Online Presentation of an Upper Ontology
Michal Ševčenko
Department of Computer Science
Czech Technical University in Prague
sevcenko@vc.cvut.cz

Abstract. This article presents the SUMO Browser—online tool that can be used
for browsing the Suggested Upper Merged Ontology, SUMO, and its connection
to the WordNet lexicon. The Browser facilitates the process of getting familiar
with SUMO content. A brief introduction into SUMO and WordNet is also presented.

1

Introduction

New engineering tasks like information retrieval, natural language processing, knowledge representation, and data interoperability require new resources. Among important resources needed for designing knowledge systems belong ontologies—formal descriptions of the structure of knowledge bases. Special class of ontologies is formed
by upper ontologies—domain-independent ontologies intended to be reused and extended for particular domain to form a domain ontology. Another important resources
are electronic lexicons. Lexicons provide a bridge between the knowledge represented
in knowledge systems, and natural language.
This article presents a short introduction into one particular upper ontology—the
SUMO [1, 3], and one particular lexicon—the WordNet [2, 4]. A discussion about theusability of the WordNet lexicon in automated natural language processing tasks is
included. The next section gives the idea of interconnecting SUMO and the WordNet.
The last sections introduce the SUMO Browser [5]—an online tool for exposing thecontent of SUMO and the WordNet to the user in an appropriate form. Separate sections
are dedicated to the paraphrasing feature of the Browser, and to sample application
demonstrating programmatical access to the SUMO and WordNet content.

2 SUMO—the Suggested Upper Merged Ontology
SUMO is a collection of approximately 1000 well-defined and well-documented concepts, interconnected into semantic network and accompanied by a number of axioms.
The concepts range from very general ones, such as Quantity, to very specific, such as
Bird. The axioms mostly reflect common-sense notions that are generally recognizedamong the concepts. SUMO is intended as a domain-independent substrate for designing domain ontologies.
Axioms help to constraint interpretation of concepts, and provide guidelines forautomated reasoning systems that process knowledge bases conforming to the SUMO
ontology. An example of such an axiom is:“If c is an instance of combustion, then there exist heating h and radiating light
l so that both h and l are subprocesses of c”.
This rather complicated, but logical, sentence says that a process of heating and a process of emitting light accompany each process of combustion (burning). Moreover, thisaxiom is coded in SUMO in a formal logical language.
Concepts in SUMO are organized into a single hierarchy rooted at Entity, representing the most general concept. The first two levels of the hierarchy are depicted in
Figure 1. You can see, for example, that Entities are divided into physically existent
stuff (Physical), and abstract, mentally represented stuff (Abstract). Physical things arefurther distinguished as objects and processes, etc.
Subclasses of a class are usually mutually exclusive, i.e. they do not share common instances. For example, nothing can be both an abstract and a physical, neither
both an object and a process. This property is explicitly specified in SUMO. However,some classes can have multiple superclasses. For example, a Human is both Hominid (amember of certain class of animals) and a CognitiveAgent (an entity with the ability toreason).
One of the drawbacks of SUMO is its relatively low coverage that does not allow
its employment for open-domain applications. It also lacks a connection between its
concepts and natural language words. These limitations have been partially overcome
by connecting SUMO to the WordNet lexicon.

EntityAbst ract

Physi calObjectQu an t i t y

Att ri bu t e

Proposi ti on

Process

SetOrCl ass

Rel ati on

Fig. 1. Top-level concepts in SUMO

3

WordNet—an Online Lexical Database

WordNet is a freely available on-line lexicon. Linguists at the Princeton University
have created it as a result of their psycholinguistic research. However, in the last decade
WordNet proved to be very valuable resource for automated processing of natural language.

Technically, WordNet is an electronic thesaurus, defining large set of word meanings, interlinked with semantic pointers. The logical structure of WordNet is shown in
Figure 2.

w or d f or m s

w ord m ean i ng s (syn set s)Fig. 2. The logical structure of WordNet

Word meanings are associated with word forms that can express them. We can see
on the figure that the relation between word forms and word meanings is m to n—wordform can have many meanings, and many word forms can refer to the same meaning.
The former phenomenon is called polysemy, the latter is called synonymy. Dealing
with such an ambiguity of natural language is the key challenge in automated natural
language processing.
Each word meaning entry (also called synonym set, or synset), is accompanied withshort informal definition (called gloss), and list of word forms that can represent thesynset in spoken or written language. Synsets are kept separately for different parts ofspeech: there are databases of nouns, verbs, adjectives and adverbs. It should be notedthat the semantic relations between synsets are different for different part of speech. For
example, for nouns a chief relation between synsets is an is-a relation, well known fromdata modeling. In WordNet, this relation is called hypernymy/hyponymy.
It may seem at the first sight that synsets in WordNet built up a large semantic network, as we know it as a knowledge representation paradigm of artificial intelligence.
However, a closer look reveals that semantic relations in WordNet are sometimes veryvague and non-logical, and cannot be used for logical inference. The relations werecoded by lexicographers, and were meant as a resemblance of humans’ understandingof relations between word meanings. Moreover, due to the overwhelming size of thesemantic network, the design rationale for semantic relations was rather local, without
paying attention to the overall structure of the whole network.
Consider an example of the synset corresponding to the word blues. The WordNet
defines blues as ‘a type of folk song that originated among Black Americans at the
beginning of the 20th century; has a melancholy sound from repeated use of blue notes’.
The hypernymy hierarchy of this synset is shown in Figure 3.

en t i t y
abst ract

objectt h i ngr el at i o n
art ef act

s o ci al r el at i o ncreat i oncomm un i cat i onar t

st y l em u si cm u si c g en r e

popu l ar m u si c

m u si cal com posi t i onf o l k m u si c

song

f ol k song

bl ack m u si c
bl u esFig. 3. The hypernymy hierarchy of synset blues

It can be seen from this figure that this small semantic network is quite ill formed,
due to loose interpretation of concept meanings. For example, blues is both abstract(mental, nonexistent) and thing (physically existing). Similarly, the concept folk song
has two senses: one sense denotes a class of songs which are folk, and is kind of song.
However, the concept can be also understood as an attribute of songs, being kind ofmusic genre. In common language these two concepts are usually not explicitly distinguished, and thus this distinction is not handled in WordNet. Similar situation occurs
with multiple hypernyms of the concept music genre, where it is omitted the distinction between a process (music), and its role (social relation). Of source, there are moresimilar problems that complicate exploitation of WordNet as a resource for automatednatural language processing.
It is interesting to ask whether these discrepancies are intrinsic to resources such as
WordNet, or if they can be avoided by more careful design. The problem is that logic
and linguistic rationales for organizing word meanings are quite different, especiallyfor very general concepts, which are close to the root of the hierarchy.

4Mapping WordNet to SUMO

It should be clear from the previous sections that both SUMO and WordNet address thesimilar problem, although with different focus. Both SUMO and WordNet define conceptualizations (simplifications) of our world. WordNet with the chief purpose to map
these conceptualizations into natural language terms, and SUMO with the purpose toorganize them into a logical structure. It thus makes sense to create a mapping between
these two resources.
SUMO authors have developed such a mapping. The mapping enriches WordNet
database files by tagging each synset with the corresponding SUMO concept. Moreover, a kind of relation between WordNet synset and SUMO concept is presented—the
WordNet synset may be declared as equivalent to the SUMO term, as subsumed by it,or as an instance of it1 . For example, the synset {animal, beast, fauna} is marked as
equivalent to the SUMO concept Animal. The synset scavenger, however, is declared
as subsumed by the concept Animal (if there were an equivalent class for this synset in
SUMO, it would be a subclass of Animal). Similarly, the WordNet synset Pythagoras ismarked as an instance of SUMO concept Human.
These mapping files allow mapping natural language words into SUMO terms, using WordNet synsets as an intermediate layer.

5

The SUMO Browser

Although SUMO authors spent considerable effort to make the structure of concepts andaxioms transparent and unambiguous, it takes a while for a novice reader to get familiar
with it and to understand its philosophy. To facilitate this process, an online tool, theSUMO Browser, has been developed. Using this tool, users may browse both SUMO
and WordNet hierarchies, and navigate from a SUMO concept to the corresponding
WordNet synsets and vice versa.
The Browser has been designed with the intention to present the content of SUMO
in a user-friendly way, so that it is legible even for non-expert users. One of its uniquefeatures is natural language paraphrasing of axioms, which is described in more detail in
section 6. Moreover, although SUMO implementation is internally just a list of axioms,
the Browser understands and interprets some of the axioms and displays them in an
appropriate way.
The Browser can display information about one concept (class) at a time. It displays
the following information:– the ontology section to which the class belong– the list of immediate subclasses– the list of all superclasses– the list of instances– the list of coordinate terms (other subclasses of the immediate superclass)– equivalent WordNet synsets
1 It should be noted that WordNet hypernymy/hyponymy relations cover subclass-superclassrelations as well as class-instance relations. In SUMO, these relations are strictly distinguished.

– all related WordNet synsets (on separate page)– list of relevant axioms, both in logic and in natural language paraphrase
An example of a sheet for concept Animal can be seen in Figure 4. Where appropriate, the contents of the concept information is converted into active hypertext linkleading to another SUMO concept or WordNet synset.
One axiom relevant for the concept Animal is displayed on the sheet. As you cansee, the axiom is presented both in natural language and in prefix logical notation.
Alternatively, the Browser allows navigating through the subclass–superclass hierarchy of concepts, similarly like file managers allow to navigate a hierarchy of folders
on a disk drive. You can see an example of a hierarchy view in Figure 5.
The third way how to access the content of SUMO or WordNet is by looking up a
particular concept or English word given a text. If an English word is searched, a listof corresponding WordNet synsets is displayed. These synsets can be navigated along
WordNet semantic pointers, or a corresponding SUMO concept may be displayed.
Other online ontology browsers, such as Ontolingua [6], Chimæra [7], or the original SUMO browser, prefer simplicity and logical consistency of the user interface overits user-friendliness. They try to avoid using ad-hoc visualization patterns for different
axioms. Our Browser, on the other hand, frequently employs such ad-hoc patterns. Doing so generally produces more legible output, but clearly requires more work during
design and implementation of the Browser.
An example of ad-hoc visualization pattern is visualization of type restrictions offunctions or relations. For instance, instead of displaying axioms(domain ListOrderFn 1 List), (domain ListOrderFn 2 PositiveInteger)and (range ListOrderFn Entity)
in their raw form, they are interpreted and displayed as a single expression
Entity ListOrderFn(List, PositiveInteger).
The source code of the Browser is freely available. This allows users to either install
it on their computer, or to reuse the source code to access either SUMO or WordNet
programmatically, as described in section 7.

6

English Paraphrasing of Axioms

This section briefly describes the natural language paraphrasing feature of the Browser.
Although this feature merely transcripts logical expressions encoded in KIF2 language[8] into English-like sentences, it may be of great help for users that are not familiar
with particular KIF syntax. The paraphrases are also good feedback for axiom authors.
SUMO axioms are predicate logic expressions. These expressions are internally
represented as trees, where internal nodes are logical conjunctions and quantifiers, andtree leaves are predicates. The paraphrasing algorithm is a straightforward recursive
traversal, which applies single rule to each node to produce natural language text. For
example, expression E1 ∧ E2 is translated into “N(E1 ) and N(E2 )”, where N(x) is anatural language representation of expression x, which is obtained applying the samealgorithm. Besides these simple rules, the following techniques are used:
2 KIF = Knowledge Interchange Format, the language in which SUMO ontology is encodedFig. 4. Example of a SUMO browser sheet for the concept Animal

Fig. 5. Example of a hierarchy view

– If the input expression contains a negation in some internal node, the negation isalways distributed to the leaf node using simple transformation rule; for example,
expressions ¬(A∧B) and ¬(∀x : P(x)) are transformed into ¬A∨¬B and ∃x : ¬P(x),respectively. This improves the legibility of resulting English sentences.
– If the expression structure is too complex, and mere linear representation mightlead to ambiguous interpretations, indented lists are used to structure the English
paraphrase. For example, expression (A ∧ B) ⇒ (C ∨ D) is translated into
• if A and B• then C or D
This provides legible and unambiguous output even for very complex expressions.
– The following frequently occurring expression pattern:∃x, y : instance(x, Person) ∧ instance(y, Animal) ∧ pet(x, y)
is translated into more concise representation “There exists Person x and Animal yso that x has a pet y”, rather than “There exists x and y so that x is an instance. . . ”.
The remaining issue is how to paraphrase leaf nodes of expressions, i.e. predicatesand functions. The paraphrasing algorithm relies on the presence of special formatting
strings that are embedded in the ontology and define the natural language output ofpredicates and functions. Special relation format associates each predicate or function
with its formatting string. For example, axiom associating the predicate parent with its
formatter reads “(format parent "%2 is %n a &%parent of %1")”. During paraphrasing, %1 tag is replaced with natural language output of first predicate’s argument,%2 with the second one, and %n tag with the word ‘not’ if the predicate is being rendered as negative, or with an empty string otherwise. The ‘&%’ tag means that the word‘parent’ is made into a hypertext link that leads to the SUMO concept parent.
This approach has been extended to support multilingual paraphrases. It is possible
to associate more formatters to single predicate, each for different language. In cooperation with SUMO authors, formatters for five languages have been developed: English,
German, Czech, Italian and Hindi.

7

Demonstration of SUMO Browser API

As it has been mentioned, the SUMO browser is distributed with its source code. A partof the source code are libraries that can access SUMO and WordNet files programmatically. This section presents a sample application that makes use of these libraries.
One of the problems with WordNet database is that it is very large, as it contains
words from all areas of human interest. One might want to get rid of concepts fromuninteresting domains to make the database more manageable, by means of pruning the
WordNet hierarchy. The SUMO-WordNet mapping is an excellent resource for such atask, and SUMO Browser API enables us to do it in minutes.
Let’s assume that we are not interested in biological concepts in WordNet. We might
try to prune all synsets that are associated with SUMO concept Organism or one of its
subclasses. The code fragment in Figure. 7 demonstrates how to do this procedure using
SUMO Browser API.

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// wordnet noun synsets
set<SynSet> synsets;// SUMO ontology
OntologyInfo ontology;// read wordnet noun database (special version annotated with SUMO concepts)
SynSet::ReadDataFile(synsets, ”noun.dat”);// read SUMO ontology
ontology.ParseOntology(”merge.txt”);
set<SynSet>::iterator i, next;// go through all wordnet synsets
i = synsets.begin();while (i != synsets.end()){
next = i; ++next;// prune flagbool prune = false;// go through all SUMO terms associated with the current synset
for (list<SynSet::SumoTerm>::iterator k = i->SumoTerms.begin();!prune && k != i->SumoTerms.end(); ++k){// is the current associated SUMO term a subclass of ”Organism”?
if (ontology.IsSubclassOf(k->Concept, ”Organism”))// if so, mark it for pruning
prune = true;}// erase synset if prune flag is set
if (prune)
synsets.erase(i);i = next;}// write remaining synsets back to the database file
SynSet::WriteDataFile(synsets, ”noun.dat”);

Fig. 6. C++ Code fragment for pruning WordNet noun database

Code at lines 1–9 loads SUMO ontology and WordNet noun database into memory.
The main cycle at lines 14–32 considers each noun synset for pruning. The synset ispruned if any of its associated SUMO concepts is a subclass of SUMO concept Organism (lines 20–27). Remaining synsets are written back to disk at line 35 in the WordNet
native format.

8

Conclusions

SUMO, WordNet and the SUMO Browser have been presented in this article. SUMO
is intended as a domain-independent substrate for designing domain ontologies. The
WordNet lexicon provides a link between formal content expressed in SUMO and natural language. WordNet also proved to be useful in development of domain ontologies
built up on the top of SUMO, as well as in testing the coverage of SUMO or SUMO
compliant domain ontologies. The SUMO Browser facilitates these tasks by exposing
the content of SUMO, WordNet, and eventual user-defined domain ontologies in a userfriendly way. One of its unique features is paraphrasing hard-to-read logical inscriptionof axioms into natural language. SUMO Browser source code contains libraries foraccessing SUMO and WordNet files programmatically. They can be used for experimenting with these resources.

References
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http://projects.teknowledge.com/AAAI-2002/Pease.ps
2. Miller, G.A., Beckwith, R., Fellbaum, C. Gross, D., Miller, K.J.: Introduction to WordNet: anon-line lexical database. In International Journal of Lexicography 3 (4), 1990, pp. 235 - 244.
ftp://ftp.cogsci.princeton.edu/pub/wordnet/5papers.ps
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