Extracting Ontological Knowledge from Textual Descriptions
Kevin Alex Mathews

P Sreenivasa Kumar

Indian Institute of Technology Madras
kevin@cse.iitm.ac.in

Indian Institute of Technology Madras
psk@cse.iitm.ac.in

KEYWORDS

Text Analytics, Ontology Learning, Knowledge Extraction

1

the input sentence without substantial background knowledge of
the domain. Most current authoring systems produce one formalization of a sentence. We ﬁnd that the best way to handle these
challenges is to construct axioms corresponding to alternative formalizations of the sentence so that the end-user can make an appropriate choice.
In this paper, we propose a novel ontology authoring system
that simpliﬁes the authoring process for the users. Our system
takes English text that conforms to a newly proposed CNL called
TEDEI input and generates corresponding OWL axioms. We scope
ontology authoring to building the schema (TBox) of an ontology.
Within this context, we focus on OWL class expression axioms.
We have outlined the architecture of our system in Figure 1.
The ﬁve main modules of the system are lexical ambiguity handler, TEDEI parser, semantic ambiguity handler, syntactic transformation and ACE-to-OWL translation. Lexical ambiguity handler
accepts the input sentences, expressed in English, and generates
possible lexicalizations of the sentence using POS tag patterns. Lexicalization is the process of breaking the given sentence into tokens such that these tokens can be identiﬁed as various ontology
elements, namely, classes, individuals, properties and concept constructors. Then TEDEI parser parses the lexicalizations on the basis
of TEDEI grammar rules. Only valid TEDEI lexicalizations are processed further. Sentences not conforming to TEDEI are indicated as
such to the user so that they can be reformulated. Semantic ambiguity handler generates possible interpretations of the TEDEI lexicalizations using speciﬁc sentence patterns. The interpretations
are converted to ACE in the syntactic transformation module using rules of transformation. Finally ACE sentences are converted
to OWL by the existing ACE parser.
The output of the system is compared against human-authored
axioms and in substantial number of cases, human-authored axiom
is indeed one of the alternatives given by the system. Our framework clearly outperforms ACE in terms of the number and types
of sentences the system can handle. In comparison with existing
systems, due to the use of TEDEI, our framework is a robust way
to generate ontological axioms from text. TEDEI helps in clearly
deﬁning the scope of the system and provides the ability to reject
a sentence and ask for reformulation. Also, employing ACE as an
intermediate language aids formalization and reduces the complexity of the system.
Our contributions in this paper are as follows: (1) an ontology authoring process, (2) an ontology authoring language whose
grammar reﬂects the constructs of OWL and which has better expressivity than ACE, (3) extraction of OWL axioms from sentences
of the language, and (4) handling ambiguity associated with formalization.
The remainder of the paper is structured as follows: in Section 2,
we discuss the related works. In Section 3, we discuss the important

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arXiv:1709.08448v3 [] 28 Sep 2017

ABSTRACT
Authoring of OWL-DL ontologies is intellectually challenging and
to make this process simpler, many systems accept natural language text as input. A text-based ontology authoring approach
can be successful only when it is combined with an eﬀective
method for extracting ontological axioms from text. Extracting axioms from unrestricted English input is a substantially challenging task due to the richness of the language. Controlled natural
languages (CNLs) have been proposed in this context and these
tend to be highly restrictive. In this paper, we propose a new CNL
called TEDEI (TExtual DEscription Identiﬁer) whose grammar is
inspired by the diﬀerent ways OWL-DL constructs are expressed
in English. We built a system that transforms TEDEI sentences
into corresponding OWL-DL axioms. Now, ambiguity due to different possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges in this context. We ﬁnd that
the best way to handle these challenges is to construct axioms corresponding to alternative formalizations of the sentence so that
the end-user can make an appropriate choice. The output is compared against human-authored axioms and in substantial number
of cases, human-authored axiom is indeed one of the alternatives
given by the system. The proposed system substantially enhances
the types of sentence structures that can be used for ontology authoring.

INTRODUCTION

The key to building large and powerful AI systems is knowledge
representation. An ontology is a knowledge representation mechanism. It provides a vocabulary describing a domain of interest, and
a speciﬁcation of the terms in that vocabulary. However the manual creation of ontologies is intellectually challenging and timeconsuming [3]. Also the knowledge in an ontology is generally expressed in ontology languages such as RDFS [1] or OWL [6], which
are based on DL [17]. As a result, it is diﬃcult for non-logicians
to create, edit or manage ontologies. So a user-friendly format for
communication of ontological content is required for ontology authoring. Many systems accept natural language (English) text as input. Now a text-based ontology authoring system can be successful
only when it is combined with an eﬀective method for extracting
ontological axioms from text. Extracting axioms from English is
challenging due to its unrestricted and ambiguous nature. CNLs,
which are restricted unambiguous variants of natural languages,
have been proposed in this context.
Now, ambiguity due to diﬀerent possible lexicalizations of sentences and semantic ambiguity present in sentences are challenges
in the context of ontology authoring. It is diﬃcult to disambiguate
2017-10-02 00:09 page 1 (pp. 1-8)

POS Tag Patterns

English Text

Lexical
Ambiguity
Handler

Sentence Patterns

TEDEI CNL
Lexicalizations

TEDEI
Parser

TEDEI Lexicalizations

Semantic
Ambiguity
Handler

Transformation Rules
Interpretations

Syntactic
Transformation

ACE Parser
ACE Text

ACE-to-OWL
Translation

OWL Axioms

Non-TEDEI Lexicalizations

Figure 1: Architecture
grammar rules of TEDEI. In Section 4, we discuss ACE and transformation of TEDEI text to ACE. In Section 5, we describe ambiguity
in formalization and how it is handled by the proposed system. Section 6 describes the results and evaluation and Section 7 concludes
the paper.

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2

exploitation of lexico-syntactic patterns [15], utilization of
knowledge-rich resources such as linked data or ontologies [13],
and syntactic transformation [24]. Statistics-based techniques involve relevance analysis [2], co-occurrence analysis, clustering,
formal concept analysis, association rule mining [8] and deep
learning [20]. Wong et al. [26] presents a study on prominent
ontology learning systems such as OntoLearn Reloaded [23],
Text2Onto [4], and OntoGain [8]. Most of the current systems employ a combination of the aforementioned techniques.
In comparison with existing systems, the proposed system has
a streamlined approach to the generation of axioms from text. Existing systems attempt to convert any English sentence and the
process would either fail or generate an incorrect axiom in several
cases. However, in the proposed approach, since it is guided by a
grammar, the system can identify a sentence it can not handle and
give an error signal. Although existing systems might be using a
grammar, since it is not explicitly mentioned, it is diﬃcult to deﬁne the scope of the input. In our framework, the use of grammar
clearly deﬁnes the set of sentences the system can handle. Our approach gives the end-user an opportunity to rewrite the sentence
and possibly convey the same information in a diﬀerent way.
In addition, by using ACE in our framework, we incorporate the
advantage of using a CNL in our framework. Also, existing systems
generate only one formalization per sentence without taking into
the account the impact of ambiguity in formalization.

RELATED WORKS

In this section, we discuss the works related to ontology authoring and how the proposed system is an improvement over the existing systems. Section 2.1 discusses ontology authoring based on
CNLs. Note that a text-based ontology authoring approach needs
to have a corresponding solution to the problem of extracting ontology from text. The success of the authoring tool depends on
an eﬀective technique for extraction. Extracting ontology is also
termed as ontology learning in the literature and we review some
of the relevant works in Section 2.2.

2.1

CNL-based ontology authoring

Some CNLs that were developed speciﬁcally for creating OWL ontologies are ACE [11], Rabbit [7], CLOnE [12] and SOS [5]. However ACE is the most commonly used CNL for Semantic Web.
There are numerous tools based on ACE such as ACEWiki1 and
APE2 . ACE is designed to be unambiguous and less complex as
compared to standard English. In the context of ontologies, ACE
oﬀers a simple platform for domain experts who are not comfortable with ontology languages to author ontologies. ACE-OWL, a
sublanguage of ACE, has a bidirectional mapping with OWL.
Although ACE is used for ontology authoring, ACE has two
main limitations. Firstly, ACE is a subset of standard English. As a
result, many English sentences are not present in ACE. Secondly,
in some cases, even though a sentence is valid in ACE, the ACE
parser, named Attempto Parsing Engine (APE), fails to axiomatize
it because only a subset of ACE sentences map to OWL axioms.
Thus we ﬁnd that though ACE is good for authoring ontologies, it
restricts the user to a limited subset of English.
However, we noted that with suitable transformation, it is possible to make sentences ACE-compliant or OWL-compliant. The
proposed system addresses both the limitations of ACE in the syntactic transformation phase. Syntactic transformation carries out
the necessary transformation and as a result, it extends ACE for
the purpose of ontology authoring.

2.2

3 TEXT DESCRIPTION IDENTIFIER (TEDEI)

Only sentences having at least one lexicalization that is valid according to TEDEI rules are handled by our system. The expressivity of DL covered by the language deﬁned by TEDEI is ALCQ.
We use ANTLR [19] parser generator to generate the parser for
TEDEI. ANTLR takes TEDEI grammar as input and generates a recognizer for the language. We employ the recognizer to read the
input stream and check whether it conforms to the syntax speciﬁed by TEDEI.
We developed TEDEI keeping in mind the rich set of primitives
of OWL 2 [14]—the W3C recommended and widely adopted ontology language. We also mined Brown corpus [10] to identify various ways in which OWL primitives can be expressed in natural
language. We used various regular expressions to extract the patterns. Brown corpus, being a prominent text corpus, is rich enough
to contain various kinds of such patterns. Those patterns that correspond to some OWL primitive are encoded as rules in the grammar.
The non-terminals of the grammar correspond to OWL primitives. In this section, we describe only the important rules of the

Ontology learning

The main techniques in ontology learning from text are linguisticsbased and statistics-based. Linguistics-based techniques involve
1 http://attempto.iﬁ.uzh.ch/acewiki/
2 http://attempto.iﬁ.uzh.ch/ape/

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|
|
|

grammar. The primary rule in TEDEI corresponds to a class expression axiom. A class expression axiom deﬁnes a concept. The textual
description corresponding to a class expression axiom consists of a
concept and a class expression. An example is the sentence “Every
square contains right angles,” which consists of the concept square
and the class expression contains right angles.
A class expression can either be an atomic class or a complex
class. An atomic class denotes a single concept. A complex class
can be constructed in various ways. It is possible to employ set
operations (union, intersection, and complement) and property restrictions (existential restriction, universal restriction, cardinality
restriction, value restriction, and self-restriction.) It is also possible to construct complex classes by enumerating all the instances
in the class.
The natural language indicators for various OWL primitives are
given in the production rules of the grammar. Brown corpus has
been beneﬁcial in identifying these indicators. We consider the relative pronouns which, that, and who as the natural language indicators for intersection of classes. An example is the sentence “Every
square is a quadrilateral that has 4 right angles.” Sometimes, the absence of any such relative pronoun also indicates an intersection,
as demonstrated by the sentence “Every rectangle is a quadrilateral
having 4 right angles.” The natural language indicator for union
of classes is the conjunction or. An example is the sentence “Every polygon is concave or convex.” The determiner some indicates
existential restriction. An example is the sentence “Every rectangle
contains some right angles.” The determiner only indicates universal restriction. An example is the sentence “Every rectangle contains
only right angles.”
The important rules of TEDEI are outlined below in BNF notation. For the sake of readability, we provide only an abstract version of the rules originally written in ANTLR. The non-terminals
of the grammar are shown in italics and terminals are shown in
uppercase. CLASS, INDIVIDUAL, and PROPERTY are three special terminals of the grammar, and they correspond to ontology elements
class, individual and property respectively. The actual text tokens
that correspond to these terminals are identiﬁed during the lexicalization process. Sometimes, there may be multiple lexicalizations
for a sentence and we discuss the details of the lexicalization process in Section 5.1. Rest of the terminals such as THAT and WHICH
correspond to words or phrases of the input sentence.
start ::= lexpr rexpr

qualMaxCard //qualified maximum cardinality
indValueRes //individual-value restriction
selfValueRes //self-value restriction
classComb

complement ::=
preComplementInd PROPERTY classComb
| PROPERTY postComplementInd classComb
uniRes ::=
PROPERTY universalInd classComb
| universalInd PROPERTY classComb
existRes ::=
PROPERTY existentialInd classComb
| PROPERTY classComb

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exactCard ::=

PROPERTY exactCardInd CARDINALITY
| PROPERTY ambiExactCardInd CARDINALITY

minCard ::=

PROPERTY preMinCardInd CARDINALITY
| PROPERTY CARDINALITY postMinCardInd

maxCard ::=

PROPERTY preMaxCardInd CARDINALITY
| PROPERTY CARDINALITY postMaxCardInd

qualExactCard ::=

PROPERTY exactCardInd CARDINALITY classComb
| PROPERTY ambiExactCard CARDINALITY classComb

qualMinCard ::=

PROPERTY preMinCardInd CARDINALITY classComb
| PROPERTY CARDINALITY postMinCardInd classComb

qualMaxCard ::=

PROPERTY preMaxCardInd CARDINALITY classComb
| PROPERTY CARDINALITY postMaxCardInd classComb

indValueRes ::= PROPERTY INDIVIDUAL
selfValueRes ::= PROPERTY selfInd

classComb ::= CLASS (clsExpInd classComb)*

clsExpInd ::= AND | OR | ,
unionInd ::= OR

intersectionInd ::= THAT | WHICH | WHO | WHOSE
preCompInd ::= DOES NOT | DO NOT | DID NOT
postCompInd ::= NOT | NO

lexpr ::= CLASS | INDIVIDUAL
rexpr ::= union

universalInd ::=
EXCLUSIVELY | NOTHING BUT | NOTHING EXCEPT | ONLY

union ::= (intersection | complement) (unionInd union)*
intersection ::= clsExpComb (intersectionInd intersection)*

existentialInd ::=
A | AN | ALL | ANY | FEW | MANY | SOME | SEVERAL

clsExpComb ::= clsExp (clsExpInd? clsExpComb)*

exactCardinalityInd ::= EXACTLY | JUST | MAY BE | ONLY

clsExp ::= complement
| uniRes
//universal restriction
| existlRes
//existential restriction
| exactCard
//exact cardinality
| minCard
//minimum cardinality
| maxCard
//maximum cardinality
| qualExactCard//qualified exact cardinality
| qualMinCard //qualified minimum cardinality

ambiExactCardInd ::=
ABOUT | ALMOST | APPROXIMATELY | AROUND | CLOSE TO
preMinCardInd ::=
ATLEAST | LEAST | MORE THAN | NOT LESS THAN
postMinCardInd ::= OR MORE
preMaxCardInd ::=
ATMOST | MOST | LESS THAN | NOT MORE THAN | WITHIN
3

through syntactic transformation. The transformed sentences become valid ACE sentences. Table 2 lists down a few ACE sentences
that are not OWL-compliant, along with the reasons for their noncompliance. We then show corresponding sentences that are made
ACE-compliant through suitable transformation. The transformed
sentences can successfully be formalized into OWL.
Note that it might be possible to apply syntactic transformation either by modifying the grammar rules of ACE or by improving APE. However, ACE is a formal language that was developed
with a focus on knowledge representation. Since the focus of our
work is ontology authoring, we chose to have a separate module of
syntactic transformation, which is independent of ACE/APE. This
module will be unaﬀected by any update which would be made to
ACE/APE. Hence, we decided to use ACE only as an intermediate
language to generate axioms, instead of modifying ACE/APE.

postMaxCardInd ::= OR LESS
selfInd ::= MYSELF | OURSELVES | YOURSELF | YOURSELVES | HIMSELF |
HERSELF | ITSELF | THEMSELVES

4

SYNTACTIC TRANSFORMATION

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The proposed system employs syntactic transformation to convert
TEDEI text (i.e. sentences having at least one TEDEI lexicalization)
to ACE text. We implemented syntactic transformation as actions
that are attached to grammar elements in TEDEI. We describe here
the important steps in this transformation process.
We refer to the phrases in a sentence that correspond to OWL
concepts as concept-terms and the phrases that correspond to
OWL relations as relation-terms. For example, in the sentence “Every adenine is a purine base found in DNA”, purine base is a conceptterm, and found in is a relation-term.
In a sentence, a concept-term or a relation-term may contain
more than one word. As per the rules of ACE, multi-word terms
are required to have hyphens. We handle such terms by inserting
a hyphen between consecutive words in the term. For example, the
concept-term purine base is transformed to purine-base.
If a concept-term or a relation-term is not present in the ACE
lexicon, we add it to the lexicon dynamically by tagging it with the
preﬁx that indicates its word class. All noun phrases having common nouns are identiﬁed as OWL concepts and they are tagged
with the preﬁx n. All verb phrases are identiﬁed as OWL relations and they are tagged with the preﬁx v. All noun phrases having proper nouns are identiﬁed as OWL individuals and they are
tagged with the preﬁx p. For example, the concept-term purine base
is tagged with n: to denote that it is an OWL concept. These preﬁxes are as per requirement of the ACE parser.
According to the ACE construction rules, every noun should be
preceded by an article. In the absence of an article, we insert an
appropriate article. We consider the determiner a to be the default
article for OWL atomic concepts and the determiner some to be the
default article for OWL role ﬁllers.
We handle missing role ﬁllers by adding something as the role
ﬁller. This transformation is semantics-preserving because something denotes the top concept in OWL. For example, the sentence
all kids play is transformed to all kids play something.
In ACE, all coordinated noun phrases have to agree in number and verb form (either ﬁnite or inﬁnite). In this context, noun
phrases denote OWL role ﬁllers. In the case of a role ﬁller that is
coordinated by and and or, we distribute the individual role ﬁllers
to the relation according to ACE semantics. For example, the coordinated noun phrase likes cats and dogs is transformed to likes cats
and likes dogs.
In ACE, all coordinated verb phrases have to agree in number
and verb form (either ﬁnite or inﬁnite). In this context, verb phrases
denote OWL relations. In the case of a relation that is coordinated
by and and or, we distribute the individual relations to the role
ﬁller according to ACE semantics. For example, the coordinated
verb phrase seizes and detains a victim is transformed to seizes a
victim and detains a victim.
Table 1 lists down a few English sentences that are not ACEcompliant, along with the reasons for their non-compliance. We
then show corresponding sentences that are made ACE-compliant

5 AMBIGUITY IN FORMALIZATION

A natural language sentence can be ambiguous due to various reasons. We note that there are diﬀerent types of ambiguity such as
lexical ambiguity, scope ambiguity, syntactic ambiguity and semantic ambiguity. The concept of ambiguity is well-studied in the context of NLP tasks such as POS tagging, word sense disambiguation
and sentence parsing [18] [16]. For instance, syntactic parsing of a
sentence can often lead to multiple parse trees. Various algorithms
are used to identify the most-probable parse tree. However, as far
as we know, there is no concrete study of ambiguity in the context
of formalization of sentences into OWL axioms. We investigate
the impact of two types of ambiguities, namely lexical ambiguity
and semantic ambiguity. In the following section, we describe each
type in detail and how both are handled by the system.

5.1 Lexical Ambiguity

Lexical ambiguity is a major type of ambiguity associated with formalization of sentences. An ontology element (i.e. a class, relation
or instance) can be lexicalized in more than one way due to various
modeling possibilities. It depends on factors such as the domain, individual preferences, and application. As a result, it is possible to
generate multiple formalizations for the same sentence, by combining diﬀerent lexicalizations of its elements in various ways. For
example, consider the following sentence: Every vegetable pizza is
a tasty pizza. Note the adjective tasty that describes the noun pizza.
While formalizing the sentence, this adjective can either be lexicalized as a separate concept or associated with the corresponding
noun. This lexical ambiguity with respect to the adjective results
in two diﬀerent formalizations, as shown below:
(1) VegetablePizza ⊑ TastyPizza
(2) VegetablePizza ⊑ Tasty ⊓ Pizza
Lexically ambiguous sentences can be disambiguated by using
a point of reference such as an existing ontology [9]. In that case,
the lexicalization that best ﬁts the reference can be chosen. However, in the absence of suitable points of reference, the best possible
way is to generate all possible lexicalizations. Our system identiﬁes
lexical ambiguity associated with formalization and generates all
possible formalizations of the sentence.
Williams [25] studies the syntax of identiﬁers from a corpus of
over 500 ontologies. The identiﬁers chosen for the study are class

4

Table 1: Making English sentences ACE-compliant through syntactic transformation
English Sentence
Every battery produces electricity.
An adenine is a purine base.
An abdomen exists between thorax and
pelvis.
A kidnapper seizes and detains a victim.
Every binomial consists of two terms.

Reason for non-compliance
Every noun should be preﬁxed by a determiner.
Multi-term expressions should be hyphenated.
Coordinated noun phrases have to agree in
number and verb form.
Coordinated verb phrases have to agree in
number and verb form.
Prepositional phrases should be attached to
the corresponding verb.

Transformed Sentence
Every battery produces some electricity.
An adenine is a purine-base.
An abdomen exists between thorax and exists between pelvis.
A kidnapper seizes a victim and detains a
victim.
Every binomial consists-of two terms.

ACE Sentence
All kids play.

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Table 2: Making ACE sentences OWL-compliant through syntactic transformation

Reason for non-compliance
Intransitive verbs are not supported by APE
OWL generator.
Every person should learn some maths.
Modal verbs are not supported by APE
OWL generator.
Every abacus eﬃciently performs some Adverbs are not supported by APE OWL
arithmetic.
generator.
A console houses some electronic instru- Adjectives are not supported by APE OWL
ments.
generator.

Every person should-learn some maths.
Every abacus eﬃciently-performs some
arithmetic
A console houses some electronicinstruments.

one vehicle to become a driver. A universal quantiﬁcation is inappropriate due to the fact that a driver might drive any vehicle, not
necessarily a car.
Now consider a structurally similar sentence: Every vegetable
pizza is made of vegetable items. There are 3 possible interpretations of this sentence. The corresponding formalizations are:
(1) VegPizza ⊑ ∃madeOf.VegItems
(2) VegPizza ⊑ ∀madeOf.VegItems
(3) VegPizza ⊑ ∃madeOf.VegItems ⊓ ∀madeOf.VegItems
However, in this case, according to world knowledge and commonsense, the correct interpretation is denoted by the third formalization.
It is necessary to have access to suﬃcient background knowledge to disambiguate such sentences, as demonstrated above. However, in the absence of background knowledge, the best possible
way is to generate possible multiple interpretations of the semantically ambiguous sentence. We studied various sentence patterns
and then manually identiﬁed the ones that are semantically ambiguous. We also record as to how to map them to corresponding
interpretations. In the online phase, the system checks whether
the new sentence makes use of any one of the patterns that were
identiﬁed beforehand. In such cases, the system generates all the
corresponding interpretations of the sentence. In the case of sentences that do not contain any of the patterns, we generate only
one interpretation of the sentence. The list of sentence patterns
that indicate ambiguity is extendable.

identiﬁers, named individual identiﬁers, object property identiﬁers
and data property identiﬁers. According to the study, identiﬁers
follow simple syntactic patterns and each type of identiﬁer can
be expressed through relatively few syntactic patterns. These patterns are expressed using Penn POS tag set [21]. We adapt these
syntactic patterns in our approach.
First, we POS-tag each word in the input text using Stanford
part-of-speech tagger [22]. Then, we use the aforementioned syntactic patterns to extract all possible identiﬁers from sentences. By
properly combining the identiﬁers, we generate all the formalizations of the sentence. In case of the above example, the following
phrases/words are extracted as identiﬁers: vegetable pizza, is, tasty
pizza, tasty and pizza. From these identiﬁers, our system generates
both the given formalizations.

5.2

Transformed Sentence
All kids play something.

Semantic Ambiguity

Another type of ambiguity associated with formalization is semantic ambiguity. Due to semantic ambiguity, a sentence can have
more than interpretation. For example, consider the following sentence: Every driver drives a car. There are 3 possible interpretations
of this sentence. The formalizations corresponding to each interpretation are:
(1) Driver ⊑ ∃drives.Car
(2) Driver ⊑ ∀drives.Car
(3) Driver ⊑ ∃drives.Car ⊓ ∀drives.Car
Note the quantiﬁcations associated with each axiom. The ﬁrst axiom denotes the correct interpretation (and hence the correct formalization) of the given sentence. The existential quantiﬁcation is
appropriate due to the fact that a person should be driving at least
5

6

Every n:adenine is a n:purine-base and v:found-in a n:DNA.

RESULTS AND DISCUSSION

The details of the evaluation of our framework are given below. All
the datasets and evaluation results are available online3 .

6.1

axiom
Every n:adenine

Datasets

a n:purine-base and v:found-in a n:DNA.

Every adenine

clsExp
a n:purine-base
classComb
CLASS

a purine base

clsExp
v:found-in a n:DNA
existRes
PROPERTY classComb

a n:DNA
found in

CLASS

Axiom Generation

We use the ACE parser, Attempto Parsing Engine (APE), to translate ACE text into OWL axioms. The target ontology language chosen for formalization is OWL 2, in OWL/XML syntax.
Few examples to demonstrate syntactic transformation and axiom generation are given in Table 3. The table presents sentences
of various types and illustrates how the proposed system handles each type by generating the appropriate axiom. Each example
shows an input sentence and a formalization produced from the
sentence by the system. Here from the set of axioms produced by
the system we only show the most appropriate one. In Figure 2
we present a parse tree for the input sentence of the ﬁrst example Every adenine is a purine base found in DNA . The sentence is
translated through syntactic transformation in a bottom-up fashion resulting in an equivalent ACE sentence. The transformations
performed on the sentence are shown at respective locations in
the parse tree. In this case, the resultant ACE sentence is shown
at the root of the parse tree. The corresponding OWL-DL axiom is
Adenine ⊑ PurineBase ⊓ ∃foundIn.DNA.
Few examples to demonstrate handling of ambiguity are given
in Table 4. The given sentences are both lexically and semantically
ambiguous. Each example shows an input sentence and the various
axioms produced from the sentence by the system.

6.3

intersection

CLASS

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6.2

rexpr

lexpr

We use various types of datasets in our evaluation covering multiple domains. This facilitates concrete evaluation of our approach
and also ensures that our approach is neither dataset-oriented nor
domain-dependent. The datasets used for evaluation are PIZZA,
SSN, VSAO, PP1 , PP2 and LEXO. PIZZA, SSN and VSAO are obtained
from Emani et al. [9]. Each dataset consists of 25 sentences that
are text descriptions. Each dataset is based on a domain ontology.
PIZZA is based on Pizza ontology, SSN is based on Semantic Sensor Network ontology and VSAO is based on Vertebrate Skeletal
Anatomy ontology. PP1 and PP2 are built from scratch. We identiﬁed 40 classes from people-pets ontology4 and collected their descriptions from Wikipedia and WordNet resulting in PP1 and PP2
datasets respectively. LEXO is obtained from Völker et al. [24]. This
dataset contains 115 sentences covering a variety of domains.

dna

Figure 2: Parse tree for the input sentence “Every adenine is
a purine base found in dna."
Text Every adenine is a purine base found in DNA.
Axm Adenine ⊑ PurineBase ⊓ ∃foundIn.DNA
Text Sloppy giuseppe pizza is topped with mozzarella and
parmesan.
Axm SloppyGiuseppePizza ⊑ ∃toppedWith.Mozzarella ⊓
∃toppedWith.Parmesan
Text An interesting pizza is a pizza that has at least 3 toppings.
Axm InterestingPizza ⊑ Pizza ⊓ ≥3has.Toppings
Text Every abdication is the act of abdicating.
Axm Abdication ⊑ ∃actOfAbdicating.⊤
Text Every exotic species is a species that is not native to a
region.
Axm ExoticSpecies ⊑ Species ⊓ ¬∃isNativeToRegion

Table 3: Syntactic transformation and axioms generation

Grammatical Coverage Analysis
guaranteed to generate at least one valid OWL axiom from every
TEDEI sentence. For every dataset, the number of TEDEI sentences
is signiﬁcantly more than the number of ACE sentences. For instance, out of the 25 sentences in the PIZZA dataset, none of the
sentences are valid according to ACE, whereas 19 sentences are
TEDEI sentences. We can observe a similar pattern in every other
dataset.

We evaluated the quality of TEDEI by analyzing its grammatical
coverage. We compare the coverage of ACE and TEDEI. Our analysis shows that TEDEI has larger grammatical coverage as compared
to ACE.
The analysis is reported, in detail, in Table 5. IS refers to the
number of input sentences that are present in the dataset. AS refers
to the number of ACE sentences i.e. sentences that conform to
ACE grammar. TS refers to the number of TEDEI sentences i.e. sentences that have at least one TEDEI lexicalization. Our system is

6.4 Formalization
We evaluated the number of formalizations generated by the proposed system. We chose the datasets having at least 50% TEDEI

3 https://anon.to/SuLSzZ

4 http://sadi-ontology.semanticscience.org/people-pets.owl

6

DATASET
PIZZA
SSN
VSAO
IS
25
25
25
LX
677
5964
560
TLX
157
297
93
INP
260
594
186
VAX
206
180
72
Table 6: Evaluation of formalization on various datasets (IS

Text Quarks possess color charge.
Axm Quark ⊑ ∃possess.ColorCharge
Axm Quark ⊑ ∃possess.Color ⊓ ∃possess.Charge
Axm Quark ⊓ ∃possess.ColorCharge ⊑ ⊤
Axm Quark ⊓ ∃possess.Color ⊓ ∃possess.Charge ⊑ ⊤
Text A vegetarian pizza is an interesting pizza.
Axm VegetarianPizza ⊑ InterestingPizza
Axm VegetarianPizza ⊑ Interesting ⊓ Pizza
Axm VegetarianPizza ⊓ InterestingPizza ⊑ ⊤
Axm VegetarianPizza ⊓ Interesting ⊓ Pizza ⊑ ⊤

denotes the number of input sentences, LX denotes the total number of lexicalizations, TLX denotes the total number of valid TEDEI
lexicalizations, INP denotes the total number of interpretations,
and VAX denotes the total number of valid ACE axioms)

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Table 4: Handling of ambiguity

DATASET
PIZZA SSN VSAO PP1 PP2
LEXO
IS
25
25
25
40
40
115
AS
0
0
0
1
2
1
TS
19
19
16
7
22
22
Table 5: Comparison of grammatical coverage of TEDEI and
ACE on various datasets (IS refers to the number of input sen-

7 CONCLUSIONS AND FUTURE WORKS

In this paper, we propose an ontology authoring framework that
extracts class expression axioms from natural language sentences
through grammar-based syntactic transformation. The input sentences that conform to the grammar are transformed into ACE.
We then use ACE parser to generate OWL axioms from the transformed text. We also explore the eﬀect of ambiguity on formalization and construct axioms corresponding to alternative formalizations of a sentence.
Our framework clearly outperforms ACE in terms of the number and types of sentences the system can handle. In comparison
with LExO and other ontology learning systems, our framework
is a robust way to generate ontological axioms from text. TEDEI
helps in clearly deﬁning the scope of the system and provides the
ability to reject a sentence and ask for reformulation. Employing
ACE as an intermediate language aids formalization and reduces
the complexity of the system. The output of the system is compared against human-authored axioms and in a substantial number
of cases, human-authored axiom is indeed one of the alternatives
given by the system.
Our future works include enhancing the grammar so that the
framework can handle a larger range of sentences and investigating the impact of other types of ambiguities such as scope ambiguity on formalization.

tences, AS refers to the number of ACE sentences, and TS refers to
the number of TEDEI sentences.)

sentences for this evaluation. As can be observed from Table 5,
PIZZA, SSN, and VSAO have at least 50% TEDEI sentences. Hence,
these datasets are used for this evaluation.
The results are reported in Table 6. IS denotes the number of
input sentences in the dataset. LX denotes the total number of lexicalizations generated by lexical ambiguity handler for all the input
sentences. TLX denotes the total number of valid TEDEI lexicalizations i.e. lexicalizations that conform to TEDEI. INP denotes the
total number of interpretations generated by semantic ambiguity
handler. Each interpretation is the result of syntactic transformation of the TEDEI lexicalizations. VAX denotes the total number of
valid ACE axioms i.e. interpretations that conform to ACE.
As we can observe from the table, the proposed system generates many lexicalizations from a given input sentence, out of which
many are valid TEDEI sentences. Based on various sentence patterns, we identify semantically ambiguous sentences, which have
more than one interpretation. A major portion of the complete set
of interpretations are valid ACE sentences, each of which can be
converted to an equivalent OWL axiom. For instance, from the 25
sentences in the PIZZA dataset, 677 lexicalizations are generated,
out of which 157 are valid according to TEDEI. Subsequently, 260
interpretations are generated, out of which 206 are valid according
to ACE.
We also evaluated the correctness of the axioms. We chose the
PIZZA dataset for this evaluation. We manually authored gold standard set of axioms and compared it with axioms generated by the
system. Out of the 25 sentences in the dataset, for 17 sentences, the
human-authored axiom is indeed one of the alternatives given by
the system.

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