From Lexical Semantics to Conceptual Metaphors: Mapping
Principle Verification with WordNet and SUMO
Kathleen Ahrens
National Taiwan University
No. 1, Sec. 4, Roosevelt Road
Taipei, 106 Taiwan
ahrens@ntu.edu.tw

Siaw-Fong Chung
National Taiwan University
No. 1, Sec. 4, Roosevelt Road
Taipei, 106 Taiwan
claricefong6376@hotmail.com

Chu-Ren Huang
Academia Sinica
No.128, Sec. 2, Academia
Road, Nankang
Taipei, 115 Taiwan
churen@sinica.edu.tw

Abstract

The goal of this paper is to further develop methods for verifying Mapping Principles between
source and target domain pairings of conceptual metaphors. Previous work (Ahrens, Chung &Huang, 2003) integrated the Conceptual Mapping Model with an ontology-based knowledge
representation (i.e. SUMO) in order to demonstrate that conceptual metaphor analysis could be
restricted and eventually, automated. However, that study relied on an operational definition that
required a large number of instances of source-target domain pairings in order to determine themost frequent mapping. In this study, we further operationalize the frequency-based definitionand examine the possibility of using information derived from WordNet and SUMO to verify
Mapping Principles for source-target domain pairings that do not occur frequently enough (if atall) in a corpus.
1

Introduction

Lexical resources are central to any natural language processing system and the plethora of multi-lingual
resources such as EuroWordNet (EWN) and Academia Sinica’s Bilingual Ontological WordNet (BOW)
are based firmly in the lexicon. Recent work in figurative language processing, however, has pointed out
shortcomings of these resources for processing of conceptual metaphor. Veale (2003), for example,
points out that determining the aptness is dependent upon parameters that are, as yet, hidden. Lonneker(2003) decries the lack of relationship between literal and figurative synsets in WordNet, while Alonge
and Castelli (2003) suggest that the EWN Top Ontology needs to be extended with more concepts inorder to deal with figurative language, since at this point in time it only notes general semantic
distinctions.
Ahrens (2002), taking an intuition-based approach, proposed that the linguistic expressions that areused 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 expressions havebeen analyzed, an underlying reason for these mappings can then be postulated. This study relied onnative speaker intuition regarding what is mapped conceptually from the source to the target domain.
For example, in the four examples from the metaphor LOVE IS PLANT, given below, the Mapping
Principle (MP) of “Love is understood as plant because plants involve physical growth and love
involves emotional growth” was extracted based on the fact that all the examples in some way had to dowith growth.
1. (a) liang ren de ai miao zuijin cai gang mengya
two people MOD love seedling lately just recently sprout‘Their love just begins to sprout lately.’(b) wo dui tade ai-yi jianjian zizhang
I for his love gradually grow
‘My love for him has grown gradually.’(c) aiqing xuyao xinqin
de quanqai
love need industriously water‘Love needs to be watered industriously.’
Ahrens, Chung & Huang (2003), followed up on this study and presented a corpus-based approachto determining the systematicity between source and target domain pairings (i.e. Mapping Principles(MPs)). They propose that each source-target domain pairing will have a prototypical instance of
mapping as indicated by a lexical item that is frequently mapped, as compared with other mappings. In
addition they use the Suggested-Upper-Merged-Ontology (SUMO) to delimit the source domain
knowledge. SUMO has the advantages of being able to be used to infer knowledge through automatic
reasoning as well as to constrain the falsifiability of the MP.
For example, when they looked at the metaphor ECONOMY IS A BUILDING, they find 102
instances (in their randomly extracted corpora of 2000 instances from Sinica Corpus) of thissource-target domain pairing. Of these instances, 39 are mapped with the lexical item jianshe‘construction’, and the suggested Mapping Principle is therefore: Economy is a building because
buildings involve a (physical) structure and economy involves an (abstract) structure (p. 38).” However,clear numerical criteria for determining the minimal number of tokens necessary, as well as thepercentage of tokens necessary to reach consensus for a mapping principle was not established. In
addition, if enough conventional metaphor examples cannot be found in the corpora, there is currently
no way to verify the intuition-based mapping principle. Thus, the determination of mapping principles
by means of frequency needs to be researched further. In this paper, then, we will create guidelines fordetermining when MPs can be established on the basis of frequency, as well as extend Ahrens et al.’s(2003) scope and examine the possibility of using information derived from WordNet and SUMO toverify MPs for source-target domain pairings that do not occur frequently enough in a corpus.
2

Using Frequency to Verify Mapping Principles

In this section, we attempt to determine frequency criteria for verifying MPs (Table 1). Two thousandexamples were extracted for ECONOMY metaphors from the Academia Sinica Balanced Corpus(http://www.sinica.edu.tw/SinicaCorpus/), and then manually sorted into metaphorical and literalexamples. In addition, we also look at data on STOCK MARKET extracted from on-line newspapers in
Taiwan (Chung, Ahrens & Sung 2003).
Table 1: Total Number of Metaphorical Instances Extracted from 2000 Samples
Metaphor

Total # of
TokensMost Frequent Percentage

MPs match

ECONOMY IS A PERSON

121

88

72% Yes

ECONOMY IS BUILDING

102

39

38% Yes

STOCK MARKET IS OCEAN WATER

58

53

91.00% Yes

ECONOMY IS COMPETITION

40

14

35% Yes

STOCK MARKET IS WAR

25

2

8% Not clear

STOCK MARKET IS A BALL

24

17

ECONOMY IS WAR

23

5

21% Not clear

STOCK MARKET IS A PERSON

192

10% Not clear

STOCK MARKET IS EARTHQUAKE

17

16

94.12% Yes

STOCK MARKET IS DISASTER

16

1168.75% Yes

ECONOMY AS JOURNEY

155

33% Yes

ECONOMY IS AN AEROPLANE

10

8

80% Yes

71.00% Yes

The second column in Table 1 lists the total number of metaphorical examples found for each source
domain. Next, examples with the same lexical item were counted. For example, in ECONOMY IS APERSON, ‘chengzhang’ growth is found 88 times or in 72% of the data. In ECONOMY IS A
BUILDING, ‘jianshe’ construction occurs 39 times (or in 38% of the data). In both these cases (and in
all the shaded cases in the table) the most frequent lexical item is related to what was originally
postulated as a Mapping Principle on the basis of linguistic intuition.
However, in the three non-shaded cases, the relationship between the frequency of the lexical items
and the Mapping Principle is not straightforward. For example, in STOCK MARKET IS WAR, no one
lexical item receives priority in terms of frequency. In ECONOMY IS WAR, there are five instances of‘gua-shuai’ to take position of the commander in the army, but this does not clearly relate to the MP of“Economy is war because war involves a violent contest for territorial gain and the economy involves avigorous contest for financial gain.” Therefore, we propose that the following two conditions be met fora frequency-based account to be valid: First, at least 10 tokens must found and, in addition, at least 30%of these tokens must be of one particular lexical item.
3

Using WordNet and SUMO to Verify Mapping Principles

In the cases where there are fewer than 10 instances found in the corpora, or in cases where thepercentage of a single lexical item is less than 3o%, we need to find another method to verify MPs. Our
goal is aided by studies linking the lexicon in WordNet and SUMO (http://ontology.teknowledge.com,
Niles & Pease, 2003). After the links are established, the ontological nodes in SUMO provide hyperlinks
to their semantic definitions by WordNet. Also facilitating this analysis is the work of Huang (2004) inwhich all the nodes in SUMO are available in both Chinese and English through the Academia Sinica
Bilingual Ontological WordNet (http://bow.sinica.edu.tw).
We examine two instances here: LOVE IS A PLANT (Table 2) and LOVE IS FOOD (Table 3).
Table 2: LOVE IS PLANT Definitions from WordNet and SUMO
WordNet
Items
Explanation
Senses萌芽
2: sproutgrow sprouts, of a plant

SUMO Category

苗

1: seedling長

1: grow

Growth(生長)FloweringPlant
young plant or tree grown from a seed
(開花植物)
come to have, of physical features and attributes Growth(生長)灌溉

1: waterpour water on

Wetting(濕潤)開花

1: bloom

produce or yield flowers

Growth(生長)Ahrens (2002) proposes the following MP for LOVE IS A PLANT: “Love is understood as plant
because plants involve physical growth and love involves emotional growth.” Since corpora searches do
not come up with any instances of this metaphor, it is difficult to ascertain the validity of this principle.
We therefore propose looking at the 1) WordNet sense, 2) the WordNet definition, and the 3) SUMO
node for the WordNet sense (Niles & Pease 2003) for the intuition-based examples in order to see if
there are any semantic overlaps within, or between, these three types of information. Table 2 shows thatthe word “Growth” appears three times in the SUMO category, out of the five examples. “Grow” also
appears three times in the sense and definition columns from WordNet. Thus, the combination of
WordNet information and the SUMO representation agrees with the MP originally given.
In another example that has less than ten corpora examples, LOVE IS FOOD (Table 3), both the
WordNet information and the SUMO information again matches up with the Mapping Principle
suggested in Ahrens (2002), that “Love is understood as food because food has different tastes as love
involves different feelings.” Table 3 shows that taste is mentioned five times in the WordNet sense anddefinition, and three out of four times in the SUMO category. Thus, determining the number ofoverlapping lexical items in WordNet definitions and SUMO categories to verify Mapping Principles
seems to hold promise for instances where there are not enough exemplars to make a judgment based on
frequency alone.

Table 3: LOVE IS FOOD: Definitions from WordNet and SUMO
WordNet
Items
Explanation
Senses
滋味
1: tastedistinguishing a taste by means of the taste buds
one of the four basic taste sensations; sharp and苦1: bitterdisagreeable; like the taste of quinine
味道
1: tastedistinguishing a taste by means of the taste buds吃
4

1: eatingthe act of consuming food

SUMO Nodes
Tasting(嚐)TasteAttribute(味覺屬性)
Tasting(嚐)Eating(吃)Testing the Verification Principle

In Section 2, we pointed out that there were three instances in the STOCK MARKET and ECONOMY
metaphors that did not meet the 30% criteria. We look at these three metaphors below in order todetermine if WN and SUMO can be used to verify the MPs.
Table 4: ECONOMY IS WAR: Definitions from WordNet and SUMO
WordNet
Items
Explanation
SUMO Nodes
Senses侵略the act of invading; the act of an army that invades ViolentContest
4: invasion(暴力性的競爭)for conquest or plunder戰
War (戰爭)1: warthe waging of armed conflict against an enemy
老兵
a serviceman who has seen considerable active SocialRole
1: veteranservice(社會角色)
攻防戰x
策略
the act of concealing yourself and lying in wait to ViolentContest
6: ambush(暴力性的競爭)
attack by surprise
大權
x
千瘡百孔
全力(衝刺) x
掛帥
大權…操
在…的手
上
犧牲
犧牲品

x
x
Killing(殺害)
1: sacrifice kill or destroy
personnel that are sacrificed (e.g., surrendered or
1: sacrifice
Human(人類)lost in order to gain an objective)

Table 4 shows that there are three instances of the concept of ‘Invasion’ found in the WN definitions,but they are all in the same definition. An alternate hypothesis is that ‘ViolentContest’ is the criticalissue since it occurs in the SUMO nodes of two different words. (The blank cells indicate that this word
was not found in BOW and so does not have a listed sense or associated SUMO node.) In addition,
Ahrens et al. (2003) noted that ECONOMY IS WAR is a subset of the ECONOMY IS CONTEST
metaphor, with the MP of ‘Economy is war because war involves a violent contest for territorial gain
and the economy involves a vigorous contest for financial gain.’ Moreover, the SUMO node of WAR islinked to ViolentContest. This example demonstrates that not only do we need to have an expansion of
the SinicaBow to link to more items in WordNet, we also need to expand our notion of semantic space toinclude related Sumo nodes. In sum, our current analysis suggests that the previous MP was correct.
Table 5 gives instances of STOCK MARKET IS WAR. Although different lexical items are mapped
as compared with ECONOMY IS WAR, the mapping of the concept of ‘contest’ is the same.

Table 5: STOCK MARKET IS WAR: Definitions from WordNet and SUMO
WordNet
Items
Explanation
SUMO Nodes
SensesWar (戰爭)1: warthe waging of armed conflict against an enemy
戰the act of concealing yourself and lying in wait to ViolentContest
6: ambush
策略(暴力性的競爭)
attack by surprisex
焦土1:
an explosive mine hidden underground; explodes
Weapon (武器)地雷land_mine when stepped on or driven overx
攻勢
1:
a place (as at a frontier) where travellers are
LandArea (陸地)關卡checkpoint stopped for inspection and clearancex
大關
防衛

2: defend

功臣

x

全軍覆沒

x

殺空

x

撤出

x

殺出

x

上(攻)

1: attack挑戰
撤離

be on the defensive; act against an attack

Contest (競爭)take the initiative and go on the offensive: "TheSerbs attacked the village at night"

Contest (競爭)4:a call to engage in a contest or fightRequesting (請求)challenge1:
the act of evacuating; leaving a place in an orderly
Motion (移動)evacuation fashion; esp. for protection

Thus, the proposed MP is the same; ‘Stock market is war because war involves a violent contest for
territorial gain and the stock market involves a vigorous contest for financial gain.’ Note that this does
not violate the Mapping Principle Constraint proposed in Ahrens (2002) since two different target
domains are recruiting the same source domain for the same reason. The Mapping Principle Constraint
says that ‘A target domain will select only source domains that involve unique mapping principles.’ This
constraint applies to the source domains that are selected by a single target domain.
The last metaphor in our study that does not meet the frequency requirement of 30% is STOCK
MARKET IS A PERSON. Although no one lexeme predominates, variants of the word ‘life’ occur
several times. It is interesting to note that although in the previous examples a case could be made forlooking solely at the SUMO nodes (cf. Tables 2-5 above), in this instance, only the words used in the
WN definition are found to coalesce around a concept. In this instance, we suggest that the MP is: Stockmarket is a person because people have varying degrees of physical activity (liveliness) and the stock
market has varying degrees of financial activity. It may also be beneficial to look at whether analyzing
the relationship between the words given in the definitions (i.e. if some words are near-synonyms)
would aid in determining the underlying reason for the source-target domain pairing. However, anotherpossibility is that this source domain is too broad or not clearly delimited. In fact, one step that needs tobe taken in this regard is to incorporate the steps that Chung, Ahrens and Huang (2004) take to delimit
source domains before analyzing MPs.

Table 6: STOCK MARKET IS A PERSON: Definitions from WordNet and SUMO
WordNet
Items
Explanation
SUMO Nodes
SensesBiologicalAttribute (生liveliness and energy
4: pep活力物屬性)any break in the skin or an organ caused by violence
Injuring(傷害)2: wound創傷
or surgical incisionpass from physical life and lose all bodily attributes
Death(死亡)
2: die瀕 (死) 的and functions necessary to sustain life
x
敏感的
x

疲軟

12: spur

give heart or courage toIntentionalPsychological
Process(意向性心理歷程)

cause to become alive again

OrganismProcess (生命歷程)the front of the head from the forehead to the chinand ear to ear

BodyPart (軀體部件)

return to consciousness: "The patient came toquickly"

BiologicalProcess (生物
歷程)

激勵
輸血
起死回生

x
1:
resurrect

1: face
變 (臉)
應聲倒地,
x
應聲而倒
復甦

1: come_to

失血

x

5

Conclusion

We propose the following steps to determine Mapping Principles: First, if the number of tokens of aconceptual metaphor is greater than 10, and the highest number of tokens mapped is greater than 30% of
all tokens, then this lexical item is the basis for the MP. Second, if either the first or second criteria are
not met, determine the sense of each item in WordNet (extracted through SinicaBow) and the associated
SUMO category for that particular sense. Next calculate the number of times that a particular lexical
item is used in the WN sense, explanation, or linked SUMO category and determine if that lexical item isrelated to what intuitively seems to be mapped between the source and target domains in the example
sentences.
Further research will focus on examining whether it is necessary to extend the parameters of thesecond step to include synonyms, thus expanding the semantic space by which a MP can be determined.
In addition, the biggest hurdle this type of account faces is that the WN sense must currently be
stipulated manually, although we hope that the implementation of semantic tagging can overcome thisproblem in the future. We hope that the line of research discussed herein will stimulate more research in
how a computational approach can help set parameters for determining metaphorical sense and point theway to the application of creating a systematic relationship between literal and figurative synsets in
WordNet.
Acknowledgements

We would like to thank the CLSW-5 reviewers for their comments on this paper and NSC grant#NSC92-2411-H-002-076-ME for supporting the research discussed herein. Any remaining errors are
the sole responsibility of the authors.

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