CONCEPTUAL FRAMEWORK FOR XML SCHEMA MAPPING

Myriam Lamolle, Amar Zerdazi

LINC, IUT of Montreuil-University of Paris 8, 140 rue de la Nouvelle France, 93100 Montreuil, France

Ludovic Menet

Orchestra Networks, 75 boulevard Haussman, 75008 Paris, France

Keywords: Mapping, node similarity, schema integration, XML Schema.

Abstract: Today’s web-based applications and web services publish their data using XML, as this helps

interoperability with other applications and services. The heterogeneity of XML data has led to recent

research in schema matching, schema transformation, and schema integration for XML. In this paper, we

propose an approach for mapping integration for XML schema. The basic idea is to drive direct as well as

complex matches with their associated transformation operations from the computed element similarities.

The representation of a mapping element in a source-to-target mapping clearly declares both the semantics

correspondences as well as the access paths to access and load data from source into a target schema. We

detail our mapping generation process and proceed in four steps to specify a formal representation of a

source-to-target mapping in order to discover structural mappings between XML schemas.

1 INTRODUCTION

Today’s web-based applications and web services

publish their data using XML, as this helps

interoperability with other applications and services.

The heterogeneity of XML data has led to recent

research in schema matching, schema

transformation, and schema integration for XML.

In this paper, we propose an approach for

mapping integration for XML schema. The basic

idea is to drive direct as well as complex matches

with their associated transformation operations from

the computed element similarities. The

representation of a mapping element in a source-to-

target mapping clearly declares both the semantic

correspondences as well as the access paths to

access and load data from source into a target

schema.

The paper is organized as follows. Some related

works are presented in section 2. In sections 3 and 4,

we respectively give a brief overview of our formal

model for XML schema (XML Schema graph) and

highlight a set of structure transformation

operations. Section 5 presents the core of this paper.

We detail our mapping generation process and

proceed in four steps to specify a formal

representation of a source-to-target mapping in order

to discover structural mappings between XML

schemas. Section 6 concludes this paper.

2 RELATED WORKS

Several approaches (Bouzeghoub et al., 2003),

(Miller et al., 2000), (Li et al., 2000) have been

proposed to generate mappings when the target and

the source schemas are expressed using the

relational model. The approach presented in (Kedad

and Xue, 2005) generates a set of mappings from a

set of source schemas using linguistic

correspondences between target attributes and

source attributes expressing the idea that these

elements represent the same concept.

In (Popa et al., 2002), an approach is proposed

for generating mappings from one source schema to

a target schema when these schemas are represented

by XML Schema. In (Yu and Popa, 2004), a query

rewriting algorithm which uses these mappings is

proposed for integrating data sources. Other

approaches have been proposed (Claypool et al.,

2003), (Yang et al., 2003), and (Zamboulis and

Poulovassilis, 2004) to generate mappings from

Lamolle M., Zerdazi A. and Menet L. (2008).

CONCEPTUAL FRAMEWORK FOR XML SCHEMA MAPPING.

In Proceedings of the Tenth International Conference on Enterprise Information Systems - DISI, pages 85-90

DOI: 10.5220/0001670700850090

 SciTePress

several source schemas. These approaches comprise

two steps: (i) the definition of rules to restructure

each source schema according to the structure of the

target schema, and (ii) the generation of mappings

from these restructured schemas. In these

approaches, source schemas must be restructurable

with respect to the target schema in order to use

them for mapping definition.

3 THE DATA MODEL

As already mentioned in section 2, up to now few

existent XML schema matching algorithms have

focused on structural matching exploiting all W3C

XML schemas (W3C, 2001) features. In this section,

we propose an abstract model that serves as a

foundation to conceptually represent W3C XML

schemas and potentially other schema languages.

We model XML schemas as a directed labeled graph

with constraint sets; a so-called schema graph. This

graph is used in the matching process for the

measure of node context similarity.

Figure 1: A schema graph example.

A schema graph consists of series of nodes that

are connected to each other through directed labeled

links. In addition, constraints can be defined over

nodes and links. We proposed model for XML

schemas in order to define a formal framework for

solving matching problems.

4 OPERATIONS ON MAPPING

A mapping element relates, in its simplest form, a

construction (which refers to nodes and edges in the

schema graph) from S to a semantically similar

construction from T (respectively source and target

schema graphs). Although, a source may not have a

construction that directly corresponds to the target

one, nevertheless target constructions may be

derived from source constructions by applying a set

of predefined operations. This mechanism is

somehow similar to virtual views creation in data

integration, where a virtual view over the source has

to match a mediated schema (e.g., the concatenation

of source elements firstname and lastname gives rise

to a virtual element name that matches the target

element name). Virtual views are also applicable to

edges (e.g., the two edges author

→

firstname, and

author

→

lastname are merged together into a virtual

edge author

→

name that matches the edge

author

→

name in the target schema). Based on this

observation we borrow the notion of virtual view to

formally define mappings between source and target

schemas. We have been inspired essentially by

research in the field of generating virtual views in

data integration systems (Biskup and Embley, 2003),

(Dobre et al., 2003), (Xu and Embley, 2003).

Definition 1 (Schema alphabet): Given a schema S

(in keeping with the schema graph formalism), we

define the alphabet of S,

∑

as the union of nodes

and edges in the S schema graph.

∑

∪

Definition 2 (Virtual view): Given a source schema

S and its alphabet

∑

, a virtual view over S, v

defined as a derivation from the alphabet

∑

Applying a set of predefined operations

O={o

,…o

Definition 3 (Mapping element, mapping direct,

mapping complex): Let V

denotes the set of

possible virtual views constructed over

∑

. A

mapping element is a function μ:

∑

∪∑

, that

associates an element s in

∑

∪

to an element t in

∑

. A mapping element is direct mapping if it binds

an element in

∑

(

⊆

) to an element in

∑

and a

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Figure 2: Mapping generation process.

complex mapping if it binds a virtual element v

∈

to a target element in

∑

through a mapping

expression defined over O.

Mappings can be combined by means of some

operators giving a result that in turn is a mapping. In

(Zerdazi and Lamolle, 2005), we extended the

relational algebra so that we were able to formally

define a set of operators that are defined over source

and target schema nodes and that can be used to

combine nodes to form complex mapping

expressions.

Our mapping algebra concerns a set of

transformation operations, as listed below:

 Rename: t=rename

〈

〉

, generates a

construction that is the same as a construction

s, but with a different name t. For example,

editor=rename

〈

publisher

〉

;

 Merge: t=merge

〈

,..s

〉

, generates a

construction t whose value is obtained by

concatenating s

,..s

in the case of strings. For

example, address=merge

〈

street, city, zip

〉

;

 Split: (t

,..t

)=split

〈

〉

, where t

,..t

are obtained

by splitting a construction s with respect to a

separation criterion. For example, (first-name,

last-name)=split

〈

name

〉

;

 Parity: t=parity

〈

〉

, generates a construction t

which has the same content and label as s. for

example, author=parity

〈

author

〉

5 DISCOVERY OF NODES AND

EDGES MATCHES

Most schema matching algorithms (Li et al., 2000),

(Doan et al., 2003), (Mitra et al., 2000), (Milo and

Zohar, 1998), (Palopoli et al., 2000), (Castano et al.,

2004), (Do and Rahm, 2001), (Melnik et al., 2002),

(Noy and Musen, 2001), (Giunchiglia et al., 2004)

produce similarity scores between source and target

schemas nodes such as the ones we produce in

section 5.3; however, such a mapping result partially

solves the problem. First, produced similarities

between individual nodes are not enough to produce

access paths for retrieving data from the available

sources. For example, based on previous matching

techniques, we obtain a set of node matches such as

the match between laboratory and laboratory, or

between researcher and author or between book and

book. Without matches between edges, however, it

may be impossible to distinguish authors that wrote

books, from authors that wrote articles. Intuitively, a

source-to-target mapping should describe all the

produced mappings such as one-to-one mappings,

complex mappings identified using type hierarchy

have to be incorporated in the matching result and

further complex mappings have to be discovered. To

do this, we proceed in four steps (figure 2).

5.1 Admissible Node Identification

While generating mapping elements, we apply a top-

down strategy (We use the same top-down strategy as in

(Xu, 2003b). However the difference is that this technique

is used to discover structural similarity. In our approach, it

is just for mapping generation; the structural matching has

been already performed)

. At the top level, we establish

correspondences between complex nodes of the

target and source schemas. Matched complex nodes

are called compatible nodes. In figure 1(a), the

complex node laboratory is considered to be a

compatible node since it is matched to node

laboratory in the schema graph of figure 1(b), while

node library is not a compatible node. Then, at the

bottom level, with the guide of compatible nodes

between the target and source schemas, we establish

the finer-level correspondences between nodes and

edge sets. Figure 3 illustrates compatible nodes.

Visually compatible nodes are depicted as colored

circles and dashed lines.

5.2 Context Generation for Admissible

Node

After identifying admissible nodes, we proceed to

construct a context for each admissible node by

taking edges around a complex node n into account.

We cluster a set of nodes and edges with a complex

node as a conceptual component in the schema

graph. We call this the context of n. The context for

an admissible node n consists of a set of nodes and a

set of edges among those nodes. For a given

Gtarget

Gsource

Similarity

computation

Admissible

nodes

identification

direct and complex

mapping

Context

generation

Node

mappings

generation

Access

paths

generation

1 2 3 4

CONCEPTUAL FRAMEWORK FOR XML SCHEMA MAPPING

Figure 3: Example of a schema graph after admissible

node identification.

admissible node n, we construct such a context as

follows:

1. Including all atomic directly node related to

the admissible node n.

2. Including all non-admissible nodes directly

connected to n with their connected atomic

nodes and connected non-admissible nodes.

We continue this procedure until we find an

admissible node.

3. If a directly connected admissible node is

also similar to an atomic node, it is also

included in the context of n.

4. Including all nodes having an association

relationship with n and their respective

context.

5. Including all containment relationships

between nodes in the context of n.

Example: Figure 4 illustrates a schema graph of

Figure 3 after context construction. The context of

the admissible node laboratory includes atomic

nodes name and location and non-admissible node

Library. The context of admissible node Article

includes referential node Journal and its context.

The context of node laboratory includes the

admissible node address, since address is similar to

a leaf node (location) belonging to the context of a

matched node laboratory.

5.3 Node Mappings Generation

At this stage, we have completed with the top-level

comparison between source and target schema

graphs. We are now ready to detect node and edges

matches at the bottom level. For each matching pair

, n

) which represented two admissible nodes in

source and target schema graphs, we make use of the

node similarity score generated in previous work

Figure 4: Example of a schema graph after context

generation.

(Zerdazi and Lamolle, 2007) to settle node matches.

The following gives examples on how we proceed.

Example 1: Let the schema in Figure 5(a) be the

source schema and the schema in Figure 5(b) be the

target schema. Consider the two admissible nodes,

source node laboratory (laboratory

) and the target

node laboratory (laboratory

), we first settle node-

set matches between both source and target contexts

that hold with the highest node similarity score. As

an example, we settle the match pair (name

, name

)

using a parity operator.

Example 2: The target node address

is both similar

to the source nodes laboratory/location

and

author/address

with approximatly the same scoring.

This is due in the case of laboratory/location to the

fact that ancestor context similarity is high and in the

case of author/address to the fact that leaf context

similarity is high (Zerdazi et al., 2006). Since target

node laboratory/address and source node

author/address belong to non-admissible complex

nodes while target element laboratory/address and

source element laboratory/location belong to two

admissible contexts, a match is then derived between

source node laboratory/location and target node

laboratory/address.

Moreover, since we have decided to map a non-leaf

node with a leaf node, a complex mapping with split

operation can be deduced.

5.4 Access Path Generation

With the available correspondences between nodes

in source and target schemas, we further discover

matches between edges. As in (Xu, 2003), the

recognition of edges matches starts by locating an

ICEIS 2008 - International Conference on Enterprise Information Systems

Figure 5: Source and target schema graphs after context generation.

edge set e

in G

. Then, based on nodes n

connected

by e

, we can locate a set of nodes that correspond to

in G

, from which we either locate or derive an

edge set e

that corresponds to e

. We essentially

focus on the discovery of access paths in order to

retrieve source data when performing

transformations. For each target element t, we first

define the access path indicating where matched

source elements are localized, then the discovered

transformation operation and finally the conditions

under which the mapping element holds true.

It is widely accepted that the matching process

cannot be fully automated. Thus is necessary to

incorporate user feedback on the matching task. The

following stage will be validating mapping result

and constraint filtering.

6 CONCLUSION

In this paper, we have presented a framework for

discovering structural mappings between XML

schema. This approach produces a set of mappings

for a target schema considering a set of source

schemas and a set of correspondences; each target

mapping has different semantics.

Since the result of our approach is a set of

structural mappings, one interesting perspective is to

take advantage of these multiple semantics; the

system should be able to select the mapping that best

fits the needs of a specific user, using preferences or

quality criteria. To achieve this goal, the users are

invited to validate the final mapping result with

having the possibility to add, delete or update

mapping elements. The accuracy of a matching

system considerably reduces post-matching user

efforts. Another perspective of our work is the

maintenance of the mappings. In dynamic

environments such as the Web, data sources may

change not only their data but also their schemas and

their semantics. Such changes must be reflected in

the mappings. Mappings left inconsistent by a

schema change have to be detected and updated. It is

important therefore to develop techniques for

automatically adapting mappings as schemas evolve.

Authors in (Velegrakis et al., 2003) proposed a

mapping adaptation technique, but which essentially

deals with relational schema changes).

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