RockQuery

An Ontology-based Data Querying Tool

Jose Lozano, Joel Carbonera, Marcelo Pimenta and Mara Abel

Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, RS, Brazil

Keywords:

Ontology, Ontology View, Ontology Engineering, Human-computer Interaction, Interface, Interaction, Visual

Query Systems.

Abstract:

Nowadays many petroleum companies are adopting different knowledge-based systems in order to improve the

reservoir quality prediction. In the last years, these systems have been adopting ontologies for representing

the domain knowledge. However, there are still some challenges to overcome for allowing geologists with

different backgrounds to retrieve information without the help of an information technology expert. New

terminology can be added to the ontology, making the user interaction cumbersome, especially for the novice

users. In this paper, we propose an approach that combines ontology views with Human-Computer Interaction

(HCI) techniques, for improving the user interaction in computer applications, by reducing the overload of

information with which the user should handle for performing tasks. We propose RockQuery; a new Visual

Query System that applies our approach, and which is able to present to the user only the knowledge that

is relevant for supporting the required query formulation. In addition, the interaction design of RockQuery

includes data visualizations that help geologists to make sense of the retrieved data. In order to test our

approach, we evaluated the impact of using ontology views in the performance of the users for formulating

queries.

1 INTRODUCTION

In the last years, the adoption of ontologies by

knowledge-based systems has been increasing. These

systems evolve over time and, during this process, the

ontologies are extended by including new terminol-

ogy. The users perform queries in these knowledge-

based systems in order to retrieve information for sup-

porting decision-making processes. They have differ-

ent ways of formulating a query, but the process can

be summarized as follows. Firstly, the users organize

in their minds a set of concepts that are required to

formulate the query. Then, the users formulate the

query by combining concepts and instances of those

concepts. Finally, the users perform the query on the

information system. Sometimes, the performing of

this query process can be challenging, because the

users do not ﬁnd in the application the required con-

cepts. This can happen because the interface presents

to the user a huge amount of concepts or relations.

This situation characterizes what the literature calls

information overload. According to (Ho and Tang,

2001; Keim et al., 2008; Karr-Wisniewski and Lu,

2010), information overload happens when an indi-

vidual is oversupplied with information, or when the

quantity of information exceeds the cognitive capa-

bility of an individual or, in other words, when an in-

dividual’s information processing capabilities are ex-

ceeded by the information processing requirements.

The same authors state that this problem affects not

only individual’s work performance, but also business

productivity on an organizational level.

Another reason that hinders the users to retrieve

the desired information is the interaction design ap-

plied in the visual query system (VQS). In some tools,

the interface is not well designed to support end-users

in the query process.

In this work, we claim that it is important to de-

velop computer applications that avoid the informa-

tion overload, by identifying only the knowledge that

is relevant for the task at hand, reducing the amount

of concepts or relations that are presented to the users.

We also claim that this can be taken as a principle

for guiding the interaction design of visual query sys-

tems.

In this paper, we propose an approach for im-

proving the user interaction in ontology-based visual

query systems. Our approach combines HCI notions

and methods with the notion of ontology view. In this

context, an ontology view is a smaller sub-set of a big-

Lozano J., Carbonera J., Pimenta M. and Abel M..

RockQuery - An Ontology-based Data Querying Tool.

DOI: 10.5220/0005379500250033

In Proceedings of the 17th International Conference on Enterprise Information Systems (ICEIS-2015), pages 25-33

ISBN: 978-989-758-098-7

 2015 SCITEPRESS (Science and Technology Publications, Lda.)

ger ontology, which is developed for meeting the re-

quirements of a speciﬁc task. The resulting approach

reduces the overload of the information, by present-

ing to the user only the information that is required

by the task at hand. This approach also follows the

Nielsen’s principle of memory recognition and recall

(Nielsen, 1993), which states that is preferable that

the interface present to the user the information that

she/he can recognize.

Besides that, in this paper we also present Rock-

Query; a new VQS for consulting petrographic data,

which applies our approach. Our system has three

main aspects: (1) the interface design, (2) the rep-

resentation of query results with data visualizations,

and (3) the ontology view graph used for formulat-

ing query. In order to evaluate our approach, we

analyzed the impact of using ontology views in the

performance of the users for formulating queries in

RockQuery.

In Section 2, we analyze four visual query sys-

tems. In Section 3, we brieﬂy present the notion of

Well-founded Ontology Views, which is applied in our

work. Section 4 describes the RockQuery design and

presents the application of the tool in a case study.

Section 5 presents the results of the evaluation of

RockQuery. Finally, Section 6 presents our main con-

clusions.

2 RELATED WORKS

In this section, we describe four ontology-based vi-

sual query systems that were proposed in the litera-

ture. Our aim is to analyze the main features of visual

query systems and to evaluate if the state of the art

systems are able of presenting to the user only the in-

formation that is required by the user for a given task.

PetroQuery

(Castro et al., 2005) is a mature

commercial VQS that supports multidimensional,

user-deﬁned queries over a petrographic database

structured by ontologies. PetroQuery GUI consists of

three sections. In the ﬁrst section, there are list boxes

for concept, attribute and value selection. In the sec-

ond section, the query is shown in a SQL like form in

a list box. The last section contains the results, which

are represented in a table (see Figure 1). The pro-

cess of query formulation starts by selecting one or

multiple samples. After, the user selects the concept,

attribute or value that she/he is looking for in the sam-

ple descriptions. Throughout the process of selecting

concepts, a textual query appears in the second sec-

tion. Each line is a SQL like query. The user can also

delete a line of the query.

At the same time, during query formulation, that

Figure 1: PetroQuery Interface (Castro et al., 2005).

Figure 2: GRQL Interface (Athanasis et al., 2004).

query line text is added to the list box and the result

appears in the table. In addition, the user can visualize

in a ternary plot the results in another window.

Graphical RQL (Athanasis et al., 2004) is a GUI

for browsing and ﬁltering RDFs description bases.

The GRQL GUI consists of three basic interaction ar-

eas. The left area provides a tree-shaped display of

the subsumption hierarchies of both the classes and

properties deﬁned in an RDF schema. The right up-

per area of the GRQL GUI allows the users to explore

progressively the individual RDF class and property

deﬁnitions and generate navigational and/or ﬁltering

RQL queries. Finally, the right lower area presents

the constructed query/view results. A snapshot of the

GRQL GUI is illustrated in Figure 2. The process of

query formulation starts in the left area. The users se-

lect a node in the tree and can access their subclasses

and sub-properties by expanding the tree nodes. After

selecting the concept or property, its complete deﬁni-

tion is shown in the right upper area and the user can

perform operations over the instances.

VisualSPEED (see Figure 3) is a visual query in-

terface that provides a visual query interface, and sup-

ports automatic query generation (de Alencar and Sal-

gado, 2013). The system is structured in two layers:

the user interaction layer and the management layer.

The user interaction layer consists of four modules (a)

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

Figure 3: VisualSPEED Interface (de Alencar and Salgado,

2013).

View Ontology, which is responsible for ontology vi-

sualization; (b) Form Query, which is responsible for

formulating queries that are sent to the query module;

and displaying the results of queries; and (d) View

network, which is responsible for displaying network

topology. The management layer is composed of two

modules that are responsible for the communication

between the user interaction and the SPEED’s core

layers.

The process of query formulation is done through

the selection of concepts in the graphical representa-

tion of the ontology. The selected concepts are shown

on query composition ﬁeld, which is in the form of a

query module. In this area, logical constructors such

as OR, AND or NOT can be used to compose the

query. The query submitted by the user is interpreted

by the system and translated to a SPARQL query to be

executed in data. The users can customize the query

by selecting and prioritizing semantic relationships.

The enrichment of the query is shown in the bottom

right side of Figure 3. The system displays the results

organized in a table shown in the bottom right side

of the Figure 3 and presents the data in another win-

dow. This VQS combines a graph representation for

performing their queries.

OptiqueVQS (Soylu et al., 2013) is a visual query

system for the Optique Scalable End-user Access to

Big Data project

. It relies on ontology-base data ac-

cess (OBDA) framework (Kogalovsky, 2012), which

allows access relational data through ontologies. The

interface consists of three widgets, which can be

viewed as building blocks of a VQS with limited func-

tionality and complexity. The ﬁrst widget (W1 - see

the bottom-left part of Figure 4) is a menu-based

widget for querying by navigation. It allows users to

http://www.optique-project.eu/

Figure 4: Optique Query Interface (Soylu et al., 2013).

navigate through the concepts by pursuing relation-

ships between them. The second widget (W2 - see the

bottom-right part of Figure 4) is a form-based wid-

get, which presents the attributes of a selected con-

cept for operations of selection and projection. The

third widget (W3 - see the top part of Figure 4) is

a diagram-based widget that provides an overview of

the constructed query and affordances for manipula-

tion.

The process of query formulation is described as

follows: a user ﬁrst selects a kernel concept (the start-

ing concept) from W1, which initially lists all domain

concepts accompanied with icons, descriptions, and

the potential/approximate number of results. The se-

lected concept becomes the focus/pivot concept and it

appears on the graph as a variable-node, while in W2

is displayed its attributes, and in W1 is displayed all

concept-relationship pairs pertaining to this concept.

The user can select attributes to be included in the re-

sult list and can impose constraints on them. The user

should follow the same steps for including new con-

cepts in the query. She/he can jump to a speciﬁc part

of the query by selecting the corresponding variable-

node. Furthermore, the user can interact with the sys-

tem in textual form by using the SPARQL mode.

In Table 1, we present a comparison of the four

visual query systems described above, labeled as 1 for

PetroQuery, 2 for GRQL, 3 for VisualSPEED and 4

for OptiqueVQS, respectively. The comparison was

performed according to the following criteria:

RockQuery-AnOntology-basedDataQueryingTool

Result Visualization. Evaluates if the interface has a

section for data visualization. This includes dif-

ferent diagrams that help the user to understand

the data.

Query History. Evaluates if the interface has a sec-

tion for query history. It is common in search in-

terface to have a query history because it helps

users to reuse a previous query.

Ontology Visualization. Evaluates if the interface

has a section for the ontology visualization. An

ontology visualization will help in the exploration

and navigation of concepts to formulate the query.

Text Filter. Evaluates if the interface has text ﬁlters.

Text ﬁlters are important in search interface. It is

the widget where the user informs a keyword for

searching it within a list of terms.

Query Visualizer. Evaluates if the interface has a

section to visualize the query formulation.

Knowledge Adaptation. Evaluates if the system al-

lows presenting in the interface only the amount

of information that is necessary for the task at

hand. This feature especially importante for our

work.

Codiﬁcation. Evaluates if the interface has a section

where the user enters his query using any syntax

of a query language.

The results show that no VQS contains a section

for data visualization in a unique interface. Petro-

Query has the query history feature that other VQSs

do not have. VisualSPEED is the VQS that offers a

panel for ontology visualization. Its visualization use

icons in the representation of concepts. Optique uses

ﬁlters for searching concepts and attributes. Optique

and VisualSPEED provide the capacity of codifying

the query in SPARQL. VisualSPEED, Graphical RQL

and Optique consult over semantic web data stored in

a RDF or OWL database. PetroQuey consults over re-

lational data. No one VQS tries to adapt the informa-

tion shown in the interface to the user requirements.

That is, if the ontology has a huge amount of terms,

the interface will list all of them. We assume that it

would be preferable to avoid the information overload

by presenting to the user only the knowledge that the

user recognizes as relevant to the task at hand.

3 WELL-FOUNDED ONTOLOGY

VIEWS

In (Lozano et al., 2014), the authors propose the no-

tion of well-founded ontology view (WFOV) and a set

Table 1: Comparison of four Visual Query Systems.

Criteria [1] [2] [3] [4]

Result

visualization

No No No No

Query

history

Yes No No No

Ontology

visualization

No Yes No No

Text

Filters

No No Yes Yes

Query

visualizer

Yes Yes Yes Yes

Codiﬁcation No Yes No Yes

Knowledge

adaptation

No No No No

of algorithms that can extract a WFOV from a base

ontology. In this context, ontology views are sub-

sets of an ontology that are extracted according to

the user requirements and that can overlap other on-

tology views. On the other hand, according to the

authors, WFOV are ontology views that preserve im-

portant ontological meta-properties (such as identity,

existential dependency, etc). The ontological meta-

properties adopted in this work are provided by a

foundational ontology called UFO (Uniﬁed Founda-

tional Ontology) (Guizzardi, 2005), which is a theo-

retically well-founded domain independent system of

categories and their ties that can be used to construct

models of speciﬁc domains, such as (Carbonera et al.,

2015; Carbonera et al., 2013; Carbonera et al., 2011;

Ferrandis et al., 2013; Gonc¸alves et al., 2007).

The algorithms proposed by (Lozano et al., 2014)

take as input a set of target concepts, which are se-

lected by the user, and extracts the minimal ontol-

ogy view that does not violate the philosophical and

cognitive constraints that should be followed by on-

tologies and that are captured by the meta-properties.

This approach was proposed for allowing a consistent

reduction of an ontology, in order to provide to the

users only the fragments of the ontology that are rele-

vant to the task at hand.

4 RockQuery DESIGN

RockQuery interaction design is based on the applica-

tion of well-founded ontology views (WFOVs) in the

query formulation process. The adoption of WFOV

helps users to focus in the speciﬁc part of the whole

ontology in that they are interested in.

The interface of RockQuery was designed consid-

ering the following three main components:

The Exploration Panel. it is located on the left side

of the interface. It provides several alternatives for

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

Figure 5: Exploration Panel.

Figure 6: Processing Panel.

the user to analyze the ontology. It can be viewed

in Figure 5.

The Processing Panel. it provides the selection of

the desired instances. It is presented in Figure 6.

The Analysis Panel. it lets the user visualize the re-

sults using different kinds of graphs. It can be

viewed in Figure 7.

The following subsections provide a more detailed

description of each component.

4.1 Exploration Panel

The exploration panel (see Figure 5) consists of three

Figure 7: Analysis Panel.

widgets. The ﬁrst one is a text box where the user

can enter a term that she/he is searching. While the

user writes the term, an autocomplete function sug-

gests possible related terms. Below this widget, it

is shown the ontology visualization module, which is

described below, and a Recent Queries widget, which

contains the last queries performed by the user.

The ontology visualization panel, on its turn,

presents the ontology in a graph diagram, which sup-

ports zooming and panning. Moreover, a text box al-

lows user to enter terms that she/he is looking for in

the ontology, for the query formulation.

The main feature of the exploration panel is the

application of WFOV in the ontology visualization

panel. The WFOVs are applied both statically and

dynamically.

In RockQuery, during the registration of users in

the system, they are assigned to the speciﬁc ﬁelds of

Geology (such as Stratigraphy, Sedimentology, etc)

that can be considered as their communities of in-

terest. When the user logs in, the system extracts

a WFOV that includes the set of concepts and rela-

tions that are typical to the community to which the

user belongs. This WFOV is generated from a set of

key terms previously selected by the ontology engi-

neer and that are conﬁgured in the system database as

representative terms of some speciﬁc geological com-

munity. Thus, in the ﬁrst experience of the user with

the system, she/he will interact with a WFOV that bet-

RockQuery-AnOntology-basedDataQueryingTool

ter ﬁts to her/his proﬁle. In this case, the WFOV are

used in a static way. It is important to notice that the

VQS also allows the user to switch the ontology vi-

sualization for presenting the whole ontology or the

views of other communities.

On the other hand, the WFOV can be dynamically

generated from terms provided by the user in her/his

queries or interaction with the ontology visualization.

Speciﬁcally, when the user enters a term in the text

box or selects nodes of the ontology, the system gen-

erates a WFOV from the base-ontology, considering

the selected terms. At the end of this process, the re-

sulting WFOV is presented to the user. This resulting

WFOV can help the user in identifying additional rel-

evant concepts for the query formulation. The gen-

eration of WFOV is performed using the algorithms

proposed by (Lozano et al., 2014).

Notice that all of the operations that can be per-

formed over the ontology can also be performed over

the WFOV. The adoption of WFOV allows the VQS

to comply with the Nielsen’s Principle of Recognition

rather than recall. According to this principle, it is

preferable to present in the interface only the knowl-

edge that is required by the user for a given task.

4.2 Processing Panel

The processing panel (see Figure 6) consists of two

sections, a text box and a list box that displays the

instances of the selected concept. The user can se-

lect multiple instances and, during the selection pro-

cess, the query visualizer section adds the selected in-

stances to the tree box. The user can delete instances

in the tree box, updating the tabular data shown in the

analysis panel.

4.3 Analysis Panel

The analysis panel (see Figure 7) consists of two dis-

plays. One is the tabular display, and the other is

the data graph. During the sessions of interview with

users and of prototype testing, we identiﬁed the neces-

sity of having an analysis panel integrated in the same

query interface. This feature is crucial in the analysis

that is carried out by the user. Currently, RockQuery

provides ternary plot and scatter plot. In the future,

other data visualizations will be included in order to

help geologist to visualize the spatial data.

4.4 Application of RockQuery in a Case

Study

The application of RockQuery is illustrated through

a scenario of exploring and consulting the follow-

ing query: retrieve sample descriptions that include

blocky dolomite. This scenario shows the interactions

and features of RockQuery, and highlights how the

ontology and a data visualization support sensemak-

ing of petroleum data.

The ﬁrst screen that is presented to the user is the

displays the well founded ontology view (in the form

of a graph of concepts and relations) corresponding

to the user’s community generated by the key terms

of the community of which the user belongs. This on-

tology view is extracted from the base ontology, us-

ing the approach proposed by (Lozano et al., 2014).

Furthermore, when the user is navigating in the ontol-

ogy visualization widget, she/he can activate a popup

menu for switching the visualization to the whole on-

tology or to WFOVs of other communities. Notice

that these WFOVs are statically generated from terms

selected by the ontology engineer as representatives

of their respective communities. Besides that, the

user can dynamically generate others WFOV during

the interaction with the system. Thus, WFOVs can be

reﬁned, since new WFOVs can be generated from a

given WFOV, when the user selects additional terms

in it.

In this interface, the user can analyze and navi-

gate through the ontology graph for formulating the

query. When the user selects a node of the ontol-

ogy graph, the system lists all the instances of the

selected concept. If the user clicks twice on the in-

stance (item of the listbox), the instance is added to

the query visualizer. The user can remove a concept

or an instance from the query visualizer by selecting

it and pressing the delete key. In the interface, there

are two buttons that allow hiding the panels. This in-

creases the space available for navigating or for vi-

sualizating data. When the user does not ﬁnd a con-

cept, she/he can enters a term in the search concept

box (Figure 8 (b)). The autocomplete tool suggests

concepts, according to the information provided by

the user. When the user presses enter, the algorithm

proposed by (Lozano et al., 2014) extracts a well-

founded ontology view from the ontology, taking as

input the term provided by the user. Then, the ontol-

ogy visualization displays the resulting well-founded

ontology view to the user (Figure 8 (c)). The result-

ing view can be used by the user for identifying other

concepts and relations that can be useful for the query.

When the user selects a node, the color of the se-

lected node is changed and a list of instances of that

concept appears in the processing panel (Figure 8 (d)).

If the concept has a huge volume of instances, the

user can enter a term in the text box of the process-

ing panel for ﬁltering the data. In each iteration, the

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

treeviewer also adds the term in the query visualizer

section. When the user selects one of the items of

the listbox of the processing panel, the visualization

is presented (Figure 8 (e)). The query formulation is

an iterative process of selecting a concept and its re-

spective instance. This involves the use of the explo-

ration and processing panel. The feature of text ﬁlters

in both panels helps in ﬁnding the desired term. At

the end of the process, the user can save its query.

Following this workﬂow, the user can iteratively

construct its query, reﬁne it and visualize the result-

ing data. The user’s landscape of the areas related to

sensemaking is shown in Figure 8. Typically, tabu-

lar data is used to show the query results, while the

analysis, on the other hand, is performed in another

window. One of the RockQuery’s main difference is

that in a single interface, the user can visualize the

tabular data and analyze its visualization.

5 EVALUATION OF OUR

APPROACH

In this work, we evaluated the impact of using well-

founded ontology views in the performance of the

users for formulating queries. In order to carry out

this evaluation, we compared the times taken by the

users for formulating a set of queries. We considered

4 users, which were divided in two groups. For the

group 1, the RockQuery system used well-founded

ontology views for presenting the ontology to the

users. For the group 2, the RockQuery presented the

whole ontology for users during the query formulation

process. Considering the lack of availability of users

that have a geologic background and due to their time

constraints, we considered a time limit of 5 minutes

for formulating each query.

In our evaluation, we considered 4 users. The user

1 (U1), 2 (U2), 3 (U3) are master’s students in Geol-

ogy; U1 is specialized in carbonatic rocks; U2 and U3

are specialized in siliciclastic rocks. The user 4 (U4)

is expert in Stratigraphy. The users have previous ex-

perience with a VQS. U1 and U3 are 22 years old and

they can be considered as frequent expert users. U2

is 26 years old and is also a non-frequent user. He

does not use VQS often, but he has experience in the

domain. U4 is 43 years old and can be considered a

non-frequent normal user, since she is not an expert

in the domain of Diagenesis. In summary, they are all

petroleum geologists, with different ages, levels of ex-

perience and specializations. Most of them have basic

notions in Computer Science. Table 2 summarizes the

user characterization described above.

In our evaluation procedure, users had to use the

Figure 8: Interaction. a) RockQuery’s interface, initial

user’s landscape. b-e) Firstly, the user enters a term in the

search concept box. Then, user ﬁnds the concept and selects

it; the color of the selected node is changed in the visualiza-

tion and the list of instances of that concept appears in the

processing panel. At the same time, the tabular data with a

respective a visualization appears in the analysis panel.

Table 2: Users Characterization.

Age Expert Specialization Sex

U1 22 Yes Geologist Male

U2 26 No Geologist Male

U3 22 Yes Geologist Female

U4 43 No Stratigraphy Female

RockQuery for performing some queries. The follow-

ing queries were used in the test:

P1. Select the thin sections of Campos basin.

P2. Select the thin sections with the constituents

quartz, zeolite and sillimanite, which are localized

in the framework or in burrow pore.

P3. Select the thin sections that contain diagenetic

constituents with blocky habit.

RockQuery-AnOntology-basedDataQueryingTool

For formulating these queries, the user had to se-

lect a different number of concepts with its instances.

For instance, in query P1 the user is asked to select

one concept and one instance. In order to do that,

the user should use the text box for ﬁltering instances,

because the concept basin has a huge volume of in-

stances. In the query P2, the user need to select one

concept (constituent) related with another (pore). In

the query P3, it is tested if the user is able to select

one attribute (blocky habit) of a concept (constituent).

Results are presented in Table 3. U1 and U4 be-

long to the group 2. U2 and U3 belong to the group 1.

At the beginning, both users U1 and U4 were lost in

the huge amount of concepts that were presented. As

a result the time limit of 5 minutes was reached. Then

U1 understood the way of interact with the ontology

graph and was able of performing the other queries.

U4 was able of performing the query P1; however, in

the remaining queries the time limit was reached. U2

performed all the queries in less time than the other

users, regardless the fact that he is a non-frequent user

of a VQS. U3 also performed the queries in a reason-

able time.

The resuls demonstrated that, in average, the

adoption of ontology view reduces the time of per-

forming long queries such as P2, which involves more

than one concept. This suggests that the adoption of

ontology views have a positive impact, by reducing

the information overload and increasing the produc-

tivity.

Table 3: The times (in seconds) taken by the users for per-

forming a query with ontology views (group 1) and without

it (group 2). In this table, (>) means that the time limit of 5

minutes was reached.

Query

Group 1 Group 2

U2 U3 U1 U4

P1 14:13 30:01 > 30:08

P2 74:12 110:88 299:00 >

P3 18:36 17:41 72:84 >

According to the results of the evaluation process,

when the system applies ontology views, the users

ﬁnd the required concepts and perform the query in

less time than when the system presents the whole

ontology. This suggests that RockQuery reduces the

overload of information, allowing the user to focus on

what she/he needs for performing the query. This re-

sults in a time reduction for performing the query.

6 CONCLUSIONS

In this paper, we propose an approach that com-

bines HCI concepts and methods with ontology views

for improving the interaction of users with ontology-

based visual query systems, by avoiding the informa-

tion overload. In our approach, ontology views are

applied for identifying only the knowledge that is rel-

evant for the user in a given task. We apply this ap-

proach for developing RockQuery, a visual query sys-

tem for consulting petrographic data. In this system,

our approach helps the user to learn by exploration

of the ontology graph. The validation demonstrated

that the application of ontology view in RockQuery

implies a reduction of time in performing queries.

ACKNOWLEDGEMENTS

We gratefully thank Brazilian Research Council,

CNPq, PRH PB-17 program (supported by Petro-

bras), and Endeeper Co, for the ﬁnancial support to

this work.

REFERENCES

Athanasis, N., Christophides, V., and Kotzinos, D. (2004).

Generating on the ﬂy queries for the semantic web:

The ics-forth graphical rql interface (grql). In The Se-

mantic Web ISWC 2004, pages 486–501.

Carbonera, J. L., Abel, M., and Scherer, C. M. (2015). Vi-

sual interpretation of events in petroleum exploration:

An approach supported by well-founded ontologies.

Expert Systems with Applications, 42(5):2749–2763.

Carbonera, J. L., Abel, M., Scherer, C. M., and Bernardes,

A. K. (2011). Reasoning over visual knowledge. In

ONTOBRAS-MOST, pages 49–60. Citeseer.

Carbonera, J. L., Abel, M., Scherer, C. M., and Bernardes,

A. K. (2013). Visual interpretation of events in

petroleum geology. In Tools with Artiﬁcial Intelli-

gence (ICTAI), 2013 IEEE 25th International Confer-

ence on, pages 189–194. IEEE.

Castro, E., Mastella, L., Abel, M., and DeRos, L. (2005).

Petroquery: a tool for consultation and navigation

over ontology. Regional School of Database, Brazil.

de Alencar, A. L. and Salgado, A. C. (2013). A visual

query interface for ontology-based peer data manage-

ment systems. Brazilian Simposium of Information

Systems.

Ferrandis, A. M. M., L

opez, O. P., and Guizzardi, G.

(2013). Applying the principles of an ontology-based

approach to a conceptual schema of human genome.

In Conceptual Modeling, pages 471–478. Springer.

Gonc¸alves, B., Guizzardi, G., and Pereira Filho, J. G.

(2007). An electrocardiogram (ecg) domain ontology.

In Workshop on Ontologies and Metamodels for Soft-

ware and Data Engineering, 2nd, Jo

ao Pessoa, Brazil,

pages 68–81.

ICEIS2015-17thInternationalConferenceonEnterpriseInformationSystems

Guizzardi, G. (2005). Ontological Foundations for Struc-

tural Conceptual Models. PhD thesis, University of

Twente, The Netherlands.

Ho, J. and Tang, R. (2001). Towards an optimal reso-

lution to information overload: an infomediary ap-

proach. In Proceedings of the 2001 International ACM

SIGGROUP Conference on Supporting Group Work,

pages 91–96. ACM.

Karr-Wisniewski, P. and Lu, Y. (2010). When more is too

much: Operationalizing technology overload and ex-

ploring its impact on knowledge worker productivity.

Computers in Human Behavior, 26(5):1061–1072.

Keim, D., Andrienko, G., Fekete, J.-D., G

org, C., Kohlham-

mer, J., and Melanc¸on, G. (2008). Visual analytics:

Deﬁnition, process, and challenges. Springer.

Kogalovsky, M. (2012). Ontology-based data access

systems. Programming and Computer Software,

38(4):167–182.

Lozano, J., Carbonera, J. C., Abel, M., and Pimenta, M.

(2014). Ontology view extraction: an approach based

on ontological meta-properties. In Proceedings on IC-

TAI.

Nielsen, J. (1993). Usability Engineering. Interactive tech-

nologies. Morgan Kaufmann.

Soylu, A., Giese, M., Jimenez-Ruiz, E., Kharlamov, E.,

Zheleznyakov, D., and Horrocks, I. (2013). Op-

tiquevqs towards an ontology-based visual query sys-

tem for big data. In Proceedings International Con-

ference on Management of Emergent Digital EcoSys-

tems.

RockQuery-AnOntology-basedDataQueryingTool