KNOWLEDGE ACQUISITION WITH OM

A Heuristic Solution

Adolfo Guzman-Arenas and Alma-Delia Cuevas-Rasgado

Centro de Investigación en Computación Av. Juan de Dios Batiz, s/n, Zacatenco, 07738 México City

México and Instituto Tecnológico de Oaxaca Av. Ing. Victor Bravo Ahuja 125 and Calzada Tecnológico

68030 Oaxaca city, México

Keywords: Knowledge acquisition, knowledge models, ontology merging, ontology fusion, knowledge representation.

Abstract: Knowledge scattered through the Web inside unstructured documents (text documents) can not be easily

interpreted by computers. To do so, knowledge contained from them must be extracted by a parser or a

person and poured into a suitable data structure, the best form to do this, are with ontologies. For an

appropriate merging of these “individual” ontologies, we consider repetitions, redundancies, synonyms,

meronyms, different level of details, different viewpoints of the concepts involved, and contradictions, a

large and useful ontology could be constructed. This paper presents OM algorithm, an automatic ontology

merger that achieves the fusion of two ontologies without human intervention. Through repeated application

of OM, we can get a growing ontology of a knowledge topic given. Using OM we hope to achieve

automatic knowledge acquisition. There are two missing tasks: the conversion of a given text to its

corresponding ontology (by a combination of syntactic and semantic analysis) is not yet automatically done;

and the exploitation of the large resulting ontology is still under development.

1 INTRODUCTION

These days computers are not anymore isolated

devices but they are important entry points in the

world-wide network that interchanges knowledge

and carries out business transactions. Nowadays,

using Internet to get data, information and

knowledge interchange is a business and an

academic need. Despite the facilities to access

Internet, people face the problem of heterogeneous

sources, because there are no suitable standards in

knowledge representation. This paper is designed for

this necesity of businesses and academia.

Many answers that people require involve

accessing several sources in the Internet, which are

later manually merged in a “reasonable” way.

Merging the information is an important task. Many

languages and tools (DAML+OIL (URL 15), RDF

(URL 16) and OWL (URL 14) have been developed

to describe and process Internet content but,

unfortunately, they don’t have enough

expressiveness to detail knowledge representation.

Given a document written in a natural language,

it is required that the computer deciphers the

information in it and converts it to a suitable

notation (its knowledge base) that preserves relevant

knowledge. This knowledge base can be an

ontology. To describe a knowledge domain, an

ontology represents the knowledge through nodes

that are joined through relations. Current works that

merge ontologies (Prompt (Noy, et al, 2000),

Chimaera (McGuinness, et al, 2000), OntoMerge

(Dou et al, 2002), FCA-Merge (Stumme et al,

2002) and If-Map (URL 1)) rely on the user to solve

the most important problems found in the process:

inconsistencies and adequate knowledge extraction.

In our fusion also these inconsistencies appear buy

they are solved by OM. OM, the merging algorithm

that we will explain, is totally automatic. This

algorithm solves by itself the inconsistencies found

in the process. In some cases OM applies the

confusion algorithm (Levachkine, S., and Guzman,

A. 2004) at the moment other solutions are studying.

Two important contributions herein presented to

obtain better advantage of the Web resources are:

 A new notation to represent knowledge using

ontologies, called OM (Ontology Merging)

Notation, and

 An automatic algorithm to merge ontologies,

called OM Algorithm.

This paper it is a summary of (Cuevas, 2006), on

it we describes the second contribution. The first

356

Guzman-Arenas A. and Cuevas-Rasgado A. (2008).

KNOWLEDGE ACQUISITION WITH OM - A Heuristic Solution.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - AIDSS, pages 356-363

DOI: 10.5220/0001717703560363

 SciTePress

contribution, the OM notation it is in (Cuevas,

2006).

OM fuses two ontologies (this is our main

contribution), we are not doing any of: • ontology

comparison, this has been done by COM (see below)

and others; • ontology alignment, as Prompt (Noy &

Musen 2000); • building a gigantic unique ontology,

this may or may not be done (see Discussion); • an

ontology server, like Protegé (Noy & Musen 2000).

We neither pretend that our notation to be superior

to others (RDF (URL 16), say).

Some real examples appear where the texts

(unloaded by Internet) have became manually to

ontologies, OM have been applied and the result of

the fusion has been verified manually. We have a

work in the future; to make parser that turns of text

to ontologies and a deductor of intelligent questions

to verify the result of the fusion.

2 KNOWLEDGE ACQUISITION

The plan to follow is to acquire many “individual”

ontologies distilled from text documents, and then to

fuse them, two at the time, into a larger one. The

conversion of text into ontologies is hard, it is made

by a parser or syntactic analyzer, and will not be

covered in this work. This paper is focused to the

fusion of ontologies (arising from different sources)

amount computers. During this fusion the same

problems (redundancy, repetition, inconsistency…)

arise; the difference is that the machines have no

common sense (Lenat &Guha, 1989) and the

challenge is to make them to understand that

beneficial is the same to generous, and that triangle

represents: • a three-sided polygon; • a musical

percussion instrument; or • a social situation

involving three parts. The computer solution to

fusion should be very close to people’s solution.

This paper explains a process of union of

ontologies in automatic and robust form. Automatic

because (unaided) computer detects and solves the

problems appearing during the union, and robust

because it performs the union in spite of different

organization (taxonomies) and when the sources are

jointly inconsistent.

The fusion is demonstrated by samples taking of

real Web documents and converting them by hand to

ontologies. These are then fed to the computer,

which produces (without human intervention) a third

ontology as result, like in (Kotis, 2006). This result

is hand-compared with the result obtained by a

person. Mistakes are below (section 3.3, Table 1).

2.1 Ontology

Formally, an ontology is a hypergraph (C, R) where

C is a set of concepts, some of which are relations;

and R is a set of restrictions of the form (r c

…

) between relation r and concepts c

to c

. It is said

that the arity of r is k. Check that relations are also

concepts.

An important task when dealing with several

ontologies is to identify most similar concepts. We

wrote COM (Olivares, 2002) that finds this

similarity throught ontologies.

2.2 The Role of Ontologies

An ontology is a data structure where information is

stored as nodes (representing concepts such as

hammer, printer, document, appearing in this

paper in Courier font) and relations

(representing restrictions among nodes, such as cuts,

transcribes or hair color, they appear in this paper in

Arial Narrow font, how in (hammer cuts wood),

(printer transcribes document). Usually, the

information is stored as “high level” and it is known

as knowledge.

Ontologies are useful when arbitrary relations

need to be represented, because it offers more

freedom to represent different types of concepts and

relations.

Currently notations to represent ontologies are

DAML+OIL (Connoly et al, 2001), RDF (URL 16)

and OWL (URL 14). These languages are a notable

accomplishment, but it does not have enough

features:

• A relation can not be a concept. For instance, if

color is a relation, it is difficult to relate color to

other concepts (such as shape) by using other

relations.

• Partitions (subsets with additional properties)

can not be represented (Gómez P. A., and

Suárez F. 2004).

2.3 Exploitation of Distributed Content

Works exist (McGuinness et al, 2000; Noy &

Musen, 2000 and Dou et al, 2002) that perform the

union of ontologies in a semiautomatic way

(requiring user’s assistance). Others (Kalfoglou &

Schorlemmer, 2002 and Stumme & Maedche, 2002)

require ontologies organized in a formal way, and to

be consistent with each other. In real life, ontologies

come from different sources are not likely to be

similarly organized, nor they are expected to be

KNOWLEDGE ACQUISITION WITH OM - A Heuristic Solution

357

mutually consistent. The automation of fusion needs

to solve these problems.

3 INTEGRATION DESIGN

This section explains the procedure that follows OM

as well as the cases in which it has been applied.

3.1 The OM Algorithm

This algorithm fuses two ontologies (Cuevas, 2006)

A and B into a third ontology C = A ∪ B containing

the information in A, plus the information in B not

contained in A, without repetitions (redundancies)

nor contradictions. OM proceeds as follows:

1. Ontology A is copied into C. Thus, initially, C

contains A.

2. Using the algorithm COM (Olivares, 2002) seek

in B each concept c

of C called the most similar

concept of C into B. The search starts from the root

concept of C, taking each one of its son of this until

to visit all the concepts of C. There are just two

options:

A. If c

has a most similar concept cms ∈ B,

then:

i. Relations that are synonyms (section 3.2,

example 2) are enriched.

ii. New relations (inluding Partitions) that

cms has in B, are added to c

. Concepts that

which are in the new relations which come

from cms are copied to C (if they are not).

iii. Inconsistencies (section 3.2) between the

relations of c

and those of cms are detected.

1. If it is possible, by using confusion

algorithm (Levachkine & Guzman 2007),

to resolve the inconsistency into c

2. When the inconsistency can not be

solved, OM rejects the contradicting

information in B, and c

keeps its original

relation from A.

B. If c

does not have a cms ∈ B, go to step 3.

3. It takes the next descendant of c

into C. Goes

back to step 2 until all the nodes of C are visited

(including the new nodes that are being added by

OM). (Cuevas, 2006) explains how this works.

3.2 Problems that OM Solves

In this section, figures show only relevant parts of

ontologies A, B and the resultant C, because they are

too large to fit.

Example 1: Merging Ontologies with Inconsistent

Knowledge. Differences between A and B could be

the following: different subjects, different names of

concepts or relations; repetitions; reference to the

same facts but with different words; different level

of details (precision, depth of description); different

perspectives (people are partitioned in A into male

and female, but in B they are young or old); and

contradictions. For example A (URL 12) contains:

The Novelist, poet and writer Don Miguel de

Cervantes was born in Alcalá de Henares, Madrid

while B contains: The writer, (URL 13) poet and

romantic Cervantes was born in Madrid, Spain. Both

ontologies duplicate some information (about

Cervante’s place of birth), different expressions

(novelist, poet and writer versus writer, poet and

romantic), different level of details (Don Miguel de

Cervantes versus Cervantes), and contradictions

(Alcalá de Henares, Madrid vs. Madrid, Spain). A

person will have in her mind a consistent

combination of information: Cervantes and Don

Miguel de Cervantes are not the same person, or

perhaps they are the same, they are synonyms. If she

knows them, she may deduce that Don Miguel de

Cervantes is the complete name of Cervantes. We

solve these problems everyday, using previously

acquired knowledge and common sense knowledge

(Lenat & Guha, 1989), which computers lack. Also,

they did not have a gradual and automatical way to

grow their ontology. OM measures the inconsistency

(of two apparently contradicting facts) by asking

conf (Levachkine & Guzman 2007) to determine the

size of the confusion in using Alcalá de Henares in

instead of Madrid and vice versa, or the confusion of

using Don Miguel de Cervantes instead of

Cervantes.

OM does not accept two different concepts for a

birthplace. If A said that Don Miguel de Cervantes

was born in Alcalá de Henares and B says that

Cervantes was born in Madrid, OM chooses Alcalá

de Henares instead of Madrid because it is more

specific place while Madrid that is more general (it

deduces this from a hierarchy of places in Europe).

Small inconsistencies cause C to retain the most

specific value, while if it is large, OM keeps C

unchanged (ignoring the contradicting fact from B).

In case of inconsistency, A prevails. This is also

because we can consider that an agent’s previous

knowledge is A, and that such agent is trying to

learn ontology B. In case of inconsistency, it is

natural for the agent to trust more its previous

knowledge, and to disregard inconsistent knowledge

in B as “not trustworthy” and therefore not acquired

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– the agent refuses to learn knowledge that it finds

inconsistent, if the inconsistency is too large.

Example 2: Joining Partitions, Synonym

Identification, Organization of Subset to

Partition, Identification of Similar Concepts,

Elimination of Redundant Relations and

Addition of New Concepts. This example is

accomplished through eight cases:

a. Relation that are not Copied, if there are

Contradictions. Figure 1 shows relation

neutron without charge, that mean:

neutron does not have charge. Another

ontology containing: neutron with

positive charge that contradict what it is

before (see Figure 1), thus the contradicting

relation will not be copied into the fused result.

Figure 1: The concept neutron has a relation without

charge that OM recognizes as absence of that property in

neutron. Another ontology having relation has

charge, has negative charge linked to neutron,

will be an inconsistent information. When fusing both

ontologies, OM will not copy neutron has negative

charge into the result.

b. Copying New Partitions. building is a partition in

A (indicated in the small circle ) of Monte

Albán, therefore it is added to the resulting

ontology C (Figure 2).

c. Copying new concepts. Concepts Mixtec and

Mexican Republic were not in A, but they

appear in B. Therefore, they were copied by

OM to C (Figure 2).

d. Reorganization of Relations. Relation located in

appears twice but with different values,

therefore they are added to C because it is

possible that the relation to have several values

(Figure 2). In case of single-valued relations,

confusion algorithm (Levachkine & Guzman,

2007) is used.

e. Synonym Identification. Relation built by in A

(Figure 2) and made by in B are both

synonymous because in the definition of make

by in B (the words that defines it, between

parenthesis) we found the word build. OM fuses

in C the relation built by of A, with both

descriptive phrases build and make by.

f. Identification of Similar Concepts. In the

Figure 3, concept sculpture of a

jaguar in A and throne in the shape

of jaguar in B have the same properties

(Color and its value) therefore, OM fuses them

into a single concept. The same happens with

El Castillo and Pyramid of

Kukulkan because they have the same

properties and children.

g. Removing Redundant Relations. In A,

Chichen Itza is member of pre-

Columbian archaeological site

(Figure 3), which is a member of

archaeological sites. In B, Chichen

Itza is member of archaeological site

(which is parent of pre-Colombian

archaeological site in B), therefore it

is eliminated in C because it is a redundant

relation. In C, pre-Columbian

archaeological site i

parent of

Chichen Itza. The same occurs with

Isotope subset of chemical element in

figure 5, where A shows that Isotope is a

subset of chemical element and B shows

that Isotope is a subset of atom. OM check

that Isotope is a subset of atom that is at the

same time a subset of chemical element

and Isotope is a subset of chemical

element (OM erases this last relation because

it is redundant). But not in the Figure 4 where

the relation isotope subset of chemical

element is different to isotope member of

atom and atom subset of chemical

element.

h. Organization of Subset to Partition. In the

building partition in A there are six subsets

(Figure 3): Ballcourt, Palace, Stage,

Market and Bath. OM identifies them in B,

where they appear as subsets of Chichen

Itza. OM copies then into C like a partition

and not as simple subsets. OM prefers the

partition to just subset.

KNOWLEDGE ACQUISITION WITH OM - A Heuristic Solution

359

Figure 2: Ontology A describes Monte Alban. From a different view point, ontology B does the same. Ontology C is the

result of OM fusing them. The relation built by in ontology A and make by in B are identified (case e) as synonyms, hence it

is enriched during the fusion (into C). Only relevant parts of A, B and C are shown.

Figure 3: Ontology A and B describe Chichen Itza, where concepts Chac Mool in ontology A and Chac in B are

identified (case e) as synonyms. A more interesting case is case e, that identifies sculpture of a jaguar in A as a

similar concept (a synonym) to throne in the shape of jaguar in B. Also The Castle in A and Pyramid

of Kukulkan in B are found to be the same. Case f removes redundant relations (marked with an X in the result C). Case

g (see text) upgrades a set of subsets into a partition.

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Figure 4: In ontology A concept isotope is a subset of

Chemical element, but in B isotope is a member of

atom; into C, the resultant ontology OM provides

isotope with both ancestors Chemical element

and atom.

Figure 5: In B, isotope is subset of atom, whereas in A

isotope is subset of chemical element. This last

relation is redundant in C, and OM does not add it to C.

OM fuses carefully, eliminating redundant relations.

3.3 More Applications of OM in Real

Cases taken from the Web

OM has merged ontologies derived from real

documents. The ontologies were obtained manually

from several documents: 100 Years of Loneliness

(URL 9 and 11], Oaxaca (URL 5 and 10), poppy

(URL 2 and 4) and turtles (URL 6 and 7), describing

the same thing. These ontologies were merged

(automatically) by OM, and the product was

manually validated, obtaining good results (Table 1).

Table 1: Performance of OM in some real examples: The

columns “error” give the ratio of (number of wrong

relations) / (total number of relations) and (number of

wrong concepts) / (total number of concepts), respectively.

More real examples in (Cuevas, 2006).

Ontologies Error in the

merging of

relations

Error in the

merging of

concepts

Turtles 0 0

Hammer 0 0

Poppy 0 0

100 Years of

Loneliness

2.7% 5.3%

Oaxaca 0 0.3%

4 DISCUSSION

Is it possible to keep fusing of several ontologies

about the same topic, in order to have a larger

ontology that faithfully represents and join the

knowledge in each of the formant ontologies? OM

say “yes, it is possible.” What are the main

roadblocks? As we perceive them, they are:

a. Exploitation of hypergraphs. Although we

define ontologies as hypergraphs (section 2.1),

the restrictions (r c

… c

), where r is a

relation, are lists, and consequently, order

matters. For instance, it is not the same (kills;

Cain; Abel; jaw of donkey) that (kills;

Abel; Cain; jaw of donkey). However,

the role of each “argument” or element of the

restriction (such as jaw of donkey) must be

explained –in the example it is the instrument

used in the killing. Restrictions have different

number of arguments, each one with different

roles: consider (born; Abraham Lincoln;

Kentucky; 1809; log cabin). We can

expect a lot of arguments in a fragment of the

text. The role of each argument must be

explained or described in a transparent (not

confuse) form –ideally, we suggest OM notation

explain in (Cuevas, 2006)-, where OM can

understand such explanations, manipulate them

and create new ones. For instance, from a given

argument, it should be able to take two different

explanations (coming from ontologies A and B,

respectively) and fuse them into a third

explanation about such argument, to join into C.

Ways to do all of this should be devised.

b. A good parser. Documents are now transformed

by hand into ontologies, although fusion is

totally automatic, but the work of verify the

fusion is hard because it is also by hand. It has

been found difficult to build a parser that

reliably transforms a natural language document

KNOWLEDGE ACQUISITION WITH OM - A Heuristic Solution

361

into its suitable ontology, due to the ambiguity

of natural language and to the difficulty of

representing relations (verbs, actions, processes)

in a transparent way (see next point). Probably a

good parser will profit from the current

knowledge that OM has stored in the ontology

that was built before, as well as in additional

knowledge sources (point c below).

c. Additional language-dependent knowledge

sources could effort enhance OM. For instance,

WordNet, WordMenu, automatic discovery of

ontologies by analyzing titles of conferences,

university departments (Makagonov, 2007).

d. A query-answerer that queries a large ontology

and makes deductions. (Botello, 2007) works on

this for databases, not for ontologies. He has

obtained no results for real data, yet.

In addition, some caveats are:

e. OM does not have a way to know what is true

and what is false. All it does is to compute

ontology C as the fusion of A and B, in a

consistent form. If A and B say the same lies,

these will go into C.

f. Probably the first ontologies should be carefully

done by hand (even if parser existed), like, first

documents (their ontologies, that is) to be fed to

OM (“the first things OM will learn”) have to

be consistent, clear, and at a “low level.”

g. The formal support behind OM and OM

notation should be clearly adhered to.

5 CONCLUSIONS AND FUTURE

WORK

This paper presents an automatic procedure (the OM

algorithm) to fuse two ontologies about the same

topic, which produces good results.. Thus, it is an

important improvement to the computer-aided

merging editors currently available (section 2.3).

OM is an automatic, robust algorithm that fuses two

ontologies into a third one, which preserves the

knowledge obtained from the sources, solving some

inconsistencies, detecting synonyms and homonyms,

and expunging some redundant relations.

The examples shown, as well as others in

(Cuevas, 2006; Cuevas & Guzman, 2007), provide

evidence that OM does a good job, in spite of very

general or very specific of joining ontologies. This is

because the algorithm takes into account not only

the words in the definition of each concept, but its

semantics [context, synonyms, resemblance (through

conf) to other concepts…] too. In addition, its base

knowledge (some pre-built knowledge, such as

synonyms, external language sources, stop words,

words that change the meaning of a relation, among

others) helps.

OM has not been tried on extensive, “real”

ontologies (for instance, an ontology describing the

complete work “100 Years of Loneliness”), due to

the tedious work to hand-craft such ontology from

the written document. Section 5.b addresses this.

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