COMBINING SEMANTIC INFORMATION AND INFORMATION
QUALITY ON THE ENRICHMENT OF WEB DATA
INTEGRATION SYSTEMS
Damires Souza
1
, Bernadette Farias Lóscio
2
and Ana Carolina Salgado
2
1
Federal Institute of Education, Science and Technology of Paraiba (IFPB), João Pessoa, Paraíba, Brazil
2
Center for Informatics, Federal University of Pernambuco (UFPE), Recife, Pernambuco, Brazil
Keywords: Semantic Information, Information Quality, Web Data Integration Systems.
Abstract: The emergence of the Web and its permanent growth has caused a big impact on the database research
community. Thereby, Database research areas have evolved in order to consider the new problems arising
from the need of managing the huge volume of data available on the Web. One of such areas is Data
Integration (DI), which is considered a pervasive challenge faced by applications that need to query across
multiple autonomous and heterogeneous data sources. To help matters, we argue that semantic information
like ontological and contextual information, combined with Information Quality (IQ) provided by IQ
measures, may be employed together in order to enrich processes in DI (e.g., schema matching and query
answering). In this paper, we present our ideas regarding what we mean by semantic information and IQ and
why and how they may be combined in order to produce semantic knowledge to be used in Web Data
Integration Systems. Furthermore, we propose a preliminary version of a metamodel, which presents a
formal description of relationships between concepts associated with semantic information and IQ.
1 INTRODUCTION
The increasing use of the Web and the development
of communication infrastructures have led to a
demand for high-level integration of distributed,
autonomous and heterogeneous data sources. In
order to meet such demand, different kinds of Web
data integration systems, including Mediation
Systems (Halevy et al., 2006), Peer Data
Management Systems (Sung et al., 2005), and
Dataspaces (Hedeler et al., 2009) have been
proposed. In general, these solutions are
characterized by an architecture mainly constituted
by: i) a set of autonomous data sources ranging from
traditional databases to semi-structured or non-
structured data repositories, ii) an optional set of
global schemas representing integrated views of the
distributed data, and iii) a set of mappings, i.e.
associations between data source elements as well as
associations between data source elements and
global schema elements. On the other hand, to offer
a uniform view of heterogeneous and distributed
data, a data integration system must provide
solutions for several processes as, for example,
query answering (Souza et al., 2009;
Stuckenschmidt et al., 2005) and schema matching
(Pires et al., 2009; Giunchiglia et al., 2004).
Despite a lot of research done in this area, Data
Integration (DI) on the Web remains a challenging
problem mainly due to the heterogeneous and
autonomous nature of the data sources. Among the
techniques employed to help to overcome these
problems (Halevy et al., 2006), the adoption of
semantic knowledge has shown to be a helpful
support to deal with this.
In this work, we consider that semantic
knowledge may be produced by considering the
combination of semantic information and
information quality. In a general way, semantic
information concerns the information that helps to
assign meaning to elements (e.g., schema elements)
or expressions (e.g., queries) that need to be
interpreted in a given situation (Souza et al., 2011;
Mandreolli et al., 2009). On the other hand,
information quality (IQ) is a multidimensional
aspect of information systems and it is based on a set
of dimensions or criteria, which are used to assess
and measure a specific IQ aspect (Batista and
Salgado, 2007; Wang and Strong, 1996).
219
Souza D., Farias Lóscio B. and Salgado A..
COMBINING SEMANTIC INFORMATION AND INFORMATION QUALITY ON THE ENRICHMENT OF WEB DATA INTEGRATION SYSTEMS .
DOI: 10.5220/0003961602190224
In Proceedings of the 8th International Conference on Web Information Systems and Technologies (WEBIST-2012), pages 219-224
ISBN: 978-989-8565-08-2
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
Specifically, we are interested in semantic
information provided by ontologies and context.
Ontologies formally represent knowledge of a given
domain through the definition of concepts, the
relationships between them, axioms and individuals
(Baader et al., 2003; Gruber, 1995). Context may be
defined as a set of elements surrounding a domain
entity of interest (e.g., user, query, or data source),
which is relevant in a specific situation during some
time interval (Bolchini et al., 2009; Souza et al.,
2008; Dey 2001).
In the setting of DI solutions, ontologies may be
used, for example, as a standard model to represent
data sources metadata or as background knowledge
to help solving heterogeneity problems. In a similar
way, context may help to deal with information that
can be acquired only on the fly (e.g., the availability
of data sources), and which is perceived at run time.
We also argue that IQ assessment is fundamental
for the improvement of data integration processes
(Keeton et al., 2009; Roth and Nauman, 2005). IQ
evaluation may contribute to minimize the query
answering time as well as to enhance the quality of
query answers, for example.
In this work, we are mainly interested in how
semantic information and IQ may be combined in a
properly way in order to bring substantial gains for
the overall DI processes. Particularly, we present our
ideas with regards to the following issues:
• What we mean by semantic information and
information quality.
• How semantic information and information
quality may be combined in order to produce
semantic knowledge.
Regarding the second issue, we propose a
preliminary version of a metamodel, which presents
a formal description of relationships between
concepts associated with semantic information and
IQ. This metamodel has been developed as an
ontology, being compliant to OWL standard (Baader
et al., 2003).
This paper is organized as follows: Section 2
introduces the main concepts underlying Semantic
Information and IQ; Section 3 discusses a
motivating scenario in the light of the presented
concepts; Section 4 presents the preliminary
metamodel. Finally, Section 5 points out some
considerations and highlights important topics for
further research.
2 BACKGROUND CONCEPTS
Semantic information has been increasingly used as
a means to enhance DI processes by assisting them
to deal with the heterogeneous and autonomous
nature of distributed data sources. Meanwhile, IQ
has become a critical aspect of Information Systems
research (Ge and Helfert, 2007) and, consequently,
of DI systems (Duchateau and Bellahsene, 2010;
Wang 2010). Some works (Yasar et al., 2011;
Helfert and Foley, 2009; Molina and Olsina, 2008)
have discussed the use of semantic information
together with information quality, but they do not
present how these concepts may be combined to
properly enrich an information system. Particularly,
we argue that in order to enhance processes in DI by
using IQ criteria, it is necessary to take into account
the data semantics as well as the context around the
process at hand. In this section, we provide an
overview of the semantic information and IQ
concepts to provide a better understanding of our
proposal.
2.1 Semantic Information
As mentioned earlier, semantic information concerns
the information that helps to assign meaning to
elements or expressions that need to be interpreted
in a given situation. Specifically, we are interested in
semantic information described through ontologies
or provided by context.
An ontology is a representation of a shared
understanding of concepts in a particular domain of
interest as agreed by a community (Gruber, 1995).
The knowledge captured in ontologies can be used,
among other things, to annotate data, generalize or
specialize concepts, and infer entirely new (implicit)
information (Baader et al., 2003).
There has been a growing interest in using
ontologies for solving data heterogeneity problems.
In the DI setting, for example, ontologies have been
used for some purposes, including (Xiao, 2006): (i)
metadata representation: each data source is
represented by a local ontology; (ii) global
conceptualization: an ontology, called global
ontology, may be employed to provide a conceptual
view over the schematically heterogeneous source
schemas; and (iii) support for high-level queries:
given a global ontology, users can formulate queries
without specific knowledge of the different data
sources.
In addition, ontologies may also be used as a way
of providing a domain reference. Considering a given
knowledge domain, an agreement on its terminology
can occur through the definition of a domain
ontology, which can be used as a semantic reference
or background knowledge to enhance processes such
WEBIST2012-8thInternationalConferenceonWebInformationSystemsandTechnologies
220
as ontology matching (Pires et al., 2009).
Another kind of semantic information
increasingly used is context. Usually, context is
concerned with some specific situation, most of the
times perceived as a set of variables that may be of
interest for an agent (Bolchini et al., 2009). Context
may also be understood as the circumstantial
elements that make a situation unique and
comprehensible (Dey, 2001). More abstractly, Vieira
et al. (2007) makes a distinction between contextual
element (CE) and context. The former is any piece
of data or information that enables to characterize an
entity in a domain. The latter is the set of
instantiated contextual elements that are necessary to
support a task at hand.
In works regarding data integration settings,
context may be used to: (i) data tailoring, in order to
define context-aware data views over large
information systems (Bolchini et al., 2009); (ii)
schema reconciling, to identify in which context the
elements occur and thus, to ease spell-check and
schema-level sense disambiguation tasks (Belian et
al., 2010) and (iii) query answering, where query
results may be expanded with meaningful related
answers according to the context acquired at query
submission time (Souza et al., 2009).
2.2 Information Quality
The notion of Information Quality (IQ) has emerged
during the past years and shows a steadily increasing
interest (Duchateau and Bellahsene, 2010; Keeton et
al., 2009; Roth and Nauman, 2005). As mentioned
earlier, IQ is a multidimensional aspect and it is
based on a set of dimensions or criteria, which are
used to assess and measure a specific IQ aspect. One
of the best known quality dimensions classification
is presented by Wang and Strong in (Wang and
Strong, 1996). They have conceived one of the first
sets of structured and classified quality dimensions
that has been a strong reference for most of the
studies in this area.
Regarding a DI system, there are some key points
in which it is possible to consider IQ analysis, for
instance: data source schema, global schema, data
source selection, query processing, query routing,
data integration and data materialization (Duchateau
and Bellahsene, 2010; Wang, 2010; Keeton et al.,
2009; Batista and Salgado, 2007). Also, it is possible
to enumerate several IQ criteria that can be
associated with these DI components or processes
(e.g., schema minimality, data source availability).
In the next section, we present a motivating
example to better illustrate the use of semantic
information and IQ on a DI scenario.
3 A MOTIVATING SCENARIO
Our motivating scenario regards a Web Data
Integration System, which integrates data from a
given domain (e.g., tourism, life science) distributed
over a set of data sources related to each other by
means of mappings. The data source schemas are
represented by ontologies and the mappings between
them are incrementally identified according to the
queries posed to the system. Given this DI scenario,
in the following, we discuss how semantic
information and IQ may be used to enrich each one
of the steps that compose the query answering
process of the considered data integration system.
(1) Query Submission: whenever the user poses a
query Q, the current context of the user and the
context of the data source where the query was
posed must be acquired. For example, user
preferences, user interface and data source’s
identification are relevant and should be gathered.
Meanwhile, the query’s context, by means of
required information and operators, is also figured
out.
(2) Query Routing: during this step, contextual
information acquired at query submission time may
be used to determine the most relevant data sources
to answer Q. Candidate data sources are also
analysed according to their context (e.g., data model,
location). At the same time, IQ metrics regarding the
data sources reputation and access frequency may be
taken into account. As a result, a set of data sources,
called relevant data sources, are defined as the ones
to send the query.
(3) Schema Matching: given the set of relevant
data sources, an ontology matching is accomplished
in order to identify mappings among the
corresponding data source ontologies. To this end, a
domain ontology is used as background knowledge
to better identify the semantic mappings between the
data source ontologies.
(4) Query Reformulation: during this step, the
original query Q is reformulated into a set of queries
to be submitted to the relevant data sources. This is
done considering the mappings obtained during the
Schema Matching step. IQ metrics such as the
mappings confidence should be considered for the
selection of the best mappings to be employed
during the reformulation of Q.
(4) Query Execution and Answers Integration:
reformulated queries are submitted to the
corresponding data sources. Next, the results are
COMBININGSEMANTICINFORMATIONANDINFORMATIONQUALITYONTHEENRICHMENTOFWEB
DATAINTEGRATIONSYSTEMS
221
returned to the data source where the query Q was
originally posed and then they are integrated to
obtain a single integrated answer.
(5) Result Presentation: during this last step, the
integrated answer can be presented in various forms
according to the user’s preference, query interface
and intended usage. At this moment, IQ metrics
regarding accuracy and relevancy of the integrated
answer may also be established.
In summary, this example aims to show some of
the important usages of IQ and semantic information
in order to enrich a DI query answering process:
(i) it enables the analysis of the user’s query
through its interpretation and identification of
related entities and necessary operators on the fly;
(ii) it helps to identify the most relevant data
sources that may contribute with answers to a given
query, thus improving query answering results;
(iii) ontologies as background knowledge provide
means to identify different kinds of mappings
between pairs of data sources;
(iv) since the effects of collecting and integrating
answers from various sources need to be handled,
context may enrich the post-processing of the
retrieved answers to adjust the final result
representation according to the user preferences or
intended level of detail and
(v) acquired contextual elements and IQ metrics
may be stored in a knowledge base for later
recovery, helping to identify trends in other future
query answering processes.
4 TOWARDS A SEMANTIC
KNOWLEDGE METAMODEL
A metamodel can be viewed as a model of a modeling
language (Fuchs et al., 2005) that defines the
semantics for the main concepts that should be used to
build other models. Thereby, the task of putting
together the concepts of semantic information and
information quality may be more easily accomplished
by using a metamodel layer. Particularly, such
metamodel should specify the constructs related to
semantic information and IQ, as well as their
relationships, providing a conceptual infrastructure to
support the building of specific models.
Our metamodel has been developed as an
ontology. Since Description Logics (DL) provides
the formal semantics for specifying ontologies
(Baader et al., 2003), we may gain benefits in terms
of expressiveness and reasoning mechanisms. Also,
ontologies have been considered an interesting
approach because they enable sharing and
reusability (Souza et al., 2008; Wang et al., 2004).
In order to figure out the metamodel constructs,
we followed a participatory and incremental design
methodology. The ontology has been developed
during a series of face-to-face meetings between
experts who are concerned with issues related to
semantic information usage and IQ in DI Systems.
The proposed metamodel with its main constructs is
presented in Figure 1 and explained as follows.
The main concepts underlying the metamodel are
Semantic_Information and Information_Quality.
Both are subconcepts of Information. The former
concerns information provided by ontologies,
defined here as Ontological_Information, and
context, defined as Contextual_Information. The
latter concerns information obtained through IQ
metrics. Both information are supposed to be
identified and used when associated with a specific
Situation, which is composed by a set of Processes.
A Domain_Entity is defined as anything in the real
world that is relevant to describe the domain we are
dealing with. Contextual_Elements are used to
characterize a given domain entity. Besides, a
Measurement is defined as a score value
characterizing a particular IQ criteria.
In this sense, combining both semantic
information and IQ in a given situation may lead to
relevant Semantic_Knowledge. To this end, rules
and axioms are being developed as a way to allow
inference and consistency conditions check.
5 CONCLUSIONS
Due to the ever increasing complexity of Web Data
Integration systems, the usage of semantic
information and IQ is becoming more and more a
necessity, instead of an optional requirement. These
systems are highly dynamic and the semantic
knowledge around their processes is rather relevant
to produce results which best meet the users’ needs.
In this sense, this work presented some ideas
regarding the benefits of combining semantic
information and IQ in order to enrich the common
processes of a DI system. Furthermore, it was
proposed a preliminary metamodel, which aims to
bring together the relationships between both
concepts in order to allow inferring knowledge more
properly.
Developed as an ontology, such metamodel will
be the basis for the development of other models.
Furthermore, it will provide the ability of reasoning
over the information.
WEBIST2012-8thInternationalConferenceonWebInformationSystemsandTechnologies
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Figure 1: Semantic Knowledge Metamodel.
As further work, we will completely formalize the
metamodel. We also plan to support developers with
a framework that may provide the combined use of
semantic information and IQ.
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