MELIS

An Incremental Method for the Lexical Annotation of Domain Ontologies

Sonia Bergamaschi, Laura Po, Maurizio Vincini

DII - Universit`a di Modena e Reggio Emilia, via Vignolese 905, Modena, Italy

Paolo Bouquet, Daniel Giacomuzzi

DIT - Universit`a di Trento, Via Sommarive, 14, Trento, Italy

Francesco Guerra

DEA - Universit`a di Modena e Reggio Emilia, v.le Berengario 51, Modena, Italy

Keywords:

Semantic integration, automatic lexical annotation, Semantic Web, Ontology matching.

Abstract: In this paper, we present MELIS (Meaning Elicitation and Lexical Integration System), a method and a

software tool for enabling an incremental process of automatic annotation of local schemas (e.g. relational

database schemas, directory trees) with lexical information. The distinguishing and original feature of MELIS

is its incrementality: the higher the number of schemas which are processed, the more background/domain

knowledge is cumulated in the system (a portion of domain ontology is learned at every step), the better the

performance of the systems on annotating new schemas.

MELIS has been tested as component of MOMIS-Ontology Builder, a framework able to create a domain on-

tology representing a set of selected data sources, described with a standard W3C language wherein concepts

and attributes are annotated according to the lexical reference database.

We describe the MELIS component within the MOMIS-Ontology Builder framework and provide some ex-

perimental results of MELIS as a standalone tool and as a component integrated in MOMIS.

1 INTRODUCTION

The growth of information available on the Internet

has required the development of new methods and

tools to automatically recognize, process and man-

age information available in web sites or web-based

applications. One of the most promising ideas of

the Semantic Web is that the use of standard formats

and shared vocabularies and ontologies will provide a

well-deﬁned basis for automated data integration and

reuse. However, practical experience in developing

semantic-enabled web applications and information

systems shows that this idea is not so easy to imple-

ment. In particular, we stress two critical issues: on

the one hand, building an ontology for a domain is a

very time consuming task, which requires skills and

competencies which are not always available in orga-

nizations; and, on the other hand, there seems to be an

irreducible level of semantic heterogeneity, which has

to do with the fact that different people/organizations

tend to use “local” schemas for structuring their data,

and ontologies – if available at all – are often designed

to ﬁt locally available data rather than aiming at be-

ing general speciﬁcations of domain knowledge. The

consequence is a situation where data are organized

to comply to some local schema (e.g. a relational

schema, or a directory tree) and no explicit (formal)

ontology is available; or – if an ontology is available

– it is tailored on local data/schemas and therefore of

little use for data integration.

The two issues above led the Semantic Web

and Database communities to address two very

hard problems: ontology learning (inducing on-

tologies from data/schemas) and ontology match-

ing/integration (bridging different ontologies). For

our argument, we only need to observe that several

methods and tools developed to address the two prob-

lems rely – in different ways – on the use of lexi-

cal information. The reason is simple: beyond the

syntactic and semantic heterogeneity of schemas and

ontologies, it is a fact that their elements and prop-

erties are named using natural language expressions,

and that this is done precisely because they bring in

useful (but often implicit) information on the intended

240

Bergamaschi S., Po L., Vincini M., Bouquet P., Giacomuzzi D. and Guerra F. (2007).

MELIS - An Incremental Method for the Lexical Annotation of Domain Ontologies.

In Proceedings of the Third International Conference on Web Information Systems and Technologies - Web Interfaces and Applications, pages 240-247

DOI: 10.5220/0001280902400247

 SciTePress

meaning and use of the schema/ontology under con-

struction. Therefore, it should not come as a surprise

that a large number of tools for ontology learning and

schema/ontology matching include some lexical re-

sources (mainly WordNet

) as a component, and use

it in some intermediate step to annotate schema el-

ements and ontology classes/properties with lexical

knowledge. To sum up, lexical annotation seems to

be a critical task to develop smart methods for ontol-

ogy learning and matching.

In this context, we developed MELIS (Meaning

Elicitation and Lexical Integration System), a method

and a software tool for the annotation of data sources.

The distinguishing feature and the novelty of MELIS

is its incremental annotation method: the more

sources (including a number of different schemas) are

processed, the more background/domain knowledge

is cumulated in the system, the better the performance

of the systems on new sources. MELIS supports three

important tasks: (1) the source annotation process,

i.e. the operation of associating an element of a lexi-

cal reference database (WordNet in our implementa-

tion, but the method is independent from this choice)

to all source elements, (2) the customization of the

lexical reference with the introduction of new lexi-

cal knowledge (glossa, lemma and lexical relation-

ships), and (3) the extraction of lexical/semantic re-

lationships across elements of different data sources.

Works related to the issues discussed in this paper

are in the area of languages and tools for annotations

((Bechhofer et al., 2002), (Staab et al., 2001) and

(Handschuh et al., 2003) where an approach similar

to our is adopted), techniques for extending WordNet

((Gangemi et al., 2003), (Montoyo et al., 2001) and

(Pazienza and Stellato, 2006) where a system coupled

with Prot`eg`e

for enriching and annotating sources

is proposed), and systems for ontology management

(see the the Ontoweb

and the Knowledgeweb Net-

work of Excellence

technical reports for complete

surveys).

2 MELIS: THE LEXICAL

KNOWLEDGE COMPONENT

In most real world applications, ontology elements

are labeled by natural language expressions. In our

opinion, the crucial reason for this aspect of ontol-

See

http://wordnet.princeton.edu

for more in-

formation on WordNet.

http://protege.stanford.edu/

http://www.ontoweb.org, in particular deliverable 1.4

http://knowledgeweb.semanticweb.org/

ogy engineering is the following: while conceptual

annotations provide a speciﬁcation of how some ter-

minology is used to describe some domain (the stan-

dard role of OWL ontologies), natural language la-

bels (lexical annotations) provide a natural and rich

connection between formal objects (e.g. OWL classes

and properties) and their intended meaning. The in-

tuition is that grasping the intended interpretation of

an ontology requires not only an understanding of the

formal properties of the conceptual schema, but also

knowledge about the meaning of labels used for the

ontology elements. In other words, an OWL ontology

can be viewed as a collection of formal constraints be-

tween terms, whose intended meaning also depends

on lexical knowledge.

In most cases, lexical knowledge is used for an-

notating schema/ontology labels with lexical infor-

mation, typically WordNet senses. However, lexical

annotation is a difﬁcult task, and making it accurate

may require a heavy user involvement. Typical prob-

lems are: coverage (a complete lexical database in-

cluding all possible terms does not exist); polysemy

(in natural language, many terms have multiple mean-

ings); compound terms (schemas and ontologies are

often labeled with compound nominal expressions,

like “full professor”, “table leg”, “football team”, and

the choice of the right lexical meaning often depends

on determining the relationship between terms); in-

tegration (a standard model/language for describing

lexical databases does not exist).

That is why several tools which were developed

for annotating sources only provide a GUI for sup-

porting the user in the manual execution of the task.

However, this manual work can be highly time con-

suming, and very tedious for humans.

MELIS tries to make annotation as automatic

as possible by providing a candidate lexical anno-

tation of the sources as the combination of lexical

knowledge (from WordNet) and domain knowledge

(if available). In addition, MELIS uses the WNEdi-

tor (Benassi et al., 2004) to support customized ex-

tensions of WordNet with missing words and senses.

In the following we describe the MELIS method,

its heuristic rules and the main features of WNEditor.

2.1 The MELIS Method

The way MELIS works is depicted in Figure 1. We

start from a collection of data sources which cover re-

lated domains, e.g. hotels and restaurants. In general

we do not assume that a domain ontology is initially

available, though this may be the case. The process is

a cycle which goes as follows:

1. a schema, which can be already partially anno-

MELIS - An Incremental Method for the Lexical Annotation of Domain Ontologies

241

Figure 1: Functional representation of MELIS.

tated with lexical information, is given as input to

MELIS, together with a (possibly empty) domain

ontology (called reference ontology in the ﬁgure).

Lexical information is extracted from WordNet

which may be extended with words/senses which

are not available by interacting with WNEditor;

2. the automatic lexical annotation process starts; its

output is a partial annotation of schema elements,

together with a list of discovered relationships

across different elements. This annotation, whose

main rules are described below, is obtained by us-

ing two main knowledge sources: WordNet (for

lexical senses and relationships across them), and

the reference ontology, if not empty (it provides

non-lexical – domain dependent – relationships

across senses, e.g. between “hotel” and “price”).

Pre-existing lexical annotations are not modiﬁed,

as they may come either from manual annotation

or from a previous annotation round;

3. the resulting annotated schema is passed to a user,

who may validate/complete the annotation pro-

duced by MELIS;

4. the relationships discovered across terms of the

schema are added to the reference ontology

(which means that an extended – and lexically an-

notated – version of the domain ontology is pro-

duced, even if initially it was empty);

5. the process restarts with the following schema, if

any; otherwise it stops.

The process is incremental, as at any round the

lexical database and the reference ontology may be

extended and reﬁned. As we said, the process might

even start with an empty reference ontology, and

the ontology is then constructed incrementally from

scratch.

2.2 The Rules for Generating New

Annotations

A crucial part of the process has to do with the rules

which are used to produce the MELIS lexical an-

notations. The core rules are derived from CTX-

MATCH2.0, and are described in previous publica-

tions. However, to improve the precision and recall of

MELIS, we added a few specialized heuristic rules.

In what follows, we use the following notational

conventions:

• Letters: capital letters (A, B, C, ...) stand for

class labels, low case letters (a, b, c, ...) stand

for datatype property labels, letters followed by

“#n” (where n is a natural number)refer to the n-th

sense of the label for which the letter stands (e.g.

b#2 is the second sense of the word occurring in

the label “b”).

• Arrows: red arrows denote a subclass relation,

black arrows denote datatype properties, blue ar-

rows denote object properties.

• Ontologies: O is used for the ontology to be anno-

tated, DO

for the i-th domain ontology available

for the current elicitation process.

The annotation process takes as input a schema

O and works in two main steps: ﬁrst, for every label

in O, the method extracts from WordNet all possible

senses for the words composing the label; then, it ﬁl-

ters out unlikely senses through some heuristic rules.

The remaining senses are added as lexical annotation.

Ideally, the system produces just one annotation, but

in general it may be impossible to select a single an-

notation. Below is a general description of the heuris-

tic rules used by MELIS.

Rule 1 Couple class–property: if in O we ﬁnd a class

labeled A with a datatype property b, and in some

we ﬁnd a class annotated as A#i and a datatype

property annotated as b#j, then we conclude that the

annotations A#i and b#j are acceptable candidate an-

notations for A and b in O.

Rule 2 Inheritance parent–child: if in O we ﬁnd a

class labeled

with a datatype property

, and in

some DO

we ﬁnd a class annotated as

B#j

, with a

datatype property annotated as

b#k

and a subclass

In the paper we consider a subclass property both an

object oriented deﬁnition and a WordNet hyponym relation.

In WordNet we say that a noun X is a hyponym of a noun

Y if X is less general than Y (X is a specialization of Y);

conversely, we say that X is a hypernym of Y if X is more

general than Y. Other relationships across nouns are: X is

a holonym of Y (Y is a part of X), and X is a meronym of

Y (X is a part of Y). Different relationships are used for

WEBIST 2007 - International Conference on Web Information Systems and Technologies

242

A#i

, then we conclude that the annotations

A#i

and

b#k

are acceptable candidate annotations for

and

in O.

Rule 3 Inheritance child–parent: if in O we ﬁnd a

class labeled A with a datatype property b, and in

some DO

we ﬁnd a class annotated as A#i , with a

subclass B#j, and the latter has associated a datatype

property annotated as b#k and i, then we conclude

that the annotations A#i and b#k are acceptable can-

didate annotations for A and b in O.

Rule 4 Inheritance in sibling classes: if in O we ﬁnd

a class labeled A with a datatype property b, and in

some DO

we ﬁnd a class annotated as C#k with two

subclasses annotated as A#i and B#j, and there is a

datatype property annotated b#h associated to B#j,

then we conclude that the annotations A#i and b#k

are acceptable candidate annotations for A and b in

Rule 5 Propagation through object properties: if in

O we ﬁnd a pair of classes labeled A and B, connected

through any object property, and in some DO

we ﬁnd

a pair of classes annotated as A#i and C#k, and C#k

has a subclass B#j, then we conclude that the anno-

tations A#i and B#j are acceptable candidate annota-

tions for A and B in O.

Rule 6 Inheritance of parent–child relationship: if

in O we ﬁnd a pair of classes labeled

and

(with

subclass of

), and in some DO

we ﬁnd a subclass

hierarchy in which two classes are annotated as

A#i

...,

B#j

(with none, one or more intermediate classes

in between), then we conclude that the annotations

A#i

and

B#j

are acceptable candidate annotations

for

and

in O.

When all heuristic rules are applied, then we dis-

charge any candidate pair of annotations which is not

supported by any of the rules above.

2.3 The WNEditor

WNEditor aids the designer to extend WordNet to

coverdomain speciﬁc words which are not in the orig-

inal WordNet database.

Since WordNet is distributed as-it-is and external

applications are not allowedto directly modify its data

ﬁles, WNEditor addresses two important issues: (i)

providing a physical structure where WordNet and all

its possible extensions are stored and efﬁciently re-

trieved; (ii) developing a general technique which can

support users in consistently extending WordNet. The

other grammatical types, e.g. for verbs and adjectives. See

http://wordnet.princeton.edu

for more details.

ﬁrst issue is technically solved by storing the orig-

inal WordNet (and all its possible extensions) in a

relational database. The second issue is addressed

by giving ontology designers the possibility to in-

sert new synsets (a synset is a new “concept”, which

can be expressed by several words); insert new lem-

mas (based on an approximate string matching algo-

rithm to perform the similarity search on the whole

synset network); inserting new relationships between

synsets (given a source synset, the designer is assisted

in searching for the most appropriate target synset,

see (Benassi et al., 2004) for details).

WordNet extensions may then be exported and

reused in other applications (though they are always

presented separately from the original WordNet, and

the source of the new annotations is always made ex-

plicit).

3 MELIS AS A MODULE OF

MOMIS

We tested the MELIS approach by coupling it with

MOMIS

. MOMIS is a framework that starts from a

collection of data sources and provides a collection of

tools for:

1. semi-automatically building a customized ontol-

ogy which represents the information sources;

2. annotating each source according to the resulting

ontology;

3. mapping the created ontology and the original

sources into a lexical database (WordNet) to sup-

port interoperability with other applications.

MELIS has been experimented in MOMIS to

show that it can improve the MOMIS methodology

in two main directions: by supporting the semi-

automatic annotation of the original data sources (cur-

rently the process is manually executed), and by

providing methods for extracting rich relationships

across terms by exploiting lexical and domain knowl-

edge.

MOMIS provides a double level of annotation for

data sources and the resulting ontology: for each

source, conceptual annotations map the original struc-

ture into a formalized ontology and lexical annota-

tions assign a reference to a WordNet element for

each source term. Moreover, the ontology structure

is formalized by means of a standard model and each

concept is annotated according to a lexical reference.

MELIS inside MOMIS allows a greater automation

See http://www.dbgroup.unimore.it for references

about the MOMIS project.

MELIS - An Incremental Method for the Lexical Annotation of Domain Ontologies

243

Figure 2: Functional representation of MOMIS and MELIS.

in the process of source annotation, and provides a

way for discovering relationships among sources ele-

ments.

Figure 2 shows how the MELIS component is in-

tegrated into the MOMIS architecture. The process

of creating the ontology and deﬁning the mappings

is organized in ﬁve step (each task number is corre-

spondingly represented in ﬁgure 2) : (1) local source

schema extraction, (2) lexical knowledge extraction

performed with MELIS, (3) common thesaurus gen-

eration, (4) GVV generation, and (5) GVV and local

sources annotation. The following sections describe

the details of these steps.

Local source schema extraction. To enable

MOMIS to manage web pages and data sources,

we need specialized software (wrappers) for the

construction of a semantically rich representations of

the information sources by means of a common data

model.

Lexical knowledge extraction. The extraction of

lexical knowledge from data sources is typically

based on an annotation process aiming at associating

to each source element an effective WordNet mean-

ing.

MELIS supports the user in this task by provid-

ing an effective tool for decreasing the boring manual

annotation activity.

Common thesaurus generation. The common the-

saurus is a set of relationships describing inter- and

intra-schema knowledge about the source schemas.

The common thesaurus is constructed through a

process that incrementally adds four types of relation-

ships: schema-derived relationships, lexicon derived

relationships, designer-supplied relationships and in-

ferred relationships.

• Schema-derived relationships. The system au-

tomatically extracts these relationships by analyz-

ing each schema separately and applying a heuris-

tic deﬁned for the speciﬁc kind of source man-

aged.

• Lexicon-derived relationships. These relation-

ships, generated by MELIS, represent complex

relationships between meanings of terms anno-

tated with lexical senses. These relationships

may be inferred not only from lexical knowl-

edge (e.g. by querying WordNet for relationships

across senses), but also from background knowl-

edge (e.g. domain ontologies) which are avail-

able at the time of the annotation. As we will say

later (section 2), at any step MELIS can (re)use

any piece of ontology generated by the current ex-

traction process as a source of domain knowledge

to incrementally reﬁne the extraction of new rela-

tionships.

• Designer-supplied relationships. To capture

speciﬁc domain knowledge, designers can supply

new relationships directly.

• Inferred relationships. MOMIS exploits de-

scription logic techniques from ODB-Tools (Ben-

eventano et al., 1997) to infer new relationships.

GVV generation. The Global Virtual View (GVV)

consists of a set of classes (called Global Classes),

plus mappings to connect the global attributes of each

global class and the local sources attributes. Such a

view conceptualizes the underlying domain; you can

think of it as an ontology describing the sources in-

volved.

WEBIST 2007 - International Conference on Web Information Systems and Technologies

244

GVV and local sources annotation. MOMIS au-

tomatically proposes a name and meanings for each

global class of a GVV (Beneventano et al., 2003)

Names and meanings have to be conﬁrmed by the on-

tology designer. Local sources are conceptually an-

notated according to the created GVV.

4 TESTING MOMIS+MELIS ON A

REAL DOMAIN

We tested MOMIS integrated with MELIS by build-

ing an ontology of a set of data-intensive websites

containing data related to the touristic domain (see

ﬁgure 3), which have been wrapped and data from

them have been structured and stored into four rela-

tional databases off-line available. The main classes

of these sources are: hotel (of the “venere” database),

restaurant (“touring” database), camping (guidaC

database) and bedandbreakfast (BB database).

As discussed before, the incremental annotation

process starts with the annotation of parts of the data

sources, i.e. for each source element the ontology

designer selects one or more corresponding WordNet

synsets. For example, WordNet tells us that “hotel”

and “restaurant” are siblings (i.e. they have a common

direct hypernym); that “hotel” , “house”, “restau-

rant” are direct hyponyms of “building”; that “bed

and breakfast” is an hyponym of “building”; and that

the closest hypernym that “campsite” and “building”

share is “physical object”, a top level synset in Word-

Net. As the last relationship does not allow ﬁnding

lexical connections between “camping” and the other

classes, we used the WNEditor to add a direct re-

lationships between “campsite” and the hierarchy of

“building”.

Notice that the annotation process is a critical

process: by annotating the source element “camping”

as the WordNet synset “camping” a mistake would be

generated because it means “the act of encamping”.

The correct synset for camping is “campsite”, i.e. “the

site where people can pitch a tent”. Moreover, in or-

der to test all the implemented heuristics,“hotel” has

been annotated as its hypernym: “building”.

The annotated schema is then given both as in-

put and as the reference ontology to CtxMatch2.0.

The tool starts the meaning elicitation process and

produces a set of inferred lexical annotations of the

schema elements. The resulting annotated schema is

shown to the designer, who may validate and extend

the annotation produced by CtxMatch2.0 and, even-

http://www.bbitalia.it, http://www.guidacampeggi.com,

http://www.venere.com, http://www.touringclub.com

tually, restart the process using the updated annotated

schema as reference ontology.

Figure 4 illustrates the results of a sample test of

incremental annotation on one of our schemas. It

shows the annotations manually provided by the on-

tology designer,a fraction of the new annotations gen-

erated after a ﬁrst run of MELIS, and the additional

annotations generated after a second run, when the

outcome of the ﬁrst run was provided as additional

background knowledge in input; the numbers on the

arrows refer to the heuristic rule which was used to

generate the annotation. Notationally, a square near

a class/attribute means that the element was manually

annotated, a circle means that the element was auto-

matically annotated after the ﬁrst run, and a rhombus

that it was incrementally annotated after the second

run.

• Rule 1: the attribute “identiﬁer” of the class “fa-

cility” in the source “VENERE” is annotated as

“identiﬁer” of the class “facility” in the source

“BB” since both the classes are annotated with the

same synset.

• Rule 2: because of the hyponym relationships

generated by the annotations of the classes “ho-

tel”, “campsite”, “bed and breakfast” and “build-

ing”, the attribute “city” of the class “building” in

the source “VENERE” produces the annotation of

the same attribute in the sources “BB”, “touring”,

“guidaC”.

• Rule 3: because of the hypernym relation rela-

tionships generated by the annotations of “build-

ing” and “bed and breakfast”, the attribute “iden-

tiﬁer” of the class “bed

and breakfast” in the

source “BB” generates the annotation of the same

attribute in the source “VENERE”. By execut-

ing a second run of the MELIS process, the at-

tribute “identiﬁer” on the class “building” gener-

ates the annotation of the same attribute on the

classes “campsite” and ”restaurant” of the sources

“guidaC” and “touring” (application of the heuris-

tic rule 2).

• Rule 4: because of the new relationship intro-

duced in WordNet, “campsite” is a sister term of

“restaurant”. Consequently, the attribute “local-

ity” is annotated in the same way in the sources

“guidaC” and “touring”.

• Rule 5: in the source “VENERE” the class “map”

has a foreign key: the attribute “url” that refer-

ences to the class “hotel”. Because of this rela-

tionship joins with hierarchical relationships “ho-

tel”, “campsite”, “bed and breakfast” and “build-

ing”, the annotation of attribute “url” of the class

“map”, applying Rule 3 and Rule 5, generates the

MELIS - An Incremental Method for the Lexical Annotation of Domain Ontologies

245

Figure 3: Sources used for evaluating MELIS.

same annotation for “url” in the classes “camp-

site”, “bed

and breakfast” and “restaurant” of the

other sources.

Notice that heuristic 6 and 7 are not exploited in

this example. Such rules may be exploited in nested

structures as hierarchies, and they may not be applied

in ﬂat structures as relational databases.

The results are highly dependent on the annotation

manually provided by the user as MELIS input . For

this reason, it is not meaningful to give any evalua-

tion in terms of number of new annotations discov-

ered. Concerning the evaluation of the new annota-

tions generated, our experience highlights that all of

the new annotated elements have a correct meaning

w.r.t. WordNet.

5 CONCLUSION AND FUTURE

WORK

In this paper we presented MELIS, a method and

tool for incrementally annotating data sources accord-

ing to a lexical database (WordNet in our approach).

MELIS exploits the annotation of a subset of source

elements to infer annotations for the remaining source

elements, this way improving the activity of manual

annotation.

MELIS is based on the integration and the exten-

sion of the lexical annotation module of the MOMIS-

Ontology Builder (Benassi et al., 2004) and some

components from CTXMATCH2.0, a tool for elicit-

ing meaning and matching pairs of nodes in hetero-

geneous schemas, using an explicit and formal rep-

resentation of their meaning (Bouquet et al., 2005;

Bouquet et al., 2006). CTXMATCH2.0 was extended

with respect to (Bouquet et al., 2005; Bouquet et al.,

2006) with a set of heuristic rules to generate new an-

notations on the basis of the knowledge provided by

a given set of annotations; WNEditor was modiﬁed in

order to jointly work with CTXMATCH2.0, by pro-

viding a customized lexical database.

We experimented MELIS in conjunction with the

MOMIS system in order to improve the MOMIS

methodology for semi–automatically creating a do-

main ontology from a set of data sources. The ﬁrst re-

sults show that MELIS and MOMIS working in con-

junction are an effective tool for creating a domain

ontology. The testing was performed within the WIS-

DOM project

, for creating an ontology from several

data-intensive websites about hotels and restaurants.

As we noticed in the introduction, MELIS can be

used to provide valuable input not only for ontology

learning, but also for ontology matching tools. Here

we want to notice that these tools can greatly ben-

eﬁt from the integration of MELIS, as MELIS pro-

vides a highly accurate collection of lexical annota-

tions which can be exploited in the matching phase.

Future work on MELIS will be addressed on im-

proving the annotation technique in order to deal with

compound terms (like “full professor”, “table leg”,

“football team”). Compound terms do not appear

in any lexical database, unless they form a stable

compound (e.g.“station wagon”). Their annotation is

therefore more difﬁcult, as the choice of the right lex-

ical meaning often depends on determining the rela-

tionship between terms.

Moreover, we will introduce in MELIS more ac-

curate stemming techniques in order to improve the

matching among input terms and the words of the lex-

ical reference database. Finally, we are developing a

methodology for building and sharing among a com-

munity new lexical database entries, e.g. by establish-

ing how and when a new noun/meaning can be “pro-

moted” to be part of the common lexical reference.

http://www.dbgroup.unimo.it/wisdom

WEBIST 2007 - International Conference on Web Information Systems and Technologies

246

Figure 4: Annotations generated with MELIS.

REFERENCES

Bechhofer, S., Carr, L., Goble, C. A., Kampa, S., and

Miles-Board, T. (2002). The semantics of seman-

tic annotation. In Meersman, R. and Tari, Z., ed-

itors, CoopIS/DOA/ODBASE, volume 2519 of Lec-

ture Notes in Computer Science, pages 1152–1167.

Springer.

Benassi, R., Bergamaschi, S., Fergnani, A., and Miselli, D.

(2004). Extending a lexicon ontology for intelligent

information integration. In de M´antaras, R. L. and

Saitta, L., editors, ECAI, pages 278–282. IOS Press.

Beneventano, D., Bergamaschi, S., Guerra, F., and Vincini,

M. (2003). Synthesizing an integrated ontology. IEEE

Internet Computing, pages 42–51.

Beneventano, D., Bergamaschi, S., Sartori, C., and

Vincini, M. (1997). ODB-QOPTIMIZER: A tool

for semantic query optimization in oodb. In

Int. Conference on Data Engineering - ICDE97.

http://www.dbgroup.unimo.it.

Bouquet, P., Seraﬁni, L., and Zanobini, S. (2005). Peer-to-

peer semantic coordination. Journal of Web Seman-

tics, 2(1).

Bouquet, P., Seraﬁni, L., Zanobini, S., and Sceffer, S.

(2006). Bootstrapping semantics on the web: meaning

elicitation from schemas. In WWW2006 Conference

Proceedings. W3C.

Gangemi, A., Navigli, R., and Velardi, P. (2003). The

ontowordnet project: Extension and axiomatization

of conceptual relations in wordnet. In Meers-

man, R., Tari, Z., and Schmidt, D. C., edi-

tors, CoopIS/DOA/ODBASE, volume 2888 of Lecture

Notes in Computer Science, pages 820–838. Springer.

Handschuh, S., Staab, S., and Volz, R. (2003). On deep

annotation. In WWW, pages 431–438.

Montoyo, A., Palomar, M., and Rigau, G. (2001). Word-

net enrichment with classiﬁcation systems. In In

Proc. of WordNet and Other Lexical Resources: Ap-

plications, Extensions and Customisations Workshop,

NAACL-01, pages 101–106, Carnegie Mellon Univ.,

Pittsburgh, USA.

Pazienza, M. T. and Stellato, A. (2006). An open and scal-

able framework for enriching ontologies with natural

language content. In Ali, M. and Dapoigny, R., edi-

tors, IEA/AIE, volume 4031 of Lecture Notes in Com-

puter Science, pages 990–999. Springer.

Staab, S., Maedche, A., and Handschuh, S. (2001). An

annotation framework for the semantic web. In S.

Ishizaki (ed.), Proc. of The First International Work-

shop on MultiMedia Annotation. January. 30 - 31.

Tokyo, Japan.

MELIS - An Incremental Method for the Lexical Annotation of Domain Ontologies

247