Text Analysis with Ontology Reasoning

Anna Rozeva

Department of Computer Systems and Informatics, University of Forestry, Kliment.Ohridski blv.10, Sofia, Bulgaria

arozeva@hotmail.com

Keywords: Text Analysis, Text Mining, Domain Ontology, Context Model, Ontology Reasoning, Knowledge

Management.

Abstract: The analysis of unstructured text when performed by text mining machine learning algorithm results in

mining model holding rules for relationships and dependencies among terms extracted by text pre-

processing techniques. The obtained mining model represents knowledge derived from the analyzed text

which is hard to interpret as it lacks context. Enhancement of its semantic value can be obtained by

implementing logic based approach providing formally defined meaning and interpretation mechanism. The

generally accepted form for representation of knowledge for existing domain is by domain-specific

ontology. The aim of the paper consists in designing a framework for performing ontology instantiation and

population with the structures of a complex mining model involving classification and association rules.

Procedures have been designed for annotating them with domain concepts and semantic types. The

framework provides for turning the mining model into a context model. Ontology reasoning is implemented

to validate the input mining model by rule semantic disambiguation and dependency conceptualization. The

framework implementation provides for outputting validated domain-related knowledge base in explicit and

machine-readable form as a resource that can be adapted for decision support.

1 INTRODUCTION

Text analysis has become a topic attracting

increasing research interest and efforts in the recent

decade. The reason consists in the enormous and

continuously growing amount of published

information on Internet. Searching for specific

subject area related documents more efficiently as

compared to the traditional keyword-based

techniques has emerged as one of the most

challenging and value-adding problems concerning

it. Classification of text documents has turned out to

be the most important task in text analysis so far as

the retrieval of information from the web is

concerned. The text analysis performed by

implementing classical syntactical and statistical text

mining techniques results in mining models that

generally lack sense and ignore the semantics of the

input documents.

Most recent researches present techniques and

approaches for ensuring the lacking semantic

integration in a text mining model and improving

machine-learning based text classification (Albitar,

Fournier and Espinasse, 2012; Garla and Brandt,

2012). They implement semantic resources like

thesauri and ontologies for text “conceptualization”

(Albitar et al., 2012) or taxonomical structures for

improving feature ranking and semantic similarity

measures for projecting text into a feature space

(Garla et al., 2012) thus enriching the semantics of

the classification output.

Machine accessible semantics of text mining

models is achieved by means of ontologies

(Horrocks, 2008). They represent hierarchically

structured conceptual schemas that give formally

defined meanings to concepts referring to a specified

domain. As a model of a piece of the world it

provides vocabulary describing the aspects of the

modelled domain as well as an explicitly stated

meaning of its content. The meaning generally

represents classification information.

Ontology-based methods have been implemented

for refining text analysis and information extraction

process. Extraction of links between concepts for

capturing domain semantics by using domain

specific ontology has been considered in (Morneau

and Mineau, 2008).

Ontologies provide semantic frameworks for the

interpretation of analyzed information. This

becomes possible by using logical formalisms for

their representation. Description logic (DL) (Baader

Rozeva A.

Text Analysis with Ontology Reasoning.

DOI: 10.5220/0004774100640073

In Proceedings of the Third International Symposium on Business Modeling and Software Design (BMSD 2013), pages 64-73

ISBN: 978-989-8565-56-3

 2013 by SCITEPRESS – Science and Technology Publications, Lda. All rights reser ved

et al., 2007) is a knowledge representation

formalism distinguished by formal semantics and

provision of inference services. It is the foundation

of ontology languages like OWL DL (Motik et al.,

2012). Ontology based on description logic provides

reasoning tools and services for designing and

maintaining qualitative ontologies, answering

queries over its classes and instances, integrating

and mapping ontologies. Therefore ontology

reasoning facilitates the design and development of

ontologies as well as their deployment in

applications.

The intensively researched problem areas

mentioned so far provide the motivation for the

current work. The aim is to design a framework for

implementation of domain ontologies and reasoning

services in text document analysis enabling the

design of qualitative and validated domain related

knowledge. The rest of the paper is organized as

folows: Section 2 examines approaches, platforms,

mechanisms and applications of ontologically based

text analysis; Section 3 presents the conceptual

structure of our framework for ontological text

analysis; Sections 4 and 5 highlight framework

implementation issues and Section 6 concludes with

discussion and directions for future work.

2 REVIEW ON ONTOLOGIES IN

TEXT ANALYSIS

Text analysis is used in the sense of automatic

processing of huge volumes of textual information in

order to facilitate its retrieval, management and

structuring for research purposes. On the other hand

this notion concerns also the extraction of context

and meaning from the processed text corpus. Each of

these aspects is performed by the implementation of

specific information technology.

The technology which converts unstructured or

semi-structured natural language text in a form that

is suitable for machine processing which results in

models of extracted facts, discovered implicit links

and generated hypotheses, is referred to as text

mining. The models obtained by text mining

represent knowledge which needs to be properly

interpreted, shared and integrated. This can be

accomplished by combining them with

corresponding conceptual models which reflect the

structure of the subject area the knowledge is

referring to and defines the interpretations of its

terms, i.e. ontologies. They specify concept meaning

and their relations to other concepts in machine-

readable form. Ontologies therefore provide for the

automatic semantic interpretation of textual

information and enhance the benefits of text mining.

Further on text analysis will be distinguished as text

mining analysis and ontological analysis.

Figure 1 shows a framework for text mining

analysis performed on unstructured text corpus that

is presented in (Rozeva, 2011a).

Figure 1: Text mining analysis framework.

Within the above shown framework text is

lexically and syntactically processed for the

extraction of descriptive terms. They are determined

on the basis of evaluating frequency of appearance

in a document. Different methods for frequency

calculation can be implemented for enhancing the

relevance of the extracted terms to the document

content. The extracted descriptive terms provide for

presenting documents by term vectors. Each

document is represented by a vector, which contains

the extracted terms and their frequency of

occurrence in it. Obviously the document vector

matrix is sparse. The matrix is processed with

machine learning algorithms and in result mining

model is obtained. It stores patterns in the form of

classifications, groups and associations mined from

the input documents.

Approach for implementation of text mining

model within a specified platform for predicting

unknown features of new documents is presented in

(Rozeva, 2011b).

2.1 Ontological Text Analysis

Basic aspects and practices of using ontologies with

text mining are reviewed in (Spasic et al., 2005). So

far as ontology provides the terminology of a

domain, terms are set to express specialized domain

concepts. The mapping of term to concept is

considered as the basis for the semantic

interpretation of text mining models. Different layers

of text annotation by means of ontologies are

examined, i.e. lexical, syntactic and semantic.

A solution for the term ambiguity problem as a

general text mining challenge is reported in (Bratus

et al., 2009). It concerns cases when a term refers to

Text Analysis with Ontology Reasoning

multiple concepts which are normal for natural

language texts. The textual case-based reasoning

system developed performs taxonomic indexing of

text archives which provides for ontology-guided

search. It requires text classification and

disambiguation. The implemented classification

method locates key phrases in lexical taxonomies.

Disambiguation uses context to determine the

taxonomy regions, which are likely to be most

relevant. Algorithm has been designed for ontology-

guided text disambiguation.

Use of ontologies during the text analysis

process and as an export format for the results

obtained is shown in (Witte et al., 2007). Software

document ontology has been designed for being

automatically instantiated by text mining module.

The concepts to be included therein are the ones

related to: document structure or lexica; lexical

normalization rules; relations between classes and

software specific entities. The text mining system

for the instantiation of pre-modelled ontology

(ontology population) has been designed within the

GATE framework (Cunningham et al., 2011) for text

engineering, language processing and text analysis.

Entities resulting from natural language processing

(NLP) populate the ontology which is further on

processed for named entity recognition. Entities are

matched against a list of terms and in case of match

an annotation is added. An ontology-aware

component incorporating mappings between term

lists and ontology classes is used to assign the proper

class in case of term match. Specially designed

grammar rules are used to detect and annotate

complex named entities. They refer to the

annotations created and evaluate the ontology

directly. They detect semantic units by combining

ontology-based look-up information and extracted

noun phrases from documents. By implementation

of fuzzy-set based co-reference resolution system

detected entities are grouped into chains.

Normalization with a set of lexical rules provides

entities with canonical names. Rules are stored in

their corresponding classes in the domain ontology

which allows the inheritance of rules through

subsumption. Relations between entities are detected

by grammar rules and syntactic analysis. The

obtained relations are filtered through the ontology

for discarding the semantically invalid ones.

Integration aspects of formal ontologies in OWL-

DL format with text mining systems for supporting

NLP are discussed in (Witte et al., 2007). Ontology

is implemented both as a container for mining

results and as a language for document processing.

Requirements for the necessary information to be

included in ontology for supporting text mining

tasks are defined. Issues concerning interfacing

ontologies to text mining systems have been

considered as well.

Ontology population methods have been

considered in (Wang et al., 2005) and (Damljanovic

et al., 2009). Learning and populating ontology from

linguistic resources is presented in (Wang, et al.,

2005). Special attention is turned to the extraction of

related concepts and the method applied prevents

them to be far apart in the concept hierarchy.

Creating adapted workflows for semi-automatically

ontology population with text from diverse semantic

repositories is reported in (Damljanovic et al., 2009).

Framework for ontology-based text

categorization which performs ontology learning by

mining input documents and uses the ontology

further on for refining the categorization with both

supervised and unsupervised machine learning

approaches is presented in (Bloehdorn et al., 2005).

The framework provides ontology learning

algorithms such as for constructing taxonomies with

conceptual clustering algorithm; for classifying

instances into the ontology by using lexico-syntactic

patterns; for extracting labeled relations and

specifying their domain and range and for semantic

enrichment by finding the appropriate concept from

a given ontology, etc. The lexica and concept

hierarchies of ontologies are involved in performing

text clustering and classification by enhancing the

common term representation of documents with

concepts extracted from the used ontologies.

Ontologies enable specific concepts found in text to

be replaced by more general concept representations,

i.e. the corresponding superconcept along the path to

the root of the concept hierarchy. More recent

review on attempts to combining machine learning

techniques and ontologies by investigating mining of

semantic web data sources with inductive learning

techniques is presented in (Bloehdorn and Hotho,

2009).

2.2 Ontology Querying and Reasoning

Basic advantage of the implementation of formal

ontologies in text analysis is that it enables the

definition of semantic queries on concept instances

and automated reasoning based on DL. Reasoning

(Horrocks, 2008) has to check that ontology

knowledge is meaningful, correct, minimally

redundant, richly axiomatised, that queries can be

answered over its classes and instances, that

individuals matching a query can be retrieved and

that the knowledge base is consistent.

Third International Symposium on Business Modeling and Software Design

Many inference tasks are reduced to subsumption

reasoning and to satisfiability.

Efficiency of query answering reasoning task is

discussed in (Pothipruk and Governatori, 2005). DL-

based reasoning performs inference on a knowledge

base consisting of terminological axioms (Tbox) and

assertion axioms (Abox). Tboxes refer to the schema

of concepts and Aboxes to the names of individuals.

Reasoning on the terminological hierarchy, specified

by DL first order formulas, checks the fulfillment of

the following logical requirements: concept

satisfiability; class subsumption; class consistency;

instance checking.

A subset of reasoning rules (Wang et al., 2004)

is shown in Table 1.

Table 1: Sample OWL ontology reasoning rules.

Transitive_

Property

(?P rdf:Type owl: TransitiveProperty)

∧

(?A

?P ?B)∧ (?B ?P ?C)⇒ (?A ?P ?C)

subClassOf

(?a rdfs:subClassOf ?b) ∧

(?b rdfs:subClassOf ?c) ⇒

(?a rdfs:subClassOf ?c)

subPropertyOf

(?a rdfs:subPropertyOf ?b) ∧

(?b rdfs:subPropertyOf ?c) ⇒

(?a rdfs:subPropertyOf ?c)

disjointWith

(?C owl:disjointWith ?D) ∧

(?X rdf: type ?C) ∧

(?Y rdf: type ?D)⇒

(?X owl:differentFrom ?Y)

inverseOf

(?P owl:inverseOf ?Q) ∧ (?X ?P ?Y) ⇒

(?Y ?Q ?X)

The proposed optimization approach concerns

Abox reasoning, as the basis for query answering.

Two types of queries allowed by DL are considered.

The Boolean query represents instance checking and

the non-Boolean consists in retrieving Abox content.

The approach addresses the problem of efficient

answering a query in a DL-based semantic web

system implementing single ontology and multiple

data sources.

Architecture for ontology reasoning is proposed

in (Pan, 2007). It supports ontology languages

providing for the definition of customized data types

and customized data type predicates. It allows new

data type reasoners to be added into the architecture

without affecting the basic concept reasoner.

Ontology for modeling context and supporting

context reasoning has been designed in (Wang et al.,

2004). Its upper level captures general concepts

about the basic context. It allows hierarchical adding

of domain-specific ontologies. Logic reasoning on

this ontology consists in checking the consistency of

context information and deriving high level implicit

context from low level explicit context.

Ontologies provide the vocabulary that enables

the semantic markup of web resources. Thus they

express the terminological part of knowledge

structured in taxonomy of concepts and properties.

DL based language as OWL enables reasoning for

ontology checking, classification and recognition of

class instances. Rules on the other hand describe

logical dependencies between the ontology elements

and as such represent the deductive type of

knowledge. Rule types are: standard – for chaining

ontologies’ properties; bridging – for reasoning

across several domains; mapping – ontologies in

data integration; querying – for expressing complex

queries in ontology vocabulary and meta – for

facilitating ontology engineering (acquisition,

validation and maintenance).

As considered in (Golbreich, 2004) completeness

of inferences can be obtained on the basis of the

whole domain knowledge available both in

ontologies and rule bases. Approach for reasoning

by combining them is designed based on Semantic

web standards language OWL and SWRL (Horrocks

et al., 2004). The platform for combining DL and

rule reasoning is shown in Figure 2.

Figure 2: Combining ontology and rule reasoning.

Modeling approach for developing rule-based

application for the web is presented in (Canadas,

Palma and Tunes, 2009). It implements ontologies

for describing the concepts and their relationships in

the application domain and rules for formalizing the

inference logic thus providing for increasing the

amount of knowledge represented in ontologies.

3 CONTEXT-BASED TEXT

ANALYSIS FRAMEWORK

The review of related work on approaches, methods,

technologies and tools presented in the previous

Text Analysis with Ontology Reasoning

section has motivated the design of a framework for

analysing web text documents resulting in a

knowledge base that can be queried with automatic

semantic reasoners for inferring implicit facts. In our

previous work (Rozeva, 2012) we’ve presented an

approach for implementing pre-defined ontology at:

 Text pre-processing step for filtering terms that

don’t map to ontology individuals;

 Post-processing the rules learnt by implementing

mining algorithm.

The approach implemented in the current work uses

the rules model obtained by processing the text with

machine learning algorithm for instantiation of

domain ontology. This provides for enhancing the

efficiency of the representation of the analysed

domain by turning the mined model into a context

model. The context model being ontologically based

ensures logically validated classification and logic

reasoning. The framework is shown in Figure 3.

Figure 3: Ontological text analysis framework.

The modules in the framework are:

 NLP – natural language text processing resulting

in term extraction;

 Preparation of document vector matrix for

algorithmic processing;

 MLA - machine learning algorithm producing

mined model;

 Ontology instantiation with the mined model

producing semantically labelled context model;

 Ontology reasoning for inferring new facts

resulting in validated domain related knowledge.

MLAs that are usually applied for text analysis

purposes are: discovering groups of text documents

by clustering; classification (grouping according to

pre-defined categories) and associations.

The core of the proposed framework is the

instantiation of ontology from the rules of mined

model obtained by processing the text with machine

learning algorithm. This process refers to defining

classes, arrangement of classes in a taxonomic

hierarchy, defining properties of classes and allowed

values for them, filling in the property values for the

individuals of the classes. The framework

implements classification and association rules

machine learning algorithms. The classification

algorithm provides for building the ontology class

hierarchy. The association rules algorithm enables

the definition of non-taxonomic relations between

classes. The method for ontology building is

inspired by the method presented in (Elsayed et al.,

2007). Their approach uses the rules obtained by

mining structured data for mapping tree nodes to

OWL classes, tree branches to OWL classes and leaf

nodes to individuals. The ontology building

algorithm implements functions for: getting the

branches of a node and the branch of a leaf node;

getting the class that represents a branch and for

creating individual for a leaf node. This algorithm is

adapted in the proposed framework for taking input

from rules mined from text and is enhanced with the

definition of additional objectType property of the

classes. The method for enriching the ontology with

non-taxonomic relations is based on similar research

held on lexico-syntactic patterns from domain

specific dictionary for extracting relations between

concepts shown in (Maedche and Staab, 2000) as

well as on an approach for mining dictionary

databases for ontology generation purposes

presented in (Deliyska et al., 2012).

3.1 Building Ontology Taxonomy

The proposed algorithm for ontology instantiation

comprises two modules. The first one creates

ontology from a decision tree mining model. The

second one performs ontology enrichment by

creating relations between ontology classes from the

results of an association rules model. The general

structure of decision tree model shown in Figure 4

provides the input for the ontology building module,

i.e.:

 Predictable attribute name;

 Node ID and node name;

 Node type – root, interior or distribution;

 Node children cardinality – 2 or 0;

 Node parent;

 Node description – inputAttributeValue_ missing

or inputAttributeValue_existing;

 Node rule, containing attribute and predicate

values;

 Node distribution – probability of predictable

Third International Symposium on Business Modeling and Software Design

attribute values.

Figure 4: Decision tree text analysis model.

The predictable attribute in a text classification

task is the document category (Topic). The tree is

split by the presence or absence of an input attribute

value. The input attribute in the text classification is

Term with values extracted in the NLP stage.

Cardinality for interior nodes is two, denoting

existing or missing term value. Each node has

probability distributions of predictable attribute

values attached. The algorithm steps for mapping the

decision tree to ontology components are:

Step 1: Define independent class for the distribution

with classification topics and probability;

Method:

Class Distribution = new(OWL:class)

Distribution.Id= Distribution.name

DatatypeProperty DistributionDP=new

(owl:DatatypeProperty)

Class Topic = new(OWL:class

subClassof Distribution)

Topic.Id= Topic.name

DatatypeProperty TopicDP=new

(owl:DatatypeProperty)

Class TopicProbability =

new(OWL:class subClassof

Distribution disjointWith Topic)

TopicProbability.Id=

TopicProbability.name

DatatypeProperty

TopicProbabilityDP=new

(owl:DatatypeProperty)

Step 2: Define independent class for the nodes’

rules;

Method:

Class NodeRule = new(OWL:class)

NodeRule.Id= NodeRule.name

DatatypeProperty NodeRuleDP=new

(owl:DatatypeProperty)

Class Predicate = new(OWL:class

subClassof NodeRule)

Predicate.Id= Topic.name

DatatypeProperty NodeRuleDP=new

(owl:DatatypeProperty)

Class TermValue = new(OWL:class

subClassof NodeRule disjointWith

Predicate)

TermValue.Id= TermValue.name

DatatypeProperty TermValue DP=new

(owl:DatatypeProperty)

Step 3: Define independent class for the nodes’

probability;

Method:

Class Probability = new(OWL:class)

Probability.Id= Probability.name

DatatypeProperty ProbabilityDP=new

(owl:DatatypeProperty)

Step 4: Define the root class for the taxonomy of the

interior and terminal nodes;

Method:

Class All = new(OWL:class

disjointWith Distribution

disjointWith NodeRule disjointWith

Probability)

All.Id= All.name

DatatypeProperty All=new

(owl:DatatypeProperty)

Step 5: Define the class taxonomy from the interior

and terminal nodes;

Method:

BEGIN

For each node DN

Class C=new (owl:Class)

C.Id= DN.name

DatatypeProperty DP=new

(owl:DatatypeProperty)

Dp.Id= DN.name+”_Value”

Dp.AddDomain(C)

For each ChildNode CN of Get-

Children(DN)

Dp. AddDomain (CN.Get-Class ())

endfor

End

Step 6: Define properties and make the relation of

the class taxonomy to the independent classes;

Method:

ObjectProperty hasDistribution =

new(Owl:FunctionalObjectProperty)

ObjectProperty hasProbability =

new(Owl:FunctionalObjectProperty)

ObjectProperty hasRule =

new(Owl:FunctionalObjectProperty)

AddSubclassOf(All, hasDistribution)

AddSubclassOf(All, hasProbability)

AddSubclassOf(All, hasRule)

Step 7: Create individuals for ontology classes;

Text Analysis with Ontology Reasoning

Method:

BEGIN

For each node DN

Individual I = new

(owlclass:Individual)

Foreach Topic T

Distribution.I+=

T+TopicProbability

Endfor

NodeRule.I+= Predicate+TermValue

ADDType(Distribution.I, I)

ADDType(NodeRule.I, I)

Endfor

End

The ontology instantiated by this module involves

the hierarchical relations between the defined

classes. By including non-hierarchical relations as

well further enrichment and refinement of the

generated ontology will be obtained.

3.2 Ontology Enrichment

Ontology enrichment is achieved by the second

module in the framework which introduces non-

hierarchical relations between ontology classes.

These relations are extracted by applying the

association rules algorithm of Srikant and Agrawal

(as cited in Maedche and Staab, 2000) on the

processed text documents. Association rules are in

the form X →Y with measures for confidence and

support, where ‘X’ and ‘Y’ represent items in a

transaction set.

When applied to hierarchically structured

ontology classes the right side of the rule involves

the ancestors of the particular item as well. The

resulting rules contain ‘Y’ that isn’t ancestor of ‘X’

and the rule X→Y isn’t subsumed by one involving

their ancestors. The association rules produced by

the text mining algorithm are to be implemented as

properties between ontology classes’ individuals.

The algorithm implements a method which assumes

that individual corresponding to antecedent ‘X’ of

the association rule exists in ontology. It involves

the following method:

Method:

For each rule X→Y

If exist(owlclass.Individual=”Y”)

Begin

ObjectProperty Prop_Name =

new(Owl:ObjectProperty)

AssertObjectProperty (X,

Prop_Name, Y)

End

ElseIf

Begin

Class Association = new(OWL:class

disjointWith class.X)

Association.Id= Association.name

DatatypeProperty Association =new

(owl:DatatypeProperty)

Association:Individual=”Y”

AssertObjectProperty (X,

Prop_Name, Y)

End

Endif

EndFor

The method checks the presence of an individual ‘Y’

in ontology and creates object property which relates

it to the individual ‘X’. If not present new class is

added to ontology as disjoint with the class

individual ‘X’ belongs to. Individual ‘Y’ is added to

this class. Object property is created and is asserted

to relate individuals ‘X’ and ‘Y’.

4 FRAMEWORK

IMPLEMENTATION

The text mining module of the framework has been

implemented in Microsoft SQL Server 2008

(Microsoft SQL Server, 2013). The classification

model obtained by mining the text with Microsoft

Decision Tree algorithm is shown in Figure 5.

Figure 5: Decision tree mining model.

Topic’s distribution created for each tree node is

shown in Figure 6. Term extraction and term lookup

transformations have been implemented for text

preparation. The document corpus consists of

conference papers on e-Governance.

Figure 6: Classification topic distribution.

Third International Symposium on Business Modeling and Software Design

For instantiating the ontology the approach for

mapping database schema to ontology presented in

(Yankova et al., 2008) has been implemented. The

general form of the script statement mapping table

column to ontology classes is:

_columnToURI.putForward(“Table.Field”,

“Ontology:hasField)

The instantiated ontology with the decision tree

model by implementing the algorithm in Protégé 4.2

(Protégé 4 User Documentation, 2013) is shown in

Figure 7.

Figure 7: Ontology instantiated with decision tree model.

The association rules mining model obtained by

processing the text corpus with the Microsoft

Association Rules algorithm is shown in Figure 8.

Figure 8: Association rules mining model.

The ontology enriched with the mined rules is

shown in Figure 9. The rule “administrative reform”

= Existing → e-government = Existing has been

inserted in ontology as shown with arrows by:

 Creating new class Reform;

 Creating individuals for the class, i.e.

Administrative, Management and Budget;

 Creating object property “involves”;

 Asserting the “involves” property to “e-

government” individual of class “E-

government_existing” with the individual

“Administrative” from the Reform class.

The ontology instantiated by the proposed

framework has been classified with Pellet reasoner

(Pellet Reasoner Plug-in for Protégé 4, 2013) –

Figure 10.

Figure 9: Enriched ontology with association relations.

Figure 10: Classified ontology.

5 REASONING EXAMPLES

A classified ontology can be searched by logical

queries. Figure 11 presents sample DL query for

retrieving class individuals with restriction on

dataType property. Class queries get subclasses or

descendents in the hierarchy or the superclass of a

class.

Figure 11: DL query on classified ontology.

Text Analysis with Ontology Reasoning

Figure 12 presents sample OWL2 query in graph

view. The TBoxes, ABoxes and RBoxes are used to

provide concept relations, relations between

individuals and concepts and rules to the query.

Figure 12: Owl2 Query on classified ontology.

6 CONCLUSIONS

A framework for text analysis involving the building

and instantiation of ontology from analysis model

obtained by text mining has been proposed. It has

been designed with the aim of enhancing the

semantic content of analysis model rules and turning

them into a knowledge rule base which enables

processing with logic reasoning. Most of the

reviewed work on ontologies in text analysis

considers natural language processing and

validations with dictionaries and domain ontologies.

Current approach addresses algorithmically

processed text. It is considered that ontology

obtained from mining model can be treated as more

relative representation of text corpus overall content.

By structuring a rule model into ontology the

proposed framework ensures model’s semantic

enrichment. The contribution of the paper is the

defined approach for representing a complex text

mining model as concise domain ontology. The

examined mining model is complex because it

contains both classification and association rules.

Thus ontology proves to be the means for integrating

knowledge rules from different types of machine

learning text processing. It also provides the

processing tools for extracting the logical content

and meaning of the integrated rules.

The framework couples a text mining and

ontology instantiation modules. Methods are created

for the automatic mapping of rules into ontology

elements – classes, object and data properties and

instances. The framework has been implemented for

classification and associations analysis of selected

text corpus. Classification as the most typical

analysis task produces rules that enable the natural

mapping to ontology class hierarchy. The framework

implementation resulted in ontology that has been

successfully classified by a reasoner. Examples for

searching the classified ontology with description

logic and OWL2 queries have been provided. It’s

claimed that the ontology mapping besides for

mining models integration turns the mining model

into a context model with enhanced semantic

meaning of its rules, initially extracted from text by

machine learning algorithm.

The framework presented has been focused

primarily on the terminological part of the process of

ontology building. Future work is intended in

enhancing its axiom part as well as on mapping of

other text mining models on ontology.

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