Integrating Semi-structured Information using Semantic

Technologies

An Evaluation of Tools and a Case Study on University Rankings Data

Alejandra Casas-Bayona and Hector G. Ceballos

Campus Monterrey, Tecnologico de Monterrey, Av. Eugenio Garza Sada 2501, 64849, Monterrey, Nuevo León, Mexico

Keywords: Semantic Technology, Ontologies, Information Integration, Information Reconciliation, Best Practices.

Abstract: Information integration is not a trivial activity. Information managers face problems like: heterogeneity (in

data, schemas, syntax and platforms), distribution and duplicity. In this paper we: 1) analyze ontology-based

methodologies that provide mediation frameworks for integrating and reconciling information from

structured data sources, and 2) propose the use of available semantic technologies for replicating such

functionality. Our aim is providing an agile method for integrating and reconciling information from semi-

structured data (spreadsheets) and determining to which extent available semantic technologies minimize

the need of ontological expertise for information integration. We present our findings and lessons learned

from a case study on university rankings data.

1 INTRODUCTION

Information has become a fundamental asset for

company’s competitiveness. Integrating information

distributed across systems and platforms in the

organization for its later analysis has become the

most important task for information managers.

Integrating information residing in distributed and

heterogeneous data sources is a well-known problem

that faces two difficult challenges: heterogeneity and

reconciliation.

Heterogeneity can be classified in four

categories: 1) structural, i.e. different schemas in

data sources; 2) syntactical, i.e. different ways of

naming the same object (catalogues); 3) systemic,

i.e. diverse platforms governed by different

authorities; and 4) semantic, i.e. different meanings

for same concept or different names for the same

concept (Buccella, Cechich, & Brisaboa, 2005). For

(Cui, Brien, & Park, 2000), resolving semantic

heterogeneity consists in identifying equivalent

concepts, no-related concepts and related concepts.

On the other hand, data reconciliation has been

classified on two levels: schema and instance

(Bakhtouchi, Jean, & Ait-ameur, 2012). Schema

reconciliation consists on finding equivalences

between tables and columns from different data

sources, i.e. it addresses structural heterogeneity,

whereas instance reconciliation consists on

identifying instances that represent the same entity

in the real world despite their multiple

representations (Zhao & Ram, 2008).

Ontologies are particularly suitable for

integrating information as long as they deal with

syntactic and semantic heterogeneity (Silvescu,

Reinoso-castillo, & Honavar, 1997). The term

ontology was introduced by Gruber in the context of

knowledge sharing as “the conceptualization of a

specification” (Gruber, 2009). This is, an ontology is

the description of concepts and relationships that an

agent or a community may use (Gruber, 2008).

Some ontology-based methodologies and

techniques for information integration have been

proposed (Buccella et al., 2005). For example, the

project MOMIS (Mediator environment for Multiple

Information Sources) allows integrating data from

structured and semi-structured data sources

(Beneventano et al., 2001). SIMS is another

mediator platform that provides access to distributed

data sources and online integration (ARENS et al.,

1993). MIRSOFT, unlike the other two approaches,

does not map entirely the origin data sources but

focuses on the minimum necessary data for

answering the integration question (Bakhtouchi et

al., 2012). These approaches support the task of data

interpretation by requiring the participation of the

domain expert at some extent and minimizing the

357

Casas-Bayona A. and Ceballos H..

Integrating Semi-structured Information using Semantic Technologies - An Evaluation of Tools and a Case Study on University Rankings Data.

DOI: 10.5220/0005004203570364

In Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA-2014), pages 357-364

ISBN: 978-989-758-035-2

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

involvement of the database administrator.

In scenarios with periodic data extraction the

cost of the required infrastructure and the time

dedicated by the expert is justified. Nevertheless, in

scenarios where an agile response is needed,

information requirements are volatile, and data is

provided by different departments in semi-structured

formats, a more agile solution is needed. Our

proposal is to use available semantic technologies

for replicating the functionality that these

approaches provide.

In section 2 we describe and analyse the facilities

provided by each approach. In section 3 we

described our approach for information integration

and our criteria for selecting semantic technologies.

In section 4 we exemplify our methodology by

integrating faculty staff information from two data

sources for a university ranking. Finally in section 5

we present a summary of lessons learned and

conclude with closing remarks in section 6.

2 RELATED WORK

Ontology-based information integration approaches

provide a solution for integrating heterogeneous

databases and semi-structured data. Some of them

additionally provide data reconciliation facilities.

Table 1 shows a comparison of the three

analyzed methodologies found in literature related to

ontology-based information integration. For the

comparison we focused in five aspects: 1) the

original information requirement, 2) the way on

which information is mapped, 3) the way on which

integration is performed, 4) reconciliation facilities,

and 5) the schemas used for expressing queries.

Numbers in each row represent the order followed in

each methodology.

The MOMIS methodology starts in the extraction

phase where it uses a wrapper that transform the

structure of available data sources to a model OLDI3

based on Description Logics (Moreno Paredes,

2007). The integration process generates an

integrated view of the data sources (global-as-view)

through the generation of a thesaurus. Then it

performs an analysis for determining affinity

between terms and it makes clusters of similar

classes (Beneventano et al., 2001). Queries are

expressed in terms of thesaurus schemas.

SIMS, on the opposite way, was created

assuming that data sources are dynamic; hence it

starts by defining an initial question from which

proceeds to the selection of suitable data sources, in

order to minimize the cost of mapping schemas. The

integration phase starts with an expert annotating

data sources with ontology schemas and continues

enriching initial mappings with other vocabularies

automatically. SIMS elaborates query plans for

answering queries expressed in terms of the used

ontologies (Arens et al., 1996).

MIRSOFT departs from an initial user

requirement and importing mediator ontology. Then

it requires mapping data sources to mediator

schemas, as well as expressing functional

dependencies (FDs) of mapped classes, providing

ontology-based database access, also known as

OBDB or OntoDB. Before starting the mapping

stage, a selection of relevant classes and properties is

done, producing a pruned ontology. This

methodology additionally uses functional

dependencies for complementing queries and

providing reconciliation in query results

(Bakhtouchi, Bellatreche, Jean, & Ameur, 2012).

Queries are expressed and answered in terms of the

mediator ontology.

Table 1: Comparison of ontology-based methodologies for information integration.

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3 INTEGRATION AND

RECONCILIATION METHOD

We evaluated the use of semantic technologies in

scenarios of periodic integration exercises where

information used for answering queries is provided

by different departments using spreadsheets with

varying formats. Heterogeneity is present in all of its

expressions: information resides in different systems

(systemic), spreadsheets have different columns

(structural), varying column names and use different

catalogues (syntactical), and the same concept is

defined distinctly in each data source (semantic).

Additionally, equivalent individuals can be found

across these data sources with contradictory

information. In this scenario, we cannot rely on

fixed structures, and mappings must be generated

cost-effectively.

Based on the aspects analyzed in previous

methodologies we propose a method suitable for this

scenario that provides ontology-based information

integration taking advantage of current Semantic

Web standards and tools. Stages of our method are

described Figure 1.

Figure 1: Information integration process.

3.1 User Requirement

We start with an initial user requirement expressed

through a global schema represented by an extension

of an existing ontology O

. A suitable OWL

ontology for your domain can be chosen from

ontologies repositories like Linked Open

Vocabularies

. A data requirement is denoted by a

class C

that represents a set of individuals that must

be identified and quantified across the available data

sources. Each class C

is declared in O

as subclass

of a class C

originally contained in O

in order to

make query results compatible with the original

ontology O

i http://lov.okfn.org/dataset/lov/

In this step is important to declare consistency

constraints like if two classes C

are disjoint, this is,

when an individual must be classified as member of

only one of these classes.

3.2 Local Ontologies

Next, each datasheet is transformed to a RDF file

where each row represents an individual of a new

class C

, columns represent properties of the class

and cells are property values that describe each

individual. The resulting class, properties and

individuals constitute a local ontology O

. Available

tools for this purpose are referenced by W3C

3.3 Local-Global Ontology Alignments

The ontology O

is imported in each O

for

expressing the corresponding definition of each C

If the definition of C

only requires information of a

single data source then C

is defined in O

. If the

definition of C

requires information from multiple

sources its definition must be done in the integrated

ontology. The integrated ontology O

is a new OWL

file that imports O

and each O

This stage can be facilitated by ontology

mapping tools that identify semantic

correspondences between elements in two different

schemas (Zohra et al., 2011).Availabletoolsforthis

taskare referredby the Open Semantic Framework

initiative

iii

3.4 Reconciliation

Product of local definitions we can have multiple

individuals representing the same individual.

Reconciliation of equivalent individuals is made

declaring an OWL 2 constraint HasKey for classes

. Another alternative is declaring a SWRL rule or

using a SPARQL CONSTRUCT query that assert

statements SameIndividual for each pair of

equivalent individuals, based on functional

dependencies.

This procedure is especially important when

datasheets contain multiple rows for the same

individual that is being quantified in C

http://www.w3.org/wiki/ConverterToRdf

iii

http://wiki.opensemanticframework.org/index.php/Ontol

ogy_Tools#Ontology_Mapping

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3.5 Inference

Finally, a concept reasoner is used on O

for

classifying individuals in each C

. Reasoners can be

executed from the IDE of a comprehensive

framework like Protégé or Top Braid, or be executed

directly over O

. Another alternative is using an RDF

database with inference capabilities like Virtuoso

or Stardog

In this stage, concept reasoners are used for

detecting inconsistencies between definitions and

data. In order to solve these inconsistencies, violated

constraints can be lift or inconsistent individuals can

be removed from the data set. For instance, an

individual classified in two disjoint classes will be

inconsistent and the solution will be up to the

domain expert.

3.6 Query Resolution

Finally, the domain expert identify and quantifies

individuals classified in each CR by formulating a

query over a SPARQL end-point connected to the

inferred model OI’ or by enquiring the RDF

database using the reasoning level required by

definitions. CR is quantified by a query like:

SELECT COUNT (DISTINCT (?ind))

WHERE{ ?ind a C

}

Figure 2 shows the inputs (O

, O

, L

), the

working models (O

, O

’), and tools. Stages

of the method are numbered as well.

4 CASE STUDY

Our case study was carried out in a University that

provides statistical data to an international Ranking

organization. In this sense, the rankings department

collects information in spreadsheets from several

departments. In order to identify and classify the

university academic staff, information is collected

from both Human Resources and Academic

departments.

The integration was initially performed by a

computer science specialist with basic knowledge on

ontologies, following the use cases from (Allemang

& Hendler, 2008). After a series of integration

exercises that allowed us to evaluate available tools

we asked two other specialists in information

http://virtuoso.openlinksw.com/rdf-quad-store/

http://stardog.com/

integration to replicate one of the integration

exercises. For this purpose we prepared two user

guides with a summary of use cases given in

(Allemang & Hendler, 2008), where OWL

constructors are classified according to integration

or reconciliation purposes. Reconciliation guide is

shown in the Appendix.

Figure 2: Datasets and Tools.

In this section we describe the results of the

evaluation of tools and in section 5 we discuss our

findings and perspectives on semantic technologies.

4.1 User Requirement

The initial requirement is given by definitions of the

ranking evaluation criteria. In our integration

exercise we identified six classes C

that can be used

for answering the faculty staff criteria: Faculty Staff,

Full Time Professor, Part Time Professor, Local

Professor, International Professor and Professor with

PhD. Further calculations like the Full-time

equivalent are out of the scope of the integration and

reconciliation task.

We evaluated Neon Toolkit

, Protégé

vii

and

TopBraid Composer Standard Edition

viii

based on the

following criteria: 1) the usability of its interface, 2)

visualization of definitions, 3) facilities for

importing data from spreadsheets, 4) configurable

reasoning levels, and 5) facilities for exporting query

results. Additionally, our selection criteria

considered the minimization of the number of tools

used along the entire process, being TopBraid

Composer choosen for the first four stages and

Protégé for the last two.

On the other hand, we evaluated two ontologies:

vi http://neon-toolkit.org

vii http://protege.stanford.edu/

viii http://www.topquadrant.com/

Spreadsheet

r

G



1



Results

1

2

B

’

Reasoner SPARQL



2

(i)

(ii)

(iii)

(iv)

(v)

Imported

Process (vi)

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VIVO

and Semantic Web for Research

Communities

(SWRC) as base for our global

ontology, and selected the last one because it

provides enough expressivity for our purpose and

has less and simpler schemas.

4.2 Local Ontologies

We used TopBraid Composer facilities for

transforming spreadsheet data to local ontologies,

generating a local ontology for each data source (one

class and N properties). Two local ontologies where

produced, denoted Local A (O

L-A

) and Local B (O

L-

) in the following. Local classes (C

) generated in

each local ontology are denoted Person A (C

L-A

) and

Person B (C

L-B

), respectively. Figure 2 illustrates

with an example the heterogeneity between both

data sources.

Figure 2: Example of data found in both data sources.

4.3 Local-Global Ontology Alignments

In the Global-Local ontology alignments stage we

formulated one definition for each CR per local

ontology, i.e. 12 definitions in total. Figure 3

illustrates some definitions made for OL-A and OL-

B. Prefixes G: and A: represent the ontologies OG

and OL-A respectively.

TopBraid Composer suggested mappings

between equivalent properties in O

L-A

and O

L-B

after

they were imported to the integrated ontology O

(see Figure 4). Mappings proposed by this tool were

correct and facilitated using properties defined in

local ontologies for recovering information from

both data sources (see queries in section 4.6).

G:PTProfessor  A:Person  A:hasContrat = {A,B,C}

(1)

G:FTProfessor  A:Person  A:hasContrat = {D, E, F}

(2)

G:FacultyStaff  G:PTProfessor  G:FTProfessor

(3)

G:FTProfessor  G:PTProfessor ⊆ ⊥

(4)

G:LocalProfessor  A:Person  A:hasNac = ‘Mexican’

(5)

G:LocalProfessor  B:Person  B:hasNacionality = ‘Mex’

(6)

G:InternationalProfessor  ¬ A:MexicanProfessor

(7)

Figure 3: Example of definitions in local ontologies.

ix http://www.vivoweb.org

x http://ontoware.org/swrc/

Figure 4: Equivalent classes and properties.

4.4 Reconciliation

Reconciliation was done through the assertion of

statements SameIndividual between persons with the

same employee identification number. In O

L-A

this

property is called hasId, whereas in O

L-B

the

equivalent property is named hasIdentification. This

procedure was done with a SPARQL CONSTRUCT

query.

4.5 Inference

For the reasoning stage we evaluated the reasoners

integrated in Protégé and TopBraid Composer:

FACT++, Hermit, OWLIM

and Pellet.

Not a single reasoner in TopBraid Composer

could be configured for making the Closed World

Assumption (CWA), hence it was not possible to

classify individuals based on definitions like

International Professor that uses a ComplementOf

constraint. This was not the case of Hermit in

Protégé which makes the CWA by default and

correctly made this classification. The resoner that

had better performance in TopBraid Composer was

OWLIM.

Thanks to the definition of Full-time Professor

and Part-time Professor as disjoint classes it was

possible to detect inconsistencies between data

sources. Figure 4 illustrates an inconsistency of this

type, where the same professor is classified as Part-

time in O

(A:hasContract = A) and as Full-time in

(B:hasContract = X), given local definitions. To

solve this problem we removed the disjoint

constraint and adjusted the query for retrieving Part-

time professors as explained in next section.

4.6 Query Resolution

Information required for answering rankings

http://owlim.ontotext.com/display/OWLIMv40/OWLI

M-Lite+Reasoner

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questions were expressed in SPARQL through the

combination of classes C

. For instance, for

determining the number of Full Time International

Faculty Staff we used the intersection of Full Time

Professor and International Professor.

Given that we found professors classified as

Full-time and Part-time the query for retrieving Part-

time Professors was adjusted as follows:

SELECT COUNT (DISTINCT (?ID))

WHERE{

?prof a G:PTProfessor .

?prof A:hasID ?ID .

FILTER NOT EXISTS {?prof a G:FTProfessor.}

We also needed to adjust the query for retrieving

International professors using OWLIM. Given that it

does not support CWA, the following query asks for

faculty staff and discards local professors:

SELECT COUNT (DISTINCT (?ID))

WHERE{

?prof a G:FacultyStaff .

?prof A:hasID ?ID .

FILTER NOT EXISTS {?prof a

G:MexicanProfessor.}

5 DISCUSSION

In this section we describe the lessons learned from

tool’s maturity and the experience of non-ontologist

users with our methodology. We also identify the

advantages of ontology-based versus relational-

based integration, and delimit the reach of ontology

mapping tools in the integration process.

5.1 Lessons Learned

From the proposed method and the evaluation of

tools we get to the following conclusions.

 In our scenario, an ontologist expert could

replicate most of the functionality provided by

mediator architectures described in section 2,

including reconciliation of individuals.

 It was possible to represent semantically the

concepts in our case study with constructors

supported by OWL 2 profiles.

 Deficiencies in inference support was remediated

through the use of SPARQL.

 Inference in memory becomes unfeasible with a

relative small ammount of data (above a

thousand records with more than 30 columns).

We have to decide between preselect the

columns to use in the integration, or use a RDF

database with inference support. In the first case,

information that could be useful for further

integrations is lost, and in the second we sacrify

the facilities provided by the IDE for making

definitions and export results.

From the experience of users following our

method and the selected tools we draw the following

preliminary conclusions:

 The most dificult concept to understand for

database specialists was the notion of Open

World Assumption in the construction of

definitions. Unfortunately most of the

reasoners implement this assumption and

forces the user to adopt the equally confusing

notion of negation as failure (c.f. queries in

section 4.6).

 The usability of the selected tools was the best

given our evaluation and user guides prepared

for applying OWL constructors in the

integration and reconciliation stages (see

Appendix), in despite the users found hard to

understand the meaning of OWL constructors

without a previous introduction that include

notions of set theory.

 Interfaces for constructing OWL definitions are

not suitable for non-experts on ontology

languages.

5.2 Ontology-based versus

Relational-based Integration

Volatile input formats in our scenario make

affordable the use of semantic technology as long as

avoids the necessity of creating fixed-data structures

(tables) that cannot be reused in subsequent

integrations.

On the other hand, the absence of common keys

between datasources requires the use of entity

resolution techniques, which have been developed

for both technologies. Nonetheless, the semantic

representation allows representing equivalences

between individuals that produce an automatic

horizontal integration once datasources are merged.

Silk is an example of a tool deviced for entity

resolution in Linked Data (Volz, et al., 2009).

Despite reasoners allow inferring information

like individual equivalences based on keys, it is

necessary to merge the information from all

datasources in a single repository or having

distributed stream reasoning. This kind of

technologies are still emerging. Stardog is an

example of it.

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5.3 Ontology Mapping Tools

Ontology mapping approaches may solve the

problem of aligning local and global schemas,

avoiding (or at least reducing) the need of a non-

ontologist user to decide which OWL constructors to

use for integrating and reconciling information.

Despite local schemas may contain few

properties (datasheet columns), mapping them to

large global ontologies may be largely benefited

from the use of these tools, as ilustrated by

(Rodriguez, et al., 2011).

However, the automatic discovery of mappings

could exacerbate the problem of reasoning in large

amounts of data producing an excess of mappings

between schemas that are not used to answer the

question that motivates integration.

6 CONCLUSIONS

The need of an agile process for information

integration has become more evident in large

organizations, as much as for trivial questions as for

building indicators that involve information

managed by external organizations.

We presented a method supported by current

semantic technologies that provide information

integration of heterogeneous data sources. In

scenarios where data is provided in semi-structured

formats and varies over time, our method matches

the results obtained by other robust mediator

architectures in a cost-effective manner.

Nevertheless, information integration requires a

deep understanding of ontological representations in

order to perform a proper integration and

reconciliation, despite the facilities provided by

current tools.

Despite semantic web languages and protocols as

well as current semantic technologies are mature

enough for enabling information integration there

are still some issues that must be addressed. For

instance, by providing scalable reasoning on large

amounts of data, friendly interfaces for non-ontology

experts and configuration options for reasoners that

allows making the Closed World Assumption when

the information is complete.

Solving these problems will allow taking

advantage of ontology mapping tools for discovering

all possible alignments between local and global

schemas. Furthermore, it will be possible to select

automatically the domain ontology that better suits

(best scored matches) to a set of local schemas.

In this exercise we classified OWL constructors

according to their usage in the integration stage, but

they can be further classified according to the

heterogeneity type they address, in order to facilitate

user’s adoption.

ACKNOWLEDGEMENTS

Authors thank to Tecnologico de Monterrey and

CONACYT for sponsoring this research through

grants 0020PRY058 and CB-2011-01-167460,

respectively.

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APPENDIX

Table 2 is an example of the guide with

reconciliation operators provided to database

specialist during the integration exercise. Cases and

solutions were obained from (Allemang & Hendler,

2008). A similar table was prepared for integration

operators.

Table 2: User’s guide with OWL reconciliation operators.

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