Analogy as an Organizing Principle in Ontology Construction
Tony Veale and Guofu Li
School of Computer Science and Informatics, University College Dublin, Ireland
Keywords: Analogy, Mapping, Cliques, Text analysis, Ontology induction, Google n-grams.
Abstract: Ontology matching is a process that can be sensibly applied both between ontologies and within ontologies.
The former allows for inter-operability between agents using different ontologies for the same domain,
while the latter allows for the recognition of analogical symmetries within a single ontology. These
analogies indicate the presence of higher-order similarities between instances or categories that should be
reflected in the fine-grained structure of the ontology itself. In this paper we show how analogies between
categories in the same ontology can be detected via linguistic analysis of large text corpora. We also show
how these analogies can be clustered via clique-analysis to create meaningful new category structures in an
ontology. We describe experiments in the context of a large ontology of proper-named entities called
NameDropper, and show how this ontology and its analogies are automatically acquired from web corpora.
Ontologies, like languages, are meant to be shared.
A common ontology allows multiple agents to share
the same specification of a conceptualization
(Gruber, 1993), ensuring mutual intelligibility when
communicating in the same domain of discourse.
But like languages, there are often many to choose
from: each ontology is a man-made artifact that
reflects the goals and perspective of its engineers
(Guarino, 1998), and different ontologies can model
a domain with differing emphases, at differing levels
of conceptual granularity. Inevitably, then, multiple
agents may use different ontologies for the same
domain, necessitating a mapping between ontologies
that permits communication, much like a translator
is required between speakers of different languages.
Given the operability problems caused by
semantic heterogeneity, the problem of matching
different ontologies has received considerable
attention in the ontology community (e.g., see
Euzenat and Shvaiko, 2007). Fortunately, formal
ontologies have several properties that make
matching possible. Though formal in nature,
ontologies can also be seen as ossified linguistic
structures that borrow their semantic labels from
natural language (De Leenheer and de Moor, 2005).
It is thus reasonable to expect that corresponding
labels in different ontologies will often exhibit
lexical similarities that can be exploited to generate
match hypotheses. Furthermore, since ontologies are
highly organized structures, we can expect different
correspondences to be systematically related. As
such, systems of matches that create isomorphisms
between the local structures of different ontologies
are to be favored over bags of unrelated matches that
may may lack coherence. In this respect, ontology
matching has much in common with the problem of
analogical mapping, in which two different
conceptualizations are structurally aligned to
generate an insightful analogy (Falkenhainer, Forbus
and Gentner, 1989). Indeed, research in analogy
(ibid) reveals how analogy is used to structurally
enrich our knowledge of a poorly-understood
domain, by imposing upon it the organization of one
that is better understood and more richly structured.
Likewise, the matching and subsequent integration
of two ontologies for the same domain may yield a
richer model than either ontology alone.
If we view ontology-matching and analogical-
mapping as different perspectives on the same
structural processes, then it follows that matching
can sensibly be applied both between ontologies (to
ensure inter-operability) and within ontologies (to
increase internal symmetry). When applied within a
single ontology, matching should allow us to
identify pockets of structure that possess higher-
order similarity that is not explicitly reflected in the
Veale T. and Li G. (2009).
ONTOLOGICAL CLIQUES - Analogy as an Organizing Principle in Ontology Construction.
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development, pages 34-41
DOI: 10.5220/0002297900340041
ontology’s existing category structure. As such,
these analogies should permit the creation of a new
layer of structure in the ontology, to better reflect
human intuitions about the pragmatic similarity of
different categories and entities.
This paper has several related goals. First, we
demonstrate how analogical mappings can be
derived from corpora for large ontologies that are
themselves induced via text analysis. Second, we
show how this system of analogical mappings can
itself be subjected to further structural analysis, to
yield cliques of related mappings. Third, we show
how cliques can act as higher-level categories in an
ontology, to better capture the intuitions of end-users
(as reflected in their use of language) about which
categories and entities are more similar than others.
We begin in section 2 with a consideration of the
clustering role of categories in ontologies, and how
the graph-theoretic notion of a clique can also fulfil
this role, both at the level of instances and
categories. In section 3 we describe the induction of
our test ontology, called NameDropper, from the
text content of the web. In section 4 we then show
how analogical mappings between the categories of
NameDropper can also be extracted automatically
from web content. This network of analogical
mappings provides the grist for our clique analysis
in section 5, in which we show how analogical
cliques – tightly-knit clusters of mappings between
ontological categories – can be created to serve as
new upper-level categories in their own right. We
conclude with some final thoughts in section 6.
The taxonomic backbone of an ontology is a
hierarchical organization of categories that serves to
cluster ideas (both sub-categories and instances)
according to some intrinsic measure of similarity. In
the ideal case, ideas that are very similar will thus be
closer together – i.e., clustered under a more specific
category – than ideas that have little in common. It
follows that ontologies which employ more
categories can thus make finer distinctions that
better reflect the semantic intuitions of an end-user
(e.g., see Veale, Li and Hao, 2009).
Compare, for instance, the taxonomy of noun-
senses used by WordNet (Fellbaum, 1998) with that
of HowNet (Dong and Dong, 2006). In WordNet,
the category of {human, person} is divided into a
few tens of sub-types, which are themselves further
sub-divided, to hierarchically organize the different
kinds and roles of people that one might encounter.
In HowNet, however, every possible kind of person
is immediately organized under the category Human,
so that thousands of person-kinds share the same
immediate hypernym. For this reason, WordNet
offers a more viable taxonomic basis for estimating
the semantic similarity of two terms, as used in
various ways by Budanitsky and Hirst (2006).
Nonetheless, it is important to distinguish
between semantic similarity and pragmatic
comparability. The measures described by
Budanitsky and Hirst (2006) estimate the former,
and assign a similarity score to any pair of terms
they are given, no matter how unlikely it is that a
human might every seek to compare them.
Comparability is a stronger notion than similarity: it
requires that a human would consider two ideas to
be drawn from the same level of specificity, and to
possess enough similarities and differences to be
usefully compared. There is thus a pragmatic
dimension to comparability that is difficult to
express in purely structural terms. However, we can
sidestep these difficulties by instead looking to how
humans use language to form clusters of comparable
ideas. This will allow us to replace the inflexible
view of ontological categories as clusters of
semantically-similar ideas with the considerably
more flexible view of categories as clusters of
pragmatically-comparable ideas.
It has been widely observed that list-building
patterns in language yield insights into the
ontological intuitions of humans (e.g., see Hearst,
1992; Widdows and Dorow, 2002; Veale, Li and
Hao, 2009). For instance, the list “hackers, terrorists
and thieves”, which conforms to the pattern “Nouns,
Nouns and Nouns”, tells us that hackers, terrorists
and thieves are all similar, are all comparable, and
most likely form their own sub-category of being
(e.g., such as a sub-category of Criminal). We can
build on this linguistic intuition by collecting all
matches for the pattern “Nouns and Nouns” from a
very large corpus, such as the Google n-grams
(Brants and Franz, 2006), and use these matches to
create an adjacency matrix of comparable terms. If
we then find the maximal cliques that occur in the
corresponding graph, we will have arrived at a
pragmatic understanding of how the terms in our
ontology should cluster into categories if these
categories are to reflect human intuitions.
A clique is a complete sub-graph of a larger
graph, in which every vertex is connected to every
other (Bron and Kerbosch, 1973). A k-clique is thus
a complete sub-graph with k vertices; a clique is
maximal if it is not a proper-subset of another clique
in the same graph. In ontological terms then, a clique
ONTOLOGICAL CLIQUES - Analogy as an Organizing Principle in Ontology Construction
can represent a category in which every member has
an attested affinity with every other, i.e., a category
in which every member can be meaningfully
compared with every other. Since all ontologies are
graphs, the idea of a clique thus has a certain
semantic resonance in ontologies, leading Croitoru
et al., (2007) to propose cliques as a graph-theoretic
basis for estimating the similarity of two ontologies.
Figure 1: Cliques of different sizes in the graph of
coordinated nouns found in the Google n-grams corpus.
Cliques also indicate similarity within ontologies.
Figure 1 shows the distribution of maximal clique
sizes that we find when using the “Noun and Noun
pattern in the Google n-grams to mine coordinated
pairs of capitalized terms. In general, the cliques
correspond to proper subsets of existing categories,
and mark out subsets whose members are more
similar to each other than to other members of the
larger category. For instance, we find this 11-clique:
{Environment, Education, Finance, Industry, Health,
Agriculture, Energy, Justice, Science, Defence, Transport}
This clique seems to cluster the key societal themes
around which governments typically structure
themselves, thus suggesting an ontological category
such as Government_Ministerial_Portfolio.
Since the notion of a clique is founded on a
social metaphor, an example concerning proper-
named entities can be illustrative. Using the Google
n-grams and a named-entity detector, we can build
an adjacency matrix of co-occurring entities and
derive from the resulting graph a set of maximal
cliques. One such clique is the following 4-clique:
{Steve_Jobs, Bill_Gates, Michael_Dell, Larry_Ellison}
In an ontology of proper-named entities, such as the
NameDropper ontology described in the next
section, we would expect these entities to all belong
to the category CEO. However, this category is
likely to have thousands of members, so many
additional sub-categories are needed to meaningfully
organize this space of CEOs. What makes these
particular CEOs interesting is that each is an iconic
founder of a popular technology company; thus, they
are more similar to each other than to CEOs of other
companies of comparable size, such as those of GE,
Wal-Mart or Pfizer. In the ideal ontology, one would
expect these entities to be prominent members of a
more specific category such as TechCompany-CEO.
Figure 2: Cliques of different sizes from the graph of
coordinated proper-names in the Google n-grams corpus.
As shown in Figure 2, large cliques (e.g., k > 10) are
less common in the graph of co-occurring proper-
named instances than they are in the graph of co-
occurring categories (Figure 1), while small cliques
are far more numerous, perhaps detrimentally so.
Consequently, we find many partially overlapping
cliques that should ideally belong to the same fine-
grained category, such as Irish-Author:
{Samuel_Beckett, James_Joyce, Oscar_Wilde, Jonathan_Swift}
{Samuel_Beckett, Bram_Stoker, Oscar_Wilde, Jonathan_Swift}
{Samuel_Beckett, Seamus_Heaney}
{Patrick_Kavanagh, Brendan_Behan, James_Joyce}
This fragmentation presents us with two possible
courses of action. We can merge overlapping cliques
to obtain fewer, but larger, cliques that are more
likely to correspond to distinct sub-categories. Or we
can apply clique analysis not at the level of category
instances, but at the level of categories themselves.
In this paper we shall explore the latter option.
In the next section we describe the creation of a
large ontology of proper-named entities with a fine-
grained category structure. These fine-grained
categories expose enough of their semantic structure
to permit analogical mapping between categories,
using a corpus-based approach described in section
4. This network of analogical mappings between
categories will then allow us to form cliques of
similar categories in section 5.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
As a test-bed for our explorations, we choose a
domain in which the notion of a clique has both
literal and metaphoric meaning. NameDropper is an
ontology of the proper-named concepts – such as
people, places, organizations and events – that one
would expect to find highlighted in an online
newspaper. NameDropper is used to semantically
annotate instances of these entity-kinds in news-
texts and to provide one or many analogically-linked
categorizations for each instance.
Categories in NameDropper are semantically-
rich, and serve as compressed propositions about the
instances they serve to organize. For instance, rather
than categorize Steve_Jobs as a CEO, we prefer to
categorize him as Apple_CEO or Pixar_CEO; rather
than categorize Linus_Torvalds as a developer, we
categorize him as a Linux_developer and a
Linux_inventor; and so on. In effect then, each
category is more than a simple generalization, but
also encodes a salient relationship between its
instances and other entities in the ontology (e.g.,
Linux, Apple, etc.). These categories use corpus-
derived intuitions to augment, rather than replace,
the rich categories offered by an online resource like
Wikipedia (www.wikipedia.org). As we show in the
next section, this use of a rich-naming scheme for
categories also means that analogies between
different categories can be identified using simple
linguistic analysis of the structure of category labels.
The NameDropper ontology is extracted from
the text of the Google n-grams in a straightforward
manner. Simply, we use apposition patterns of the
following form to obtain category/instance pairs:
1. Mod Role Firstname Lastname
2. Mod1 Mod2 Role Firstname Lastname
3. Mod Role Firstname Midname Lastname
4. Mod1 Mod2 Role Firstname Midname Lastname
Here Mod, Mod1 or Mod2 is any adjective, noun or
proper-name, Firstname, Midname, and Lastname
are the appropriate elements of a named entity, and
Role is any noun that can denote a position,
occupation or role for a named-entity. A map of
allowable name elements is mined from WordNet
and Wikipedia, while a large list of allowable Role
nouns is extracted from WordNet by collecting all
single-term nouns categorized as Workers,
Professionals, Performers, Creators and Experts.
Since pattern (4) above can only be extracted from
6-grams, and Google provides 5-grams at most, we
use overlapping 5-grams as a basis for this pattern.
When applied to the Google n-grams corpus,
these patterns yield category/instance pairs such as:
a. Gladiator director Ridley Scott
b. Marvel Comics creator Stan Lee
c. JFK assassin Lee Harvey Oswald
d. Science Fiction author Philip K. Dick
Of course, not all pattern matches are viable
category/instance pairs. Importantly, the patterns
Mod Role or Mod1 Mod2 Role must actually
describe a valid category, so partial matches must be
carefully avoided. For instance, the following
matches are all rejected as invalid:
*e. Microsystems CEO Scott McNealy
*f. Vinci code author Dan Brown
*g. Meeting judge Ruth Bader Ginsberg
*h. The City star Sarah Jessica Parker
The n-grams in examples *e, *f and *h are clearly
truncated on the left, causing a necessary part of a
complex modifier to be omitted. In general this is a
vexing problem in working with isolated n-grams: it
is difficult to know if an n–gram stands alone as a
complete phrase, or if some key elements are
missing. In example *g we see that Meeting is not a
modifier for judge, but a verb that governs the whole
phrase. Nonetheless, we can deal with these
problems by performing the extraction and
validation of category labels prior to the extraction
of category/instance appositions. The following
patterns are thus used to extract a set of candidate
category labels from the Google n-grams:
5. the Mod Role
6. the Mod1 Mod2 Role
7. the Role of Mod1 Mod2 (
Mod1 Mod2 Role)
8. the Role of Mod (
Mod Role)
The patterns allow us to identify the strings the CEO
of Sun Microsystems (via 7) and the Supreme Court
judge (via 6) as yielding valid categories, but not the
Meeting judge or the Microsystems CEO (which are
not attested). Thus, only those collocations that can
be attested via patterns 5 – 8 in the Google n-grams
are allowable as categories in the patterns 1 – 4.
Overall, the intersection of patterns 1–4 and 5–8
extracts almost 60,000 different category/instance
pairings from the Google n-grams corpus, ascribing
an average of 2 categories each to 29,334 different
named-entity instances. Because the Google corpus
contains only those n-grams that occur 40 times or
more on the web, the extraction process yields
remarkably little noise. A random sampling of
NameDropper’s contents suggests that less than 1%
of categorizations are malformed.
ONTOLOGICAL CLIQUES - Analogy as an Organizing Principle in Ontology Construction
These patterns lead NameDropper to be populated
with many different complex categories and their
proper-named instances; each complex category,
like Apollo_11_astronaut, is a variation on a basic
role (e.g., astronaut) that serves to link an instance
(e.g., Neil_Armstrong) to this role in a specific
context (e.g., Apollo_11). There is some structure to
be had from these complex categories, since clearly,
an Apollo_11_astronaut is an Apollo_astronaut,
which in turn is an astronaut. But such structure is
limited, and as a result, NameDropper is populated
with a very broad forest of shallow and disconnected
mini-taxonomies. The ontology clearly needs an
upper-model that can tie these separate category
silos together, into a coherent whole. One can
imagine WordNet acting in this capacity, since the
root term of every mini-taxonomy is drawn from
WordNet’s noun taxonomy. Yet, while WordNet
provides connectivity between basic roles, it cannot
provide connectivity between complex categories.
For instance, we expect Apollo_astronaut and
Mercury_astronaut to be connected by the
observation that Apollo and Mercury are different
NASA programs (and different Greek Gods). As
such, Apollo_astronaut and Mercury_astronaut are
similar in a different way than Apollo_astronaut and
American_astronaut, and we want our ontology to
reflect this fact. Likewise, Dracula_author (the
category of Bram_Stoker) and Frankenstein_author
(the category of Mary_Shelley) are similar not just
because both denote a kind of author, but because
Dracula and Frankenstein are themselves similar. In
other words, the connections we seek between
complex categories are analogical in nature. Rather
than posit an ad-hoc category to cluster together
Dracula_author and Frankenstein_author, such as
Gothic_monster_novel_author (see Barsalou, 1983,
for a discussion of ad-hoc categories), we can use an
analogical mapping between them to form a cluster.
But as can be seen in these examples, analogy is
a knowledge-hungry process. To detect an analogy
between Apollo_astronaut and Mercury_astronaut, a
system must know that Apollo and
Mercury are
similar programmes, or similar gods. Likewise, a
system must know that Dracula and Frankenstein
are similar books to map Dracula_author to
Frankenstein_author. Rather than rely on WordNet
or a comparably large resource for this knowledge,
we describe here a lightweight corpus-based means
of finding analogies between complex categories.
Two complex categories may yield an analogy if
they elaborate the same basic role and iff their
contrasting modifier elements can be seen to belong
to the same semantic field. The patterns below give
a schematic view of the category mapping rules:
1. ModX_Role
2. ModX_Mod_Role
3. Mod_ModX_Role
4. ModA_ModB_Role
E.g., these rules can be instantiated as follows:
1. Java_creator
2. Apple_inc._CEO
3. Apollo_11_astronaut
4. Man_United_striker
Clearly, the key problem here lies in determining
which modifier elements occupy the same semantic
field, making them interchangeable in an analogy.
We cannot rely on an external resource to indicate
that Java and Perl are both languages, or that Apple
and Disney are both companies. Indeed, even if such
knowledge was available, it would not indicate
whether a human would intuitively find Java an
acceptable mapping for Linux, say, or Apple an
acceptable mapping for Hollywood, say. What is an
acceptable level of semantic similarity between
terms before one can be replaced with another?
Fortunately, there is a simple means of acquiring
these insights automatically. As noted in section 2,
coordination patterns of the form Noun1 and Noun2
reflect human intuitions about terms that are
sufficiently similar to be clustered together in a list.
For instance, the following is a subset of the Google
3-grams that match the pattern “Java and *”:
Java and Bali Java and C++ Java and Eiffel
Java and Flash Java and Linux Java and Perl
Java and Python Java and SQL Java and Sun
Coordination typically provides a large pool of
mapping candidates for a given term. To minimize
noise, which is significant for such a simple pattern,
we look only for the coordination of capitalized
terms (as above) or plural terms (such as cats and
dogs). Much noise remains, but this does not prove
to be a problem since substitution of comparable
terms is always performed in the context of specific
categories. Thus, Perl is a valid replacement for
Java in the category Java_creator not just because
Java and Perl are coordinated terms in the Google 3-
grams, but because the resulting category,
Perl_creator, is a known category in NameDropper.
As a result, James_Gosling (Java_creator) and
Larry_Wall (Perl_creator) are analogically linked.
Likewise, Linux_creator and Eiffel_creator are valid
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
analogies for Java_creator, but not Bali_creator or
Sun_creator, since these are not known categories.
Since categories can have multiword modifiers
(e.g., King_Kong_director, Harry_Potter_star), we
run a range of patterns on Google 3-,4- and 5-grams:
1. ModX and ModY
2. ModA ModB and ModX ModY
3. ModA ModB and ModX
4. ModX and ModA ModB
5. ModX and ModY PluralNoun
E.g., these patterns find the following equivalences:
1. Batman and Superman
2. James Bond and Austin Powers
3. Sin City and Gladiator
4. Microsoft and Sun Microsystems
5. Playboy and Penthouse magazines
These patterns show the scope for noise when
dealing with isolated n-grams. We might ask, what
makes the 4-gram Sin City and Gladiator a valid
coordination but the 3-gram City and Gladiator an
invalid one? Quite simply, the latter 3-gram does not
yield a pairing that can grounded in any pair of
complex categories, while the 4-gram yields the
analogies Sin_City_writer Æ Gladiator_writer,
Sin_City_director Æ Gladiator_director, and so on.
Likewise, the substitution Apples and Oranges is
not sensible for the category Apple_CEO because
the category Orange_CEO does not make sense.
To summarize then, the process of generating
inter-category analogies is both straightforward and
lightweight. No external knowledge is needed, e.g.,
to tell the system that Playboy and Penthouse are
both magazines of a somewhat sordid genre, or that
Batman and Superman are both comic-book
superheroes (interestingly, WordNet has entries for
all four of these words, but assigns them senses that
are utterly distinct from their pop-culture meanings).
Rather, we simply use coordination patterns to
formulate substitutability hypotheses in the context
of existing ontological categories. Thus, if a
substitution in one existing category yields another
existing category, then these two categories are held
to be connected by an analogy. We note that one
does not have to use Google n-grams to acquire
coordination patterns, but can use any corpus at all,
thereby tuning the analogical mappings to the
sensibilities of a given corpus/context/domain.
When applied to the complex categories of
NameDropper, using coordination patterns in the
Google n-grams, this approach generates 218,212
analogical mappings for 16,834 different categories,
with a mean of 12 analogical mappings per category.
These analogical mappings provide a high degree of
pair-wise connectivity between the complex
categories of an ontology like NameDropper, or of
any ontology where category-labels are linguistically
complex and amenable to corpus analysis. This
connectivity serves to link instances in ways that
extend beyond their own categories. Returning to the
Playboy example, we see the following mappings:
All mappings are symmetric, so what we have here
is an analogical clique, that is, a complete sub-graph
of the overall graph of analogical mappings. Such
cliques allow us to generalize upon the pair-wise
connectivity offered by individual mappings, to
create tightly-knit clusters of mappings that can act
as generalizations for the categories involved. Thus,
the above mappings form the following clique:
{Playboy_publisher, Penthouse_publisher,
A corresponding clique of modifiers is also implied:
{Playboy, Penthouse, Hustler}
In turn, an analogical clique of categories also
implies a corresponding clique of their instances:
{Hugh_Hefner, Bob_Guccione, Larry_Flynt}
It is worth noting that this clique of individuals (who
are all linked in the public imagination) does not
actually occur in the cliques of proper-named
entities that we earlier extracted from the Google 5-
grams in section 2 (see Figure 2). In other words, the
analogical clique allows us to generalize beyond the
confines of the corpus, to create connections that are
implied but not always overtly present in the data.
The cohesiveness of an ontological category
finds apt expression in the social metaphor of a
clique. No element can be added to a clique unless
that new element is connected to all the members of
the clique. For instance, since Playboy magazine is a
rather tame example of its genre, we find it
coordinated with other, less questionable magazines
in the Google n-grams, such as Sports Illustrated,
Vanity Fair, Rolling Stone and Maxim magazines.
Thus, we also obtain mappings like the following:
ONTOLOGICAL CLIQUES - Analogy as an Organizing Principle in Ontology Construction
This, in turn, implies a correspondence of instances:
All this is as one might expect, but note how the
association of Playboy and Rolling Stone does not
influence the structure of our earlier analogical
clique: Rolling_Stone_publisher does not join the
clique of Playboy_publisher, Penthouse_publisher
and Hustler_publisher because it lacks a connection
to the latter two categories; Jann_Wenner thus
avoids membership in the clique of Hugh_Hefner,
Bob_Guccione and Larry_Flynt.
Analogical cliques allow us to turn pair-wise
analogical mappings between categories into
cohesive superordinate categories in their own right.
Thus, {Playboy_publisher, Penthouse_publisher,
Hustler_publisher} acts as a super-ordinate for the
categories Playboy_publisher, Penthouse_publisher
and Hustler_publisher, and in turn serves as a
common category for Hugh_Hefner, Bob_Guccione
and Larry_Flynt. Because analogies are derived in a
relatively knowledge-lite manner from corpora,
these cliques act as proxies for the kind of explicit
categories that a human engineer might define and
name, such as publisher_of_men’s_magazines.
Analogical cliques can serve a useful structural role
in an ontology without being explicitly named in this
fashion, but they can also be extremely useful as part
of semi-automated knowledge-engineering solution.
In such a system, analogical cliques can be used to
find clusters of categories in an ontology for which
there is linguistic evidence – as mined from a corpus
– for a new super-ordinate category. Once identified
in this way, a human ontologist can decide to accept
the clique and give it a name, whereupon it is added
as a new first-class category to the ontology.
Recall from section 2 that mining the Google n-
grams for coordination among proper-named entities
yields a highly fragmented set of instance-level
cliques. In particular, Figure 2 revealed that
clustering instances based on their co-occurrence in
corpora produces a very large set of relatively small
cliques, rather than the smaller set of larger cliques
that one would expect from a sensible categorization
scheme. In contrast, Figure 3 below shows that the
graph of analogical mappings between categories
produces a wider distribution of clique sizes, and
produces many more maximal k-cliques of k > 10.
Figure 4 presents a side-by-side comparison of
the results of Figures 2 and 3. It shows that while the
analogical level produces less cliques overall
(42,340 analogical cliques versus 72,295 instance-
level cliques, to be specific), analogical cliques tend
to be larger in size, and thus achieve greater levels of
generalization than cliques derived from instances
Figure 3: Cliques of different sizes from the graph of
analogical mappings between NameDropper categories.
Figure 4: The distribution of instance-level clique sizes
(from coordinated proper-names) compared with the
distribution of analogical-clique sizes.
To appreciate the greater connectivity that a layer of
analogical cliques can provide to an ontology, we
must ask two important questions. What percentage
of the 72,295 instance-level cliques that are induced
from Google coordination patterns represent a
clustering of instances that all belong to one or more
of the same categories? In other words, what
percentage of instance-level cliques can be unified
under the same ontological category? Now, what
percentage of these cliques can be unified under the
same analogical clique? For the first question, the
answer is 33% – just 1 in 3 instance-level cliques are
proper subsets of a single ontological category. For
the second question, the answer is 56%. Clearly,
analogical cliques of categories offer a much better
model of the way that speakers intuitively cluster
their ideas in a text than do the categories alone.
KEOD 2009 - International Conference on Knowledge Engineering and Ontology Development
Word usage in context often defies our best attempts
to exhaustively enumerate all the possible senses of
a word (e.g., see Cruse, 1986). Though resources
like WordNet are generally very useful for language-
processing tasks, it is unreasonable to assume that
WordNet – or any print dictionary, for that matter –
offers a definitive solution to the problem of lexical
ambiguity. As we have seen here, the senses that
words acquire in specific contexts are sometimes at
great variance to the official senses that these words
have in dictionaries (Kilgarriff, 1997). It is thus
unwise to place too great a reliance on dictionaries
when acquiring ontological structures from corpora.
We have described here a lightweight approach
to the acquisition of ontological structure that uses
WordNet as little more than an inventory of nouns
and adjectives, rather than as an inventory of senses.
The insight at work here is not a new one: one can
ascertain the semantics of a term by the company it
keeps in a text, and if enough inter-locking patterns
are employed to minimize the risk of noise, real
knowledge about the use and meaning of words can
be acquired (Widdows and Dorow, 2002). Because
words are often used in senses that go beyond the
official inventories of dictionaries (e..g., recall our
examples of Playboy, Penthouse, Apollo, Mercury,
Sun and Apple), resources like WordNet can actually
be an impediment to achieving the kinds of semantic
generalizations demanded by a domain ontology.
A lightweight approach is workable only if other
constraints take the place of lexical semantics in
separating valuable ontological content from ill-
formed or meaningless noise. In this paper we have
discussed two such inter-locking constraints, in the
form of clique structures and analogical mappings.
Clique structures winnow out coincidences in the
data to focus only on patterns that have high internal
consistency. Likewise, analogical mappings enforce
a kind of internal symmetry on an ontology, biasing
a knowledge representation toward parallel
structures that recur in many different categories.
We have focused here on our own ontology,
NameDropper, created to annotate online newspaper
content. Our subsequent focus will expand to
include other, larger ontologies extracted from web-
content, including DBpedia and other Wikipedia-
derived resources (see Auer et al., 2007; Fu and
Weld, 2008). The category structure of Wikipedia is
sufficiently similar to that of NameDropper (in its
use of complex labels with internal linguistic
structure) that the analogical techniques described
here should be readily applicable. We shall see.
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ONTOLOGICAL CLIQUES - Analogy as an Organizing Principle in Ontology Construction