Why Wikipedia needs to make friends with WordNet
Kow Kuroda,∗ Francis Bond,∗∗,∗ Kentaro Torisawa∗∗ National Institute of Information and Communications Technology (NICT), Japan
3-5 Hikari-dai, Seika-cho, Sooraku-gun, Kyoto, 619-, Japan∗∗ Linguistics and Multilingual Studies, Nanyang Technological University, Singapore

1 Introduction
1.1 Does Wikipedia dispense with theneed for WordNet?
Data of various kinds acquired from
Wikipedia is gaining popularity in NLP
and related areas of research. For one reason,
Wikipedia provides us with data with broad
coverage. Its coverage is so broad that noother freely available linguistic resource
can match it. It is often claimed that thisis evidence for the triumph of “collective
intelligence.”
Radical enthusiasts of Wikipedia evengo on to claim that researchers in NLP
and SemanticWeb no longer need WordNet(WN) (Fellbaum, 1998).1) They allude to
the superiority of Wikipedia-derived data overmanually crafted data like WN in terms of development ease, speed, and cost as well as
coverage. WN comes with precision endorsedby psychological reality that most WWWderived data lacks, but some people also tend
to criticize the subjective nature of the word
senses that WN specify, no matter how ﬁnegrained its sense distinctions are. All in all,they seem to try to dismiss WN-like lexical
resources by suggesting that they are outdated
in the age of WWW. And here comes the crucial question, Does Wikipedia dispense with
the need for WordNet?
In this paper, we argue that the answer is1) In

http://www.mkbergman.com/417/99-wikipediasources-aiding-the-semantic-web/ (retrieved on 2009/12/01)
for example, you can ﬁnd a bold claim like: “Wikipedia
has arguably replaced WordNet as the leading lexicon forconcepts and relations. Because of its scope and popularity,
many argue that Wikipedia is emerging as the de facto
structure for classifying and organizing knowledge in the
21st century.”.

No, suggesting that we should make a goodcompromise. We show that lexical hierarchies
derived from the Japanese Wikipedia are notas well articulated as the upper ontology ofJapanese WordNet. This allows us to presume
that the hypernym set of language L obtained
from the Wikipedia of L is poor compared to
the WN of L. Under this assumption, the WN
and the Wikipedia of language L are best understood to be complementary in the following way: The WN of L speciﬁes the mapping
between an upper ontology to lexical items,
w1 , w2 , . . . , wn , of L. The conceptual hierarchies distilled from the Wikipedia written in L
specify links to “named entities” described in
w1 , w2 , . . . , wn of L.
1.2 Organization
This paper is organized as follows. In §2
we describe how we processed the hypernymhyponym pairs acquired from the Japanese
Wikipedia by Sumida et al. (2008). In §3,
we show how the hypernyms obtained in theway speciﬁed in §2 were linked to lemmasof Japanese WordNet (WN-Ja) (Bond et al.,2008; Bond et al., 2009). In §4 we discuss theimplications. Finally, in §5, we state tentative
conclusions
2 Acquiring taxonomic hierarchies from
Wikipedia hypernyms
Recently, we ﬁnished the manual cleaningof approximately 67,000 Japanese hypernym
hierarchies paired with roughly 900,000 hyponyms. We show some details of this process in §2.4. The original data, comprising roughly 2,400,000 hypernym-hyponym
pairs, was automatically compiled from theb. h1 : member
h2 : former member
h3 : former member of Floyd
h4 : former member of Pink Floyd
I: Syd Barrett

Japanese Wikipedia (Sumida et al., 2008).
They used Support Vector Machines (Vapnik, 1995) to classify the acquired data. The
hypernym-hyponym pairs extracted by Sumida et al. (2008) do not only consist of links
between Wikipedia entries, but also consider
noun phrases extracted from the text of the
Wikipedia entries themselves.
While the data thus acquired has an impressive coverage, it is noisy and unreliable
at least in the following two senses: First,
both hypernyms and hyponyms can be misparsed phrases, due to the low performancesof a Japanese tokenizer2) . Second, even correctly parsed phases can have hypernyms thatare themselves relational nouns (such as kind,member): they suffer from “semantic unsaturatedness” in the sense of Kuroda et al. (2009)
and fail to serve as good hypernyms.

Pairs (H; I), where H = h1 , . . . , hn , are automatically generated from such pairs (hmax ; I).
We refer to H as the “hypernym path” for I,4)
and to units like h1 , h2 , . . . , hn as the “pathelements” of H. A hypernym path may contain: (i) bare nouns (e.g., singer), (ii) modiﬁed nouns (e.g., famous British rock singer,
former member), or (iii) noun phrases.
We are able to create these paths in this way
because we are looking speciﬁcally at hypernym relations. Removing a modiﬁer broadens
the denotation, and thus gives a hypernym of
the more restricted term.

2.1 Extracting “base” hypernyms

2.2 Problems with automation

The data we processed resemble the following, where h is the hypernym and I is the instance (or hyponym):3)

Paths like the ones above were constructed byautomatically removing modiﬁers from hn (inJapanese) one by one. This operation is not
error-free. Manual cleaning was performed toeliminate unconventional and/or unacceptable
units like former member of Floyd, h4 of (2b)which was produced by the automatic simpliﬁcation. We could not use a Named Entity
tagger for this task as it performed to poorly
on the isolated noun phrases.

(1)

a. h: famous British rock singer
I: Peter Gabrielb. h: former member of Pink Floyd
I: Syd Barrett

The set of hypernyms extracted form
Wikipedia consist mainly of complex NPs likefamous British rock singer and former member of Pink Floyd. Clearly, terms like these
are not ideal hypernyms. Thus, we tried to extract “base” hypernyms by gradually removing modiﬁers from the complex NPs. In thisway, the two pairs in (1) are converted into thefollowing hierarchies:(2)

a. h1 : singer
h2 : rock singer
h3 : British rock singer
h4 : famous British rock singer
I: Peter Gabriel

2) The precision of Japanese tokenizers at the state-of-theart come close to 98% against newspaper articles, but theyshow much lower precisions against web-text.
3) Although we work on Japanese data, we present the English translations in this paper as the semantic phenomena are
not language speciﬁc.

2.3 What terms make good hypernyms?:
Effect of semantic saturatedness
During the manual process of cleaning, it also
became apparent that checking for the conventionality of path elements alone was noteffective. We also needed a systematic treatment of composite units like former member to take care of the function of modiﬁers.
However, lexical databases like WordNet are
not guaranteed to contain composite, phrasal
units like former X (X={ member, president,. . . }). This means that we cannot rely on lexical resources to distinguish valid phrases frominvalid ones which are only theoretically possible.
4) For both practical and theoretical purposes, we did not
distinguish between instance and hyponym relations.

This was a problem because raters we hired
showed confusion as to the conventionality ofsuch terms and disagreed in their ratings. To
them, terms like member made good terms
even if they were presented in isolation, but
terms like former member did not. When they
were presented in isolation, most raters hesitated to rate them as good hypernyms.
Part of the reason for this disturbance canbe attributed to the semantic unsaturatednessof units like former member, but the situation seems more complex. Interestingly, raters
showed little disagreement on the goodness ofmember, which is also a semantically unsaturated noun. So, the real reason for rater’s trouble in classiﬁcation is not a term’s semantic
unsaturatedness alone. Consequent researchsuggests that frequency takes effect on this:frequent semantically unsaturated nouns tend
to be classiﬁed as saturated nouns.
In passing, it deserves a brief mention thatmost linguists’ tacit assumption that relational
nouns are relatively rare and exceptional, andthat their set is closed seems far from wellgrounded. The assumption would be true of
simple nouns, but it is not true of composite
nouns with modiﬁers. The source of unsaturatedness of composite nouns are their modiﬁers. For example, former caused the effectin the example above. It is easy to provide
similar examples: the unsaturatedness in sister city comes from a metaphorical sense ofsister: city is arguably a saturated noun, butsister city is unsaturated because sister adds
unsaturatedness to it. This is why it is allowed
to say “X and Y are sister cities” or “X is the/asister city of Y .”We can add examples with more complexity. For example, disciple is a semanticallyunsaturated noun. In the combination fellow
disciple(s), fellow adds another unsaturatedness. This is why it is allowed to say “X andY are fellow disciples under Z” or “X is the/afellow disciple of Y (?under Z)” and why weinfer that both X and Y have a common master, Z when we hear such expressions.
Notably, the unsaturatedness for fellow

and disciple can co-exist.5)
Cases likethis show that unsaturatedness accumulates
through modiﬁcation.
Another class of cases show that unsaturatedness is composable, allowing the unsaturatedness of one noun to get “bridged” to another. In cases like secretary of the Minister (of Foreign Affairs), unsaturatedness is reduced through variable-binding, because secretary of X, X is bound to the Minister (of Y ),and when Y is bound to Foreign Affairs (withthe aid of of ), it gets saturated; otherwise, it
stays unsaturated.
Examples of the sort brieﬂy mentioned
above strongly suggest that the semantics ofmodifers is rather complex and needs serious
investigation. It is not guaranteed that proper
analysis of modiﬁers is possible within a natural extension of the semantics of individual
words, partly because analogy, metaphor andmetonymy play a crucial role. Note that modiﬁers undergo (often very subtle) semantic extensions: at least, sister is not used in its literal
sense in sister cit(y|ies).6) With this fact inmind, it would be safe to assume that semantic
unsaturatedness become more serious at thelevel of composite nouns or noun phrases. In
fact, they posed a challenge in the cleaning
process to be explained below.
2.4 Path element cleaning in some detail
The cleaning proceeded roughly in the way
described below.
Step 1: Fully automatic generation of
hypernym paths
First, all hypernyms were morphologically analyzed with a morphologicalanalyzer/tokenizer for Japanese.7) This gave
5) Note here that a mutuality interpretation of relational
nouns (Eschenbach, 1993) seems to have an interesting effect on the construction and interpretation of sister cit(y|ies)
and fellow disciple(s).
6) For the case of fellow disciples, the meaning of fellow
can be literal. We can say that two diciples of Z, X and Y , arein the relation of FELLOW- OF - THE - OTHER (X , Y ). But thisis not true of sister cities. We can only say that two cities,
X and Y , are in some relation analoguous to the relation ofsister-of-the-other rather than they are properly in the relationof SISTER - OF - THE - OTHER (X , Y ).
7) We
used MeCab 0.95 (http:///mecab.
sourceforge.net/)with
its
default
dictio-us a set of paths consisting of a series ofmorphemes coupled with part-of-speech(POS) and other information. Based on the
POS information thus provided, we generate
a series of terms that serve as a hypernym
path.8)In this process, however, some problematic
hypernyms were excluded. However, hypernyms with disjunctive semantics were not excluded.
Step 2: Manual evaluation of pathelements
We asked four raters to evaluate each of thepath elements for their conventionality and/or
semantic saturatedness. The criteria used
were:
(3)

a. If a path element X is felt to be
fully conventional and saturated, itshould be classiﬁed as G[ood].
b. If X is felt to be “incomplete” forless conventionality or strong unsaturatedness, it should be classiﬁed as
L[ess Good].
c. If X is felt not to be rather unconventional but the rater cannot be
sure if it is really a nonword, itshould be classiﬁed as D[ubious].
d. If X is felt to be fully unconventional and nonsensical, it should beclassiﬁed as B[ad].

We collected ratings thus created and selected the most appropriate class.9) Admittedly, L and D are mixtures of different subclasses. But we did not attempt to create
proper labels for subclasses.10)
nary IPA Dic.
While our pilot study showed thatthe combination of MeCab with UniDic (http://www.tokuteicorpus.jp/dist/)developedby the National Institute for Japanese Language provided
better results we could not in the end use it due to its
restrictive license.
8) We are afraid the the same automation would not be possible for nonhead-ﬁnal languages because the criteria used
are speciﬁc to modiﬁers that appear before the head noun.
9) The class selection was based on the ﬁrst author’s intuition, though he made use of the distribution information.
This means that the winners were not always the ones that
had acquired most votes. Actually, it turned out that some of
the raters committed systematic errors.
10) The agreement rate in terms of Fleiss’ kappa against asample of 2000 cases was 0.492 under the distinction among

Step 3: Automatic reconstruction of
hypernym paths and ﬁnalization
Because relevant information is distributed
over different paths, they are reconstructedfrom scratch for canonicalization. After this,the ﬁrst author edited the results based on his
intuition. He edited the paths and even added
missing intermediate terms and some abstract(super)hypernyms with D status at the root
(such as 者*, 手*, and 校* to be discussed in§3.3).
In the process of this cleaning, the original
set of roughly 95,000 hypernyms was reduced
to the set of 67,000.
3 Linking Wikipedia hypernyms to
WordNet3.1 Nature of hypernyms and hyponyms
in Wikipedia
Recall that we processed hypernym-hyponym
pairs automatically acquired from theJapanese Wikipedia. The data consists ofroughly 67,000 hypernym hierarchies pairedwith roughly 900,000 hyponyms.
We cleaned up all the hypernyms of thedata, but we did not process the hyponyms
for the following reason: nearly 2/3 of the hyponyms are proper names or named entities.
The amount of knowledge required to determine if such pairings are valid or not goes wellbeyond the personal knowledge of an average
person. At the time we started the cleaning, it
was unclear how to deal with them.
This, on the other hand, suggests an interesting possibility: if pairings of cleanedup hypernyms with hyponyms turn out tobe valid, the huge database of such pairings should complement traditional thesauri
as WordNet (Fellbaum, 1998) which mainly
consist of upper level concepts (by its very
design). With this hypothetical mapping between coarse-grained concepts in the upper
ontology and ﬁner-grained concepts in thelower ontology, we can specify the linkage
G, L, D, and B. This is not so good, but it increased to 0.759 ifclass L was discarded, and it increased up to 0.916 if classes
L and D were uniﬁed as one. This suggests that rater’s classiﬁcation is highly stable over the identiﬁcation of G and B,
and that raters were confused between L and D.

Figure 1: Sample matches (in red) of WN-Ja lemmas and Wikipedia hypernym paths (all inJapanese): terms are separated by “:” /1 and /0 added to term indicate “match” and “unmatch”
from named entities to upper ontology. If thisis possible, it is very promising.
With his hope in mind, we linked the rootsof the hypernym hierarchies cleaned in theway illustrated above to nodes in the Japanese
WordNet (WN-Ja) (Bond et al., 2008; Bond etal., 2009).
WN-Ja is a Japanese translation of WordNet 3.0 developed and maintained at the National Institute for Information and Communications Technology (NICT). After the ﬁrst
public release in 2009, WN-Ja underwent several updates. We used versions 0.80 and 0.90
for this study.
3.2

Current status

Currently, 95% of the hypernym hierarchies
are linked to WN-Ja. Crude statistics are
given in Table 1. A sample of matches areshown in Figure 1.

Depth in Table 1 refers to the levels of hypernym hierarchy (measured from the root) at
which WN-Ja lemmas have matches. For example, 64,400 root hypernyms out of 67,000(tokens) have matches with WN-Ja, consisting of 3,272 unique types.
In this linkage process, however, we did
not take into account the effect of word sense
disambiguation. This suggests that we havefewer correct matches than the ﬁgures in Table 1 indicate.
As Table 1 suggests, WN-Ja hypernyms and Wikipedia-derived hypernyms havematches at very shallow levels (the average is nearly 3). More speciﬁcally, lower
level nodes of WN-Ja match the upper levelnodes of Wikipedia-derived hypernyms. This
forms the strongest support for our suggestion that Wikipedia-derived hypernyms cannot do without WN. Rather, the two kind ofresources enhance each other.

Table 1: Number and ratio of matches of WNJa lemmas over Wikipedia-derived hypernym

3.3 Details of the hypernym paths

depth
1
2
3
4

In Table 2, we show some examples with relevant details. The most common 12 root hypernyms were picked with example paths. In
most cases, the lowermost elements of the hypernym paths are hypernyms for named entities. This tendency is obvious when they

# of covered
64,412
24,554
2,804
53

ratio # of types
0.95923,272
0.36572,447
0.0418465
0.000830

Table 2: Most common 12 path elements (including unsaturated (L) and dubious (D) ones):
terms with asterisk (e.g., 者*, 品*, 社*, 家*) are bound morphemes whose hypernym status are
dubious.
Rank
1

Term
者*

Count
2,396

2

品*

2,115

3

社*

1,973

4

会社

1,881

5
6

番組
作品

1,758
1,630

7

家*

1,615

8
9

人*
校*

1,496
1,482

10

手*

1,425

11

人物

1,356

12

選手

1,242

Sample Path者* (person): 首謀者 (mastermind): 直接首謀者 (active mastermind):
事件の直接首謀者 (active mastermind of (the) affair):爆破事件の直接首謀者 (active mastermind of (the) bombing affair)(1) 品* (item): 製品 (product): ドイツの製品 (products of Germany)(2) 品 (item): 用品 (item(s) for . . . ): 園芸用品 (gardening supply)(1) 社* (company): 出版社 (publisher):
音楽出版社 (music publisher): 日本の音楽出版社 (music publisher in Japan)(2) 社* (place for sacred activity): 神社 (shrine):
市の神社 (shrine of (a) city): 鎌倉市の神社 (shrine of Kamakura City)社* (company): 会社 (company):
食品会社 (food company): 大手食品会社 (major food company)
番組 (program): 音楽番組 (music program): クラシック音楽番組 (classical music program)
品* (item): 作品 ((piece of) work): 題材にした作品 ((piece of) work on . . . ):吸血鬼を題材にした作品 ((piece of) work on vampires)(1) 家* (family): 五家 ((major) ﬁve schools): 禅宗五家 ((major) ﬁve schools of Zen):
中国禅宗五家 ((major) ﬁve schools of Chinese Zen)
(2) 家* (-ist): 運動家 (activist): フェミニズム運動家 (feminism activist)
人* (person): 料理人 (cook): フランス料理人 (French cook)
校* (school): 学校 (school): 高校 (high school):
女子高校 (girl’s high school): 公立女子高校 (public girl’s high school)(1) 手* (-er): 騎手 (jockey): イギリスの騎手 (British jockey)(2) 手 (technique(s)): 禁じ手 (prohibited technique(s), foul):
相撲の禁じ手 (prohibited technique(s) in Sumo wrestling)人物 (person): 長寿人物 (longevity person): 最長寿人物 (the oldest person):
世界最長寿人物 (world’s oldest person): 元世界最長寿人物 (former world’s oldest person)
手* (-er): 選手 (player): 野球選手 (baseball player):
プエルトリコの野球選手 (baseball player of Puerto Rico)are at the bottoms of the long paths withmore than one modiﬁers. All in all, the result suggests that the structure of modiﬁcationneeds to be carefully examined to have effective links between named entities and categories/classes of upper ontology.
3.4

Prospects for sense matches

In the example above, the hypernym matches
against WN-Ja are simple string-matches andare not sense-matches, because sense disambiguation is not performed on any of the pathelements.
This is regrettable. Fortunately, the cooccurrence information required for sense disambiguation on the upper-ontological elements, which have WN-Ja matches, is alreadyavailable in the paths as long as they are longenough. Actually, it is intuitively obviousthat the terms with WN-Ja matches have sufﬁciently speciﬁc senses, unless they are too
short. For example, 手 in 手:選手:スポーツ
選手:グルジアのスポーツ選手 of 9 of Fig-ure 1 corresponds to agent-denoting sufﬁx -er
of English, though it means “hand” when it isused us an independent word. For, the English
translation of the path would be: -er: player:
sport(s) player: Georgian sport(s) player.11)
This can be contrasted with cases like 手:禁じ手:相撲の禁じ手 which can be translated into
technique(s): prohibited technique(s): prohibited technique(s) in Sumo wrestling.
In Japanese analysis, recognition of sublexical units like 手 in 選手 and 機 in コ
ミューター機 is unavoidable, because they
are bound morphemes that play a role in basic word-formation. To our great annoyance,
they are not always properly recognized in theanalysis using Japanese tokenizers because
they tend to treat them as single units whencombinations become conventional. For example, there is no tokenizer that separates 手from 選手.12)
11) Incidentally, 手 is not the only morpheme that corresponds to -er. 者 and 人 are other major possibilities.
12) Another complication for composite terminology is obvious here. The most appropriate English translation of コ

This implies that comparison of daughter
terms on the WN-Ja side would enable sensematches; and that sense disambiguation iseasy to do if (i) enough positive examplesof speciﬁc senses are provided in composite form and (ii) similarity of a target termagainst the composite positive examples canbe calculated. Thus, the only barrier is that
we do not have enough positive examples forword senses in composite forms, arguing forthe building of sense tagged data with broad
coverage. In other words, if we build sense
tagged data based on Wikipedia, it would bequite beneﬁcial. We will try on this in future using the method described in Toral et al.
(2009).
4

Discussion

The WN-Ja coverage over the original
hypernym-hyponym pairs was only 8%: thatis 8% of the extracted pairs were alreadyfound within the Japanese WordNet.13) Thismeans that most of the pairs extracted in§2.4 are new additions to WordNet. We areadding a great deal of new information to theJapanese WordNet.
Looking at named entities speciﬁed as hyponyms in the Wikipedia data and entities in
WN-Ja, there are a lot of “missing links” withwhich various intermediate concepts can be
speciﬁed. Our impression is that these intermediate, concrete enough concepts are exactly the concepts that people use to conceptualize the world around them. For example,
famous rock singer (of a country) and former
member (of a group) in (1). We may assume
that they are building blocks in their mental
models. If this is correct, ﬁlling the missing
links would be very rewarding for NLP applications and related ﬁelds such as the SemanticWeb. Admittedly, it needs more research tovalidate this hypothesis.
Hypernym-hyponym pairs automatically
ミューター機 of 2 in Figure 1 would be “commuter type”(of aircraft) or “commuter model” (of aircraft) rather than“commuter apparatus” or “commuter machine” even if themost straightforward translation of 機 would be “apparatus”
or “machine.”
13) This comparison was made using WN-Ja 0.8.

acquired from Wikipedia cannot be linked
fully automatically. We required manual processing for the hypernym cleaning. Withcurrent extraction techniques lexical hierarchy data constructed fully automatically from
Wikipedia is very unlikely to be as precise as
WordNet’s synset hierarchies.
Finally, we would like to also note thatthe kind of upper ontology speciﬁed in theform of WordNet and similar lexical resources
would not be enough to cover the incredible
variety of ontological entities that appear in
Wikipedia. In particular, it contains quite a
lot of imaginary entities — most notably, afull range of characters that appear in books,
movies, legends, and folk tales. It is understandable, however, that they are not just components of people’s fantasies but are actual elements of people’s realities. Sometimes, it becomes quite hard to tell if they are real or unreal. Lexical resources like WordNet do notcurrently provide a proper place to hold themall. We may need to broaden the standard
upper ontologies to meet the speciﬁcation requirements by Wikipedia that seems to describe people’s realities without categoricallydistinguishing between fact and ﬁctions, between true and untrue facts, and between scientiﬁc and unscientiﬁc knowledge. Wikipedia
can be a challenge for scientiﬁc categorization
because pieces of knowledge of all kinds are
mixed in it together. It would not be surprising
if no single upper ontology can successfully
handle it.
There has been much work on linking theEnglish Wikipedia to WordNet, with YAGO
(Suchanek et al., 2007) being a good example. Our work differs in several ways. Trivially, we are looking at Japanese, rather thanEnglish. More interestingly, we only consider only hypernym relations, while YAGO
considers a wide range of relations, such as
BornInYear and LocatedIn. On theother hand, we consider a wider range of possible entities: YAGO only looks at Wikipedia
entries and their categories while Sumida etal. (2008) considers the text within the entry. Because of this, there is no guarantee thatthe terms we link are unambiguous entities, infact we collapse even Wikipedia disambiguation pages. In future work, we hope to disambiguate these again, perhaps using automatic
methods such as Toral et al. (2009).
In future work, we hope to extend theselinks to English, exploiting the multilingual
links in both WordNet and Wikipedia, in cooperation with ongoing work on hyponymy
extraction in both languages Oh et al. (2009).

Kow Kuroda, Masaki Murata, and Kentaro Torisawa.
2009. When nouns need co-arguments: A casestudy of semantically unsaturated nouns. In Proceedings of the 5th International Workshop on Generatvei Approaches to the Lexicon, Sep. 17-19,
2009, Pisa, Italy, pages 193–200.

5

F. M. Suchanek, G. Kasneci, and G. Weikum. 2007.
Yago: A Core of Semantic Knowledge. In Proceedings of the 16th International World Wide Web Conference (WWW 2007), New York, NY, USA. ACM
Press.

Conclusion

This paper described base hypernym extraction from the hypernym-hyponym pairs
automatically acquired from the Japanese
Wikipedia. It then compared Wikipediaderived hypernyms and the lemmas of
WordNet-Ja by determining how many
matches there are at which levels. The results
suggest that neither of the two data sources
are redundant. This means that we cannot
fully dispense with WordNet-like, manuallydeveloped high-precision lexical resourceseven if we have Wikipedia. Thus, the twokinds of resources are best understood ascomplementary to each other. In fact, if they
are successfully coupled, we can ﬁnally havelinks from named entities to abstract entities
in the upper ontology. The links help to form
the set of all encompassing, all inclusive
hierarchies that we long for.
References
Francis Bond, Hitoshi Isahara, Kyoko Kanzaki, andKiyotaka Uchimoto. 2008. Boot-strapping a WordNet using multiple existing WordNets. In Proceedings of the 6th International Conference on Language Resources and Evaluation (LREC-2008).
Francis Bond, Hitoshi Isahara, Sanae Fujita, Kiyotaka
Uchimoto, Takayuki Kuribayashi, and Kyoko Kanzaki. 2009. Enhancing the Japanese WordNet. In
The 7th Workshop on Asian Language Resources,
pages 1–8, Singapore. ACL-IJCNLP 2009.
P. Eschenbach. 1993. Semantics of number. Journalof Semantics, 10(1):1–31.
Christiane Fellbaum, editor. 1998. WordNet: An Electronic Lexical Database. MIT Press.

Jong-Hoon Oh, Kiyotaka Uchimoto, and Kentaro Torisawa. 2009. Bilingual co-training for monolingual
hyponymy-relation acquisition. In Joint Conferenceof the 47th Annual Meeting of the Association for
Computational Linguistics and the 4th International
Joint Conference on NLP of the Asian Federation of
NLP, Singapore.

Asuka Sumida, N. Yoshinaga, and Kentaro Torisawa.
2008. Boosting precision and recall of hyponymy
relation acquisition from hierarchical layouts in
Wikipedia. In Proceedings of the 6th International
Conference on Language Resources and Evaluation(LREC-2008).
A. Toral, Ó. Ferrández, E. Agirre, and R. Muñoz. 2009.
A study on linking Wikipedia categories to Wordnet
synsets using text similarity. In RANLP 2009.
V. N. Vapnik. 1995. The Nature of Statistical Learning
Theory. Springer.