USING ASSOCIATION RULES TO LEARN CONCEPT

RELATIONSHIPS IN ONTOLOGIES

Jon Atle Gulla, Terje Brasethvik and Gøran Sveia Kvarv

Department of Computer and Information Sciences

Norwegian University of Science and Technology, Trondheim, Norway

Keywords: Ontology engineering, text mining, association rules, domain modelling.

Abstract: Ontology learning is the application of automatic tools to extract ontology concepts and relationships from

domain text. Whereas ontology learning tools have been fairly successful in extracting concept candidates,

it has proven difficult to detect relationships with the same level of accuracy. This paper discusses the use of

association rules to extract relationships in the project management domain. We evaluate the results and

compare them to another method based on tf.idf scores and cosine similarities. The findings confirm the

usefulness of association rules, but also expose some interesting differences between association rules and

cosine similarity methods in ontology relationship learning.

1 INTRODUCTION

Traditional ontology engineering approaches are

tedious and labor-intensive, requiring a wide range

of skill sets as well as an ability to deal with very

complex and formal representations. In the modeling

process it is hard to manage and coordinate the

contributions from various types of domain experts

and ontology modelers. There are also technical,

political and economical challenges that severely

hamper the construction and maintenance of

ontologies. At the same time, the ontologies are

important in Semantic Web applications and

integration projects, as they provide the vocabulary

for semantic annotation of data and help applications

to interoperate and people to collaborate.

Most ontology engineering methods today are

based on traditional modeling approaches and

emphasize the systematic manual assessment of the

domain and gradual elaboration of model

descriptions (e.g. (Cristiani & Cuel, 2005; Fernandez

et al., 1997)).

Ontology learning is the process of

automatically or semi-automatically constructing

ontologies on the basis of textual domain

descriptions. The assumption is that the domain text

reflects the terminology that should go into an

ontology, and that appropriate linguistic and

statistical methods should be able to extract the

appropriate concept candidates and their

relationships from these texts. Numerous approaches

to ontology learning have been proposed in recent

years (e.g. (Haase & Völker, 2005; Navigli &

Velardi, 2004; Sabou et al., 2007)), and they seem to

allow ontologies to be generated faster and with less

costs than traditional modeling environments.

Even though many of the approaches display

impressive results, the complexities of ontologies are

so fundamental that the generated candidate

structures often just constitute a starting point for the

manual modeling task. Advanced approaches with

deep semantic analyses of text or whole batteries of

statistical tests tend to yield better results, but are

expensive to develop and may still not compete with

traditional ontology modeling with respect to

accuracy and completeness. So far, the best results

are for the learning of prominent terms, synonyms

and concepts. For more advanced constructions, like

relationships and rules, there are still very few good

tools out there to help us. Even though there are

some ontology learning tools with relationship

learning included, the accuracy of these relationships

are questionable and there has only been limited

work on comparing the various approaches to

relationship learning. This is unfortunate, as there

are indications that many of these approaches may

be successfully combined into more reliable

relationship learning approaches.

In this paper we present an approach to ontology

relationship learning that makes use of association

Atle Gulla J., Brasethvik T. and Sveia Kvarv G. (2008).

USING ASSOCIATION RULES TO LEARN CONCEPT RELATIONSHIPS IN ONTOLOGIES.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - ISAS, pages 58-65

DOI: 10.5220/0001686200580065

 SciTePress

rules. The theory of association rules comes from

data mining, though it can easily be adapted to the

task of extracting relationships between concepts in

domain text. The underlying idea is that concepts

tend to be related if it can be shown that they show

up together in documents with a certain

predictability. The technique neither distinguishes

between types of relationships nor identifies

relationship labels, but gives a first rough set of

candidate relationships to the ontology modelers.

The paper is structured as follows. Section 2

discusses the role of relationship learning in

ontology engineering. We then introduce the

association rules in Section 3 and briefly explain

how they are used to extract relationships between

concepts in Section 4. Section 5 introduces an

alternative approach to relationship learning, using

cosine similarities between concept vectors. An

evaluation and comparison of the two approaches

follows in Section 6, while some related work is

discussed in Section 7. The conclusions are found in

Section 8.

2 LEARNING ONTOLOGY

RELATIONSHIPS

An ontology can be regarded as a representation of a

set of domain concepts (also called classes or

objects) and their relationships. The concepts may

be taxonomically related by the transitive IS_A

relation or non-taxonomically related by a user-

named relation, for example, hasPart (Maedche &

Staab, 2000). Some also make a distinction between

non-taxonomic relations about whole/parts,

class/instance or associations in general.

Web Ontology Language (OWL) is a semantic

markup language recommended by the World Wide

Web Consortium for the representation of

ontologies. For the learning of relationships, OWL

has four primitives of particular interest:

♦

Class: A class defines a group of objects or

concepts that belong together.

♦

subClassOf: Stating that a class is a subclass

of another gives us the ability to create

generalization hierarchies of classes

♦

Property: Properties are used to define

relationships between concepts. A property

of a class

Person, for example, can be

hasChild or ownsCar.

♦

subPropertyOf: Hierarchies of properties can

be useful in structuring the ontology for easy

maintenance and extension. For example, the

property

hasRelative for a class Person may

be specialized into the subproperty

hasSibling.

Classes represent concepts that are

taxonomically related, while properties define non-

taxonomical relationships between concepts.

Association rules do not distinguish between these

two types of relationships and merely suggest

relationships of some kind between two or more

concepts. Moreover, the method is not able to derive

any candidate names of the relationships identified.

Used in an ontology learning environment,

association rules may give us a rough overview of

potential relationships between concepts in the

ontology. Other techniques or manual inspection are

needed to categorize the relationships and – if

needed – give them descriptive labels. Mapping

approved relationships to the OWL constructs

shown above, for example, still remains a manual

task.

The technique may be used to relate already

modeled concepts, but it usually includes a concept

extraction pre-phase that identifies the concepts to

be analyzed with association rules afterwards.

3 ASSOCIATION RULES FOR

TEXT MINING

Association rules is a data mining techniques that

identifies data or text elements that co-occur

frequently within a dataset. They were first

introduced in (Agrawal et al., 1993) as a technique

for market basket analysis, where it was used to

predict the purchase behavior of customers. This

was primarily done for large databases of items

purchased on per-transaction basis. An example of

such an association rule is the statement that ”90%

of the transactions that purchased bread and butter

also purchased milk.”

The problem in association rules mining can be

formally stated as follows:

Let I be a set of literals, called items. Let D be a

set of transactions, where each transaction T is a

set of items such that

IT ⊆

. A transaction T

contains X, a set of some items in I, if

TX ⊆

An assocation rule is an implication of the form

YX ⇒

, where

∩

⊂⊂ YXIYIX ,,

A rule

YX ⇒

holds in the transaction set D with

confidence c if c% of the transactions in D that

USING ASSOCIATION RULES TO LEARN CONCEPT RELATIONSHIPS IN ONTOLOGIES

contain X also contain Y. The rule

YX ⇒

has

support s in the transaction set D if s% of the

transactions in D contain

∪

The idea is to generate all association rules that

have support and confidence greater than a user

specified minimum support and minimum

confidence.

The most important algorithm for the generation

of association rules is the Apriori algorithm,

introduced in (Agrawal & Srikant, 1994). The

algorithm finds all sets of items that have support

greater than the minimum support. These sets are

called frequent item sets. For every itemset l in the

frequent itemset, L

, it finds subsets of size k-1. For

every subset X, it produces a rule

YX ⇒

, where Y

= l – X. The rule is kept if the confidence

support

/)( YX ∪

support

)(X

is greater than or equal to the minimum confidence.

In a text mining context, association rules may

be used to indicate relationships between concepts.

Let us assume that an item set is a set of one or more

concepts. If the rule

YX ⇒

has been confirmed, we

conclude that there is a relationship between the

concepts in X and the concepts in Y. With item sets

of size 1, we have rules that indicate relationships

between two concepts.

In order to run association rule mining on text,

we need to structure the text to mirror the situation

in data mining. Following (Delgado et al., 2002;

Haddad et al., 2000), we consider documents –

rather than sentences or paragraphs – to correspond

to transactions in data mining. Furthermore, we are

only interested in extracting relationships between

potential concepts, which means that we can restrict

the analysis to noun phrases only. We reduce the

noun phrases to their base forms, so that project

plans and project plan count as the same term and

only include noun phrases that have a certain

prominence in the document set. We then have

documents as item sets and lemmatized prominent

noun phrases as items and can run a standard

association rules analysis to extract relationships

between these prominent noun phrases.

4 LEARNING RELATIONSHIPS

FOR PROJECT MANAGEMENT

ONTOLOGY

Our ontology learning tool is built as an extension to

the GATE environment from the University of

Sheffield (Gaizauskas et al., 1996).

General Architecture for Text Engineering

(GATE) is an open source Java framework for text

analysis. It contains an architecture and a

development environment that allows new

components to be easily added and integrated with

existing ones. The architecture defines the

organization of a text engineering system, in which

each component is assigned particular

responsibilities. The framework comes with a set of

built-in components that can be used, extended and

customized to the specific needs of the analysis.

This includes NLP components like tokenizers, POS

taggers, sentence splitters and noun phrase

extractors, but also more extensive plug-ins for

multi-language stemming, WordNet retrieval,

machine learning and ontology editors.

An analysis with GATE typically consists of a

chain of components that one by one goes through

the text and annotate it with information that will be

needed by later components. With our own

components for association rules added, we built the

analysis chain shown in Figure 1 and explained in

more detail below. The analysis is run on a

repository of documents representative to the project

management domain. Whereas the GATE

components work on individual documents, we

developed our own modules for association rules

that pulled the individual files together, extracted

prominent noun phrases as keywords, and suggested

relationships between these phrases. The

components of the chain are:

♦ Tokenizer and Sentence splitter are GATE

components that split the document texts up

in tokens and identify sentences for analysis.

A token can be a simple word of something

like a number or a punctuation mark.

♦ The GATE tagger is a statistical tagger that

associates every word in the text with a part-

of-speech tag.

♦ Having identified the parts-of-speech of a

term, the lemmatizer can look it up in a

dictionary and retrieve its lemma, or base

form. The lemma is the common base form

of all inflections of the same lexical entry,

like the lemma

process for verb forms like

processes, processing, processed, etc.

♦ The noun phrase extractor identifies noun

phrases in the text of the form

Noun (Noun)*,

i.e. phrases that consist of consecutive nouns.

This means that a phrase like very large

databases will not be recognized, since very

is an adverb and large is an adjective,

whereas project cost plan is a perfectly

recognized phrase.

ICEIS 2008 - International Conference on Enterprise Information Systems

♦ The noun phrase indexer is responsible for

indexing noun phrases found in the

documents. After removing stopwords, the

component extracts and counts the

frequencies of noun phrases in the document

set. A normalized term-frequency score (tf

score) is used to select those prominent noun

phrases that are most likely to be concepts in

the domain. The result is a set of candidate

concepts.

♦ The association rules miner uses the Apriori

algorithm and extracts association rules

between the noun phrases (concepts) found

by the previous component. These

association rules constitute possible

relationships between ontology concepts of

the domain.

The relationship learning tool was set up for the

project management domain, using documentation

from a petroleum company as domain text. The

integration of GATE components with internally

developed components was unproblematic, though

the performance of the system would need to be

improved for large-scale document collections. A 76

document collection was loaded in 0,88 seconds on

average, and the complete analysis with this

collection took 6 minutes and 57 seconds.

5 AN ALTERNATIVE

RELATIONSHIP LEARNING

METHOD

An alternative method to association rules is the

traditional information retrieval approach with

calculations of cosine similarities between concepts.

Solskinnsbakk (Solskinnsbakk, 2007) presents an

implementation of such a system for the same

project management domain.

In this approach we make use of a vector of

weighted terms for each concept in an already

existing ontology. This concept vector is constructed

on the basis of a domain text collection and contains

words that tend to co-occur with the concept itself in

the text. If the term estimate appears with weight

0.21 in concept

Cost’s concept vector, it means that

estimate and Cost are to some extent used in the

same context (sentence, paragraph or document) and

should display some semantic similarities. The

weights are based on the tf.idf score, though we

boost co-occurrences in the same paragraph and

even more in the same sentence.

When all concepts are described in terms of

concept vectors, we may calculate the relatedness

between concepts using the cosine formula

where

and

are concept vectors and x

is the

weight of the i

word of vector

. If the cosine

similarity is above a certain threshold, we conclude

that there is a relationship between the concepts. The

set of all cosine similarities above this threshold for

all concepts pairs in the ontology is the system’s

suggested list of relationships in the domain.

6 EVALUATION

Evaluating ontology relationships learning systems

is notoriously difficult, as there are potential

relationships between all concepts and only

subjective judgment can tell the important ones from

the others.

GATE

tokenizer

GATE

tokenizer

GATE

Sentence

splitter

GATE

Sentence

splitter

GATE

Tagger

GATE

Tagger

GATE

Lemmatizer

GATE

Lemmatizer

GATE

Noun phrase

extractor

GATE

Noun phrase

extractor

Noun

phrase

indexer

Noun

phrase

indexer

Association

rules

miner

Association

rules

miner

Individual document analysis

Document repository analysis

GATE

tokenizer

GATE

tokenizer

GATE

Sentence

splitter

GATE

Sentence

splitter

GATE

Tagger

GATE

Tagger

GATE

Lemmatizer

GATE

Lemmatizer

GATE

Noun phrase

extractor

GATE

Noun phrase

extractor

Noun

phrase

indexer

Noun

phrase

indexer

Association

rules

miner

Association

rules

miner

Individual document analysis

Document repository analysis

Figure 1: Process for extraction relationships using association rules.

USING ASSOCIATION RULES TO LEARN CONCEPT RELATIONSHIPS IN ONTOLOGIES

A comparative evaluation of ontology relationship

learning may be more interesting than absolute

evaluations, as it may expose the differences

between the systems and reveal to what extent they

may be combined in hybrid approaches.

Concept Extraction. The domain chosen for the

evaluation was project management in STATOIL, a

large Norwegian petroleum company. They use a

particular project management methodology, PMI,

that is documented in handbooks and also reflected

in project documentation from their own projects.

Domain experts from STATOIL have together with

ontlogy modelers built a project management

ontology (Gulla et al., 2007), which served as a gold

standard for our concept extraction part.

Our association rules mining system was run on

STATOIL’s documentation of their project

management methodology, PMBOK (PMI, 2000).

This is a book of about 50.600 words (tokens)

divided into 12 chapter.

The system extracted a total of 196 concepts,

compared to the manually constructed ontology’s

142 concepts. 50 concepts were identical in both

sets, whereas some other 61 concepts found were

abstractions of similar concepts in the manual

ontology. If we assume that both the 50 perfect

matches and the 61 abstract matches are valid, we

have a precision of 56.7% and a recall of 78.2% for

the concept extraction part.

Relationship Learning. For the relationship part,

we compared the association rule approach to the

cosine similarity system explained above. The

manual ontology did not contain enough

relationships to be of much use in this part of the

evaluation. We first made a distinction between

three types of relationships found by the two

systems:

♦ Relationships suggested only by the

association rule approach

♦ Relationships suggested only the cosine

similarity approach

♦ Relationships suggested by both approaches

Slightly more than 50% of the relationships found

were also identified by the cosine similarity method.

A selection of concepts were chosen. For each of

the three groups above, all suggested relationships

to/from these concepts were shown to four persons

that all had project management experience. Each

person individually rated each relationship as not

related (these two concepts are not related), related

(there is probably a relationship between the two

concepts) or highly related (there is definitely a

relationship between these two concepts). An

average score for each relationship was calculated

on the basis of the individual scores from the test

persons. Figure 2 shows the related concepts

suggested for the ontology concept Cost for the three

groups, as well as their average scores.

Adding the results for all concepts together, we

can compare the quality of relationships for the three

groups. As shown in Figure 3, association rules and

cosine similarities tend to produce the same share of

good relationships (score Related and Highly

related). The two methods suggested 82% and 86%

good relationships, respectively, which is a fairly

good result for such a small document collection. It

should be noted, though, that this does not mean that

they necessarily suggest the same relationships.

The share of very good relationships is worth a

closer inspection. Whereas the association rules

method only generated 7% very good relationships,

the cosine similarity method reached an impressive

24%.

A possible explanation for this difference lies in

the mechanics of association rules and cosine

similarity. For an association rule to be generated,

the corresponding concepts need to occur is a wide

range of documents. This will typically be the case

for very general concepts and their rather general

relationships. The cosine similarity method, on the

other hand, makes use of tf.idf to characterize

concepts by their differences to other concepts, and

the relationships based on cosine similarities will be

based on these discriminating concept vectors. The

relationships get more specialized and precise and

are easier to recognize as very good relationships.

This may also explain why the association rule

method had a larger share of normally good

relationships (75%) than the cosine similarity

method (65%).

Interestingly, a combination of the two methods

seems to produce much better results that each

individual method. Both methods carry some noise,

but our results indicate that this noise is dramatically

reduced if we only keep the results that are common

to both methods. In total, 97% of the relationships

suggested by both methods were rated as good

relationships by the test group (right column in

Figure 3). 30% were considered very good

relationships. This suggests that the two approaches

– although comparable in quality – are

fundamentally different with their own weaknesses

and strenghts. Since overgeneration is already a

problem in relationship learning, a better approach

ICEIS 2008 - International Conference on Enterprise Information Systems

Related concepts only

from association rules

Related concepts only from

cosine similarity

Related concepts

from both methods

project management team

management team

organization

product

information

tool

project team

application area

risk analysis

result

risk

resource

consequence

estimate

phase

probability

action

analysis

seller

cost management

cost baseline

actual cost

schedule

project schedule

earn value

staff

project staff

milestone

plan value

stakeholder

project deliverable

earn value management

management

scope definition

scope management

customer

sponsor

project management IS

constraint

project manager

project plan development

procurement management

project plan execution

quality management

work breakdown structure

activity

assumption

control

cost estimate

performance

process

project

project management

project objective

project plan

quality

scope

scope statement

Figure 2: Relationships suggested for Cost. The average scores are NR (not related), R (related) or HR (highly related).

0 %

10 %

20 %

30 %

40 %

50 %

60 %

70 %

80 %

90 %

100 %

Relations only from

association rules

Relations only from

cosine similarity

Relations from both

methods

Not related

Highly related

Figure 3: Evaluation results for three categories of relationships.

might be to combine approaches and only accept

relationships that are supported by several methods.

As far as association rules and cosine similarities are

concerned, our research indicates that a combined

approach will display substantially better results

than each individual approach.

7 RELATED WORK

As discussed in (Cimiano et al., 2006), there are

today numerous ontology learning systems with

facilities for learning relationships (see Figure 4).

Association rules are already in use in some of these

systems.

USING ASSOCIATION RULES TO LEARN CONCEPT RELATIONSHIPS IN ONTOLOGIES

Systems Synonyms Concepts Hierarchy Relations

Text2Onto clusters X X X

HASTI X X

OntoBasis clusters clusters

OntoLT/

RelExt

X X

CBC/DIRT clusters clusters

DOOBLE X X

ASIUM clusters clusters X X

OntoLearn X X X X

ATRACT clusters clusters

Figure 4: Relationships learning in current systems.

Our approach to association rules is comparable to

what can be find in other ontology learning tools.

The accuracy of ontology relationship learning is

still not very impressive and suffers from both over-

generation and uncertainty. So far, the techniques

have also failed in coming up with good labels for

these relationships. Text2Onto has a particular

structure, the Probabilistic Ontology Model (POM),

that allows them to incrementally learn concepts and

relationships (Cimiano & Völker, 2005).

Our approach draws on many of the ideas

employed by Haddad et al. (Haddad et al., 2000). In

their work they also use documents as transactions

and focus on noun phrases as the carriers or

meanings and the objects of analysis. A similar

approach is taken in (Nørvåg et al., 2006).

Our research is now focused on the integration of

different relationship learning approaches. The

combination of association rules and cosine

similarity is promising, and has to our knowledge

not been done before.

Another interesting application of association

rules is presented in Delgado et al. (Delgado et al.,

2002). Their idea is to use association rules to refine

vague queries to search engine applications. After

the search engine’s processing of the initial query,

their system weights the words in the retrieved

documents with tf.idf and extracts an initial set of

prominent keywords. Stopwords are removed and

the remaining keywords are stemmed. Representing

the stemmed keywords of each document as a

transaction, their system is able to derive association

rules that relate the initial query terms with other

terms that can be added as a refined query.

Association rules have also been applied in web

news monitoring systems. Ingvaldsen et al.

(Ingvaldsen et al., December 2006) incorporate

association rules and latent semantic analysis in a

system that extracts the most popular news from

RSS feeds and identifies important relationships

between companies, products and people.

8 CONCLUSIONS

This paper presented an ontology relationship

learning approach that makes use of association

rules to identify relationships between concepts. The

approach is implemented as a text mining analysis

chain, with GATE as the underlying architecture and

our association rules components integrated with

GATE through their standard API. As such, it is

implemented as part of a comprehensive ontology

learning workbench that also includes a battery of

other ontology learning techniques.

Association rules provide a powerful and

straight-forward method for extracting possible

ontology relationships from domain text. The

relationships extracted may be both taxonomic and

non-taxonomic, though it is difficult to use the

analysis alone to decide on the nature of the

relationships. Of the relationships extracted for the

project management domain, about 82% were

considered valid relationships by a test group with

previous experience in project management.

However, association rules do not seem to be

substantially better than methods based on concept

vector construction and cosine similarity

calculations. The cosine similarity approach seems

to generate more specialized and precise

relationships than association rules. However, the

methods are complementary, since association rules

tend to focus on general relationships between high-

level concepts and cosine similarity approaches

focus on specialized relationships among low-level

concepts.

We are now investigating to what extent several

relationship learning techniques can be combined in

an incremental learning strategy. An extension of the

POM structure from Text2Onto may be useful in

this respect, though we need to carry over more than

just probability measures when these techniques are

applied sequentially. The whole set of uncertainties,

possibly supported by evidence in terms of vectors

or raw calculations, need to come together in such a

hybrid ontology learning framework.

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USING ASSOCIATION RULES TO LEARN CONCEPT RELATIONSHIPS IN ONTOLOGIES