (weaken and de-
generate)
1
Functions Chen2zhang3 (grow) 21
Shuai1tui4 (to become
weaker)
5
Fu4shu1 (regain conscious-
ness)
9
E4hua4 (deteriorate) 4
Hui1fu4 (recover) 1
Thus, for ECONOMY IS A PERSON, the map-
ping principle is postulated to have to do with the
life cycle of a person (and not, for example, the
mental health of a person) because of the frequent
occurrence of the lexical item ‘chengzhang’
(growth).
Table 2: ECONOMY IS A BUILDING (102 in-
stances)
M.P.: Economy is building because buildings involve a
(physical) structure and economy involves an (abstract)
structure.
Metaphors Frequency
Entities jianshe (construction) 39
jiegou (structure) 20
jiqu (foundation) 15
zhichu (pillar) 1
genji (foundation) 2
guimo (model) 5
chuxing (model) 1
Qualities wengu (firm) 2
wending (stable) 8
Functions chongjian (re-build) 9
In the case of ECONOMY IS A BUILDING the
mapping principle is postulated to having to do
with structure, and not for example, leaky plumb-
ing. This is an interesting case because, as men-
tioned above, Ahrens (2002) examined IDEA IS A
BUILDING and postulated that the mapping prin-
ciple also had to do with structure (i.e the structure
of a building and the structure of ideas). As Ahrens
(2002) points out, it is not always the case that dif-
ferent target domains use the same aspect of a
source domain. For example, the source domain of
FOOD is used differently for IDEAS (to express
the notion of digestion and processing) as com-
pared with LOVE which uses FOOD to compare
different tastes to different feelings.
Table 3: ECONOMY IS A COMPETITION (40 in-
stances)
M.P.: Economy is competition because a competition
involves physical and mental strength to defeat an op-
ponent and an economy requires financial strength in
order to prosper against other economies.
Metaphors Frequency
Entities shili (actual strength) 14
jingzheng (competition) 12
jingzhengyoushi (advantage
in competition)
3
ruozhe (the weak one) 2
jingzhengli (power of com-
petition)
3
ruoshi (a disadvantaged
situation)
1
qiangguo (a powerful nation) 1
douzheng  (a struggle) 2
tuishi (a declining tendency) 1
Function shuaibai (to lose) 1
Thus, for ECONOMY IS A COMPETITION, the
emphasis is on the strength of participant in order
to defeat the opponent.
Table 4: ECONOMY IS WAR (23 instances)
M.P.: Economy is war because war involves a violent
contest for territorial gain and the economy involves a
vigorous contest for financial gain.
Metaphors Frequency
Entities qinglue (invasion) 4
zhan (battle) 2
laobing (veteran) 1
gungfangzhan (defend and
attack battle)
1
chelue (tactics) 1
daquan (immense power) 4
Qualities qianchuangbaikong (one
thousand boils and a hundred
holes; holes all over)
1
Functions quanlichongchi (to dash with
full force)
1
guashuai (to take command) 5
(daquan) chaozai shoushang
(to grasp the power)
1
xisheng (sacrifice) 1
Xishengping (victims) 1
In ECONOMY IS WAR, there is no clear-cut in-
stance of a frequent mapping. We suggest that this
is because WAR is a subset of the source domain
of COMPETITION (i.e. a violent contest) in the
SUMO representation, as discussed below.
In short, the corpora data support the CM
model’ s hypothesis that there is a subset of lin-
guistic expressions within a particular source do-
main that map to a target domain. It is not the case
that ‘anything goes’ . In fact, the corpora data pre-
sented above, suggest an even more restricted view
– that there are usually one or two linguistic ex-
pressions that frequently map between the source
and target domains and ‘drive’  the motivating re-
lationship between them. In the next section, we
look at whether or not the source domain knowl-
edge can be defined a priori through an upper on-
tology such as SUMO.
5 Defining Source Domain Knowledge
with Shared Upper Ontology
The research on Shared Upper Ontology offers a
potential answer to the challenge of how to define
and verify the structured knowledge in a source
domain. A shared upper ontology is designed to
represent the shared knowledge structure of intelli-
gent agents and allows knowledge exchange
among them. In computational application, it is an
infrastructure for knowledge engineering. In cog-
nitive terms, we can view it as a candidate for he
description of shared human knowledge. In this
paper, we adopt SUMO.
In SUMO, conceptual terms are defined
and situated in a tree-taxonomy. In addition, a set
of first order inference rules can be attached to
each conceptual node to represent the knowledge
content encoded on that term. The conceptual
terms of SUMO are roughly equivalent to the
source domains in MP theory. Hence the well-
defined SUMO conceptual terms are candidates for
knowledge representation of the source domain in
the MP theory of metaphor. In other words, SUMO
provides a possible answer the question of how
source domain knowledge is represented and how
does this knowledge allows the mapping in con-
ceptual metaphors. We examine how this might be
possible by looking at two conceptual terms that
are represented in SUMO that related to our source
domains – CONTEST and ORGANISM.
Economy is Contest
First, we found that what we intuitively termed as
‘competition’  above has a corresponding ontologi-
cal node of Contest. The term Contest is docu-
mented as ‘A SocialInteraction where the agent
and patient are CognitiveAgents who are trying to
defeat one another.’  Its only inference rule is
quoted here:
 (=> (instance ?CONTEST Contest) (exists
(?AGENT1 ?AGENT2 ?PURP1 ?PURP2) (and
(agent ?CONTEST ?AGENT1) (agent ?CONTEST
?AGENT2) (hasPurposeForAgent ?CONTEST
?PURP1 ?AGENT1) (hasPurposeForAgent
?CONTEST ?PURP2 ?AGENT2) (not (equal
?AGENT1 ?AGENT2)) (not (equal ?PURP1
?PURP2)))))
The knowledge inference rule stipulates that each
instance of Contest is carried out by two agents,
each has his own non-equal purpose. This is ex-
actly the source knowledge needed for the meta-
phor mapping. When the conceptual metaphor is
linguistically realized, lexical expressions are then
chosen to represent the conceptual terms of both
purposeful agents, and conflicting purposes for the
agents. Notice that in contest, as in economy, it is
not necessary to have only one winner. There may
be multiple winners and perhaps no winners. In
other words, the agents’ purpose may not be con-
flicting. But the purposes-for-agent are definitely
different for each agent.
In addition to the 40 instances of economy
metaphors involving contest. There are also 23
instances of metaphors involving War. In these
cases, it is interesting to observe that the central
concept is still the conflicting purposes (one’ s gain
is another’ s loss) of the warring party. This is con-
firmed by the shared ontology. In SUMO, a War is
a kind of ViolentContest, which in term is a kind
of Contest.
Contest—ViolentContest—War
The term War is defined as ‘A military confronta-
tion between two or more Nations or Organizations
whose members are Nations.’  And the term Vio-
lentContest is defined as ‘Contest where one par-
ticipant attempts to physically injure another
participant.’  As can be seen from the definition and
the metaphoric uses involving War, the ontological
source domain knowledge is not involved.
In fact, when examined more closely, it is
clear that when the domain knowledge of War is
used, it either further specifies the conflicting pur-
poses by elaborating on the quality and manner of
the conflict, or elaborating on the agent partici-
pants as combatants. In other words, Economy is
War is not a different mapping. It is subsumed un-
der the mapping of Economy is Contest, and added
elaborations on the participants.
By carefully examining the mapping from
source domain knowledge based on SUMO, we
discovered that not only mapping is indeed based
on a priori source domain knowledge. We also dis-
covered that a metaphor can often involve addi-
tional and more specified terms within a domain.
In these cases, no additional mapping is required.
The same structured domain knowledge is used,
and the subsumed terms offers only elaborations
based on the same knowledge structure.
It is appropriate to note here that based on
WordNet to SUMO mapping, economy is a So-
cialInteraction, and Contest is a subclass of So-
cialInteraction. In other words, economy is a
related concept to Contest, although it does not
belong to that conceptual domain. That a metaphor
chooses a related domain is to be expected.
Economy is Organism
Among metaphors involving economies,
one source domain stands out as being far removed
conceptually. These are the metaphors involving
Organism. We arrived at this conclusion by re-
examining the examples that we generalized as
Economy is a Person in the previous section. After
closer examination with the help of SUMO knowl-
edge representation, we found that the linguistic
realizations of this mapping do not involve any
knowledge that is specific to Human. In fact, it
only involves the notion of a life cycle, which is
the defining knowledge involving an Organism.
Organism is defined in SUMO as ‘a living
individual, including all Plants and Animals.’  And
the crucial knowledge encoded in of the attached
inference rules follows:
=> (and (instance ?ORGANISM Organism) (agent
?PROCESS ?ORGANISM)) (holdsDuring
(WhenFn ?PROCESS) (attribute ?ORGANISM
Living)))
The above inference rule encodes the knowledge
that ‘An organism is the agent of a living process
that holds over a duration.’  In other words, having
a life cycle is the defining knowledge of an Or-
ganism. This turns out to be the source domain
knowledge that is involved in the mapping.
It is interesting to observe, though this is
not encoded by SUMO, that from a Darwinian per-
spective, the Purpose of an Organism as an Agent
is to prolong his own life cycle. We found that in
actual linguistic data, when the above two meta-
phors are used simultaneously, it is only when im-
proving the life cycle (Economy is Organism) is
incorporated as the PurposeForAgent (Economy is
Contest). In other words, the source domain
knowledge is robust in conceptual metaphor and
can be automatically mapped to and merged.
6 Conclusion
In this paper, we propose an ontology-based and
corpus-driven approach towards predicting lexical
meaning of conceptual metaphors. Our theory is
thus formally constrained. We also verified our
findings with examination of corpora data. In the
final version of this paper, we will demonstrate
how the process of establishing mapping principles
and deriving metaphorical meaning can be semi-
automaticized based on both the SUMO ontologi-
cal databases and corpora data. Such a process has
important implications both in cognitive explana-
tion of conceptual metaphors and in the application
of SUMO to predict figurative meaning in meta-
phorical uses.
Acknowledgments
This study is partially supported both by a NSC
project ’Sense and Sense-Ability’, as well as a
NDAP project ’Linguistic Anchoring.’ We would
like to thank Adam Pease of Teknowledge, the
ACL workshop reviewers, as well as colleagues of
the two above projects, for their comments. Any
remaining errors are our own.

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