A Directed Concept Search Environment to Visually Explore Texts

Related to User-deﬁned Concept Models

Muhammad Faisal Cheema

, Stefan J

anicke

, Judith Blumenstein

and Gerik Scheuermann

Image and Signal Processing Group, Institute for Computer Science, Leipzig University, Leipzig, Germany

Faculty of History, Arts and Oriental Studies, Leipzig University, Leipzig, Germany

Keywords:

Text Visualization, Digital Humanities, Distant Reading, Concept Search, Information Retrieval, Concept

Editor, Concept Modeling.

Abstract:

We introduce a concept search environment that caters for the needs of humanities scholars who want to im-

prove the accuracy of search results when querying historical text corpora. For this purpose, we designed a

so-called Concept Editor that allows to model historical concepts in a diagram style according to the imag-

inations of the humanities scholar. For the inspection of results determined in the proposed concept search,

we provide a Concept Search Results Viewer that uses the existent layout of the underlying concept model

to visualize related texts according to the relevance to the given concept. We further designed the overall

system the way that the humanities scholar can iteratively reﬁne the concept idea, which leads to a gradual

improvement of search results. To illustrate the whole development pipeline, we provide a usage scenario

on modeling the concept epilepsy with the purpose of improving the accuracy of results compared to usual

applied keyword-based search methods.

1 INTRODUCTION

The retrieval of information utilizing search engines

such as Google is a daily activity nowadays. When

applying the usual keyword-based search, the results

are often unsatisfactory as they contain too less pre-

cise hits and numerous irrelevant results. The same

issue occurs for humanities scholars applying key-

word searches on historical text corpora accessible via

platforms like Perseus Digital Library

or PHI Latin

Texts

to discover texts related to a speciﬁc topic.

Then, the scholar needs to reformulate the query hop-

ing to improve the quality and quantity of search re-

sults.

Taking the search for text passages related to the

concept epilepsy as an example, a traditional key-

word search for the corresponding Latin term mor-

bus comitialis (disease of the assembly) yields in-

complete results. In contrast, a truncated search for

morb* comiti* returns more but less accurate results.

Also, false positives in the form of entirely unrelated

text passages occur, e.g., a passage from a politi-

cal text written by the Roman historian Titus Livius

http://www.perseus.tufts.edu/

http://latin.packhum.org/

(Ab urbe condita libri 41,18,16) containing both the

words morbus and comitia. Also problematic with

basic keyword-based search attempts is the impossi-

bility to receive texts paraphrasing the given concept.

In the epilepsy scenario, related texts exist that do not

use the term morbus comitialis explicitly. An exam-

ple is the didactic poem De rerum natura written by

the Roman poet and philosopher Lucretius, where the

disease is paraphrased with occurring symptoms such

as concidere (collapse) and spuma (foam). As many

other terms were used to describe epilepsy in Latin

texts, e.g., morbus sacer or epilepsia, keyword-based

approaches were inadequate for the humanities schol-

ars to discover unknown but related results.

This paper presents a system designed to im-

prove the search capabilities on historical text cor-

pora. Based upon a semi-automated process, we

aim to extract more accurate results by extending

the traditional keyword search with a so-called con-

cept search. Exemplary, we will present all impor-

tant steps from modeling the concept epilepsy, per-

forming a concept search to discover texts related to

epilepsy, analyzing the search results, and modifying

the concept iteratively. In summary, the contributions

of our work to the visualization and Digital Humani-

ties communities are:

Cheema, M., Jänicke, S., Blumenstein, J. and Scheuermann, G.

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models.

DOI: 10.5220/0005727400720083

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 2: IVAPP, pages 74-85

ISBN: 978-989-758-175-5

• A Concept Editor that borrows ideas from con-

cept and mind mapping to allow humanities schol-

ars to model concepts according to their imagina-

tions.

• A Concept Search algorithm that retrieves all

texts related to the modeled concept and clusters

them in the concept model according to their rel-

evance to the given concept.

• A Concept Search Results Viewer that intu-

itively visualizes the results of the concept search

by interactively providing statistics of the occur-

ring terms with the help of TagPies.

• A user-driven Concept Search Environment that

gives the humanities scholar full control when

creating concepts, analyzing search results, and

the opportunity to gradually improve search re-

sults by iteratively modifying the underlying con-

cept model.

Additionally, we provide insights about the col-

laborative, iterative development of the concept

search environment, a usage scenario that illustrates

the potential of the presented system, and possible fu-

ture tasks.

2 RELATED WORK

The prior goal of our work is the development of a

Concept Search environment that improves the hu-

manities scholar’s search capabilities on ancient text

corpora while controlling the entire Concept Search

process. To solve this task, we draw upon exist-

ing techniques in the ﬁelds of diagramming, natu-

ral language processing and visualization. Many dia-

gramming tools have been developed to cater various

needs, and similarly, there are several ways to ﬁnd text

passages related to a given topic and to visualize the

search results.

2.1 Existing Concept Search Methods

Besides traditional keyword-based search methods to

collect related text passages in large corpora, other

techniques have evolved in past years that take advan-

tage of the semantic and statistical information inher-

ent in the texts with the goal to improve precision and

recall of the results.

Concept search (Giunchiglia et al., 2009) is such a

method that automatically retrieves data which is con-

ceptually similar to a user-speciﬁc search query. By

employing techniques like word sense disambigua-

tion (Navigli, 2009), the result sets compared to a

Figure 1: Example concept map.

traditional keyword search can be improved by tak-

ing not only the spelling of a word but also its mean-

ing into account during the search process. Similar

approaches take advantage of different semantic rela-

tions within the text, e.g., synonyms and word varia-

tions (Guha et al., 2003), or search processes can be

based upon an underlying ontology (Fern

andez et al.,

2011). In contrast to considering semantic relations,

topic modeling approaches apply statistical models

to measure the conceptual similarity of texts (Wal-

lach, 2006). The Latent Dirichlet allocation (LDA)

is the most often used topic model (Blei et al., 2003).

Within the computation process, each text is seen as a

mixture of latent topics. Based on a predeﬁned num-

ber of ﬁnal topics, texts cluster iteratively according

to the similarity of contained words.

All the above mentioned techniques suffer from

one major disadvantage: the user has a very lim-

ited control over the detection of conceptual similari-

ties, which are based on a prior semantic or statistical

model. Only scholars having advanced knowledge of

information retrieval can tweak the parameters to ob-

tain better results. In contrast to these automated pro-

cesses that use existing semantic knowledge from the-

sauri, topic maps or semantic networks, our concept

modeling approach allows the humanities scholar to

express her understanding of a concept without the

need of any existing semantic or statistical knowl-

edge. Teevan (Teevan et al., 2004) argues that even

with a perfect search engine, a poorly constructed

search question may not lead to the right answer. So,

the user needs to be provided with a directed search

system. As semantic and statistical models would

take the control away from the humanities scholar, we

designed a directed search environment. This capa-

bility of retaining the control over the modeling and

search process was an important requirement in our

project as it supports the iterative reﬁnement of mod-

els to steadily retrieve better results.

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models

Figure 2: Example mind map (Kanter,

2015) (Figure under CC BY 2.0 license, see

https://creativecommons.org/licenses/by/2.0/ for details).

2.2 Diagram Types and Diagramming

Tools

According to Ware, half of the brain is trained in inter-

preting graphical patterns (Ware, 2010). Thus, visual

thinking tools such as diagrams utilize this capability

in helping humans to comprehend the visualized facts

faster.

There are several ways to transform an idea or a

concept into a diagram style. One such method is

concept maps (Hager et al., 1997) that illustrate the

relationships between multiple concepts. Shapes are

used to model ideas, images, or words as concepts,

and arrows among these shapes communicate rela-

tions; an example is given in Figure 1. In software

engineering, the idea of concept mapping is used to

visualize the design of a system in form of a diagram

with the Uniﬁed Modeling Language (UML) (Booch

et al., 2005).

A similar method to visually organize information

are so called mind maps (Budd, 2004). In contrast to

concept maps, a mind map represents thoughts and

ideas about a central concept (see Figure 2). The

concepts generated by the collaborating humanities

scholars of our projects borrow from both diagram

types, as they focus a central concept that is some-

times connected to correlated sub-concepts.

Several freely or proprietary diagramming tools

such as Inkscape (Bah, 2009), MSVisio

or yEd

en-

able scholars to create diagrams. Most of these tools

are used to only represent and organize information,

which is insufﬁcient for our needs. The Concept Edi-

tor proposed in this paper enables the scholar not only

http://www.visiotoolbox.com/

http://www.yworks.com/en/products/yﬁles/yed/

to model particular concepts, but also to manually at-

tach semantic information for further processing.

2.3 Concept Search Visualizations in

the Digital Humanities

Many works with a Digital Humanities motivation at-

tend to the matter of visualizing the results of Concept

Search processes or comparable methods.

The VarifocalReader, which facilitates the work

with individual, potentially large historical texts, ap-

plies a topic segmentation to cluster text parts accord-

ing to the user’s requirements, e.g., to extract and

highlight sections dealing with certain topics (Koch

et al., 2014).

Topic modeling is an often used basis for the anal-

ysis of topic changes within news corpora (Cui et al.,

2011; Dou et al., 2012). After automatic topic extrac-

tion, the temporal evolution of the found topics can

be observed in stream graphs. To minimize the com-

putational effort, the user can be integrated into this

process by picking topics of interest (Cui et al., 2014).

A rather abstract method to visualize topic changes in

news corpora is the dust-and-magnet visualization (Yi

et al., 2005). Applied by Eisenstein, topics exert mag-

netic forces on various historical newspapers that gen-

erate temporal trajectories in the form of “dust trails”

to illustrate topic change (Eisenstein et al., 2014).

Other approaches apply automated topic model-

ing techniques to cluster texts in large text collections.

The tool Serendip uses probabilistic topic models to

support the semi-automated exploration of such cor-

pora (Alexander et al., 2014). The words belonging to

speciﬁc topics can be analyzed both in close and dis-

tant reading views. That juxtaposed tag clouds are

beneﬁcial to explore automatically extracted topics

from various text corpora has been shown at the re-

cent Digital Humanities conference (J

ahnichen et al.,

2015; Montague et al., 2015). Also, the utilization

of graphs to communicate the relationships among

discovered topics has been proven useful (Kaufman,

2015).

3 DIGITAL HUMANITIES

BACKGROUND

As a consequence of many digitization projects, hu-

manities scholars nowadays have access to large

historical text corpora. Within the digital hu-

manities project eXChange

computer scientists and

http://www.exchange-projekt.de/

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

humanities scholars collaboratively develop strate-

gies to query such text corpora. The eXChange

database contains numerous ancient Greek and Latin

texts from various sources (e.g., Perseus Digital

Library (Per, 2015) and Bibliotheca Teubneriana

Latina (Btl, 2015)).

The usual approach of humanities scholars query-

ing a digital corpus is a keyword based search. Quite

often, they get lots of results, which are hard to re-

vise, so that the generation of valuable hypotheses

is nearly impossible. Moreover, a simple keyword

search causes incomplete result sets with partially in-

accurate search results as it does not include word

forms or synonyms. Especially when observing an-

cient terms that were used in different domains – pol-

itics and medicine – the humanities scholars require a

mechanism that ﬁlters for results relevant for the re-

search question at hand. Therefore, the prior goal of

the project was to develop a system capable of extract-

ing more accurate results by extending the traditional

keyword search with a so called concept search.

Following the suggestions from other visualiza-

tion researchers who worked together with humani-

ties scholars (J

anicke et al., 2015b), we closely col-

laborated within the eXChange project when design-

ing the concept search system. Initially, we discussed

the needs of humanities scholars of our project, their

workﬂows, research questions and challenges in sev-

eral meetings. As a concept is a broad term used

with different meanings in a variety of ﬁelds (Mar-

golis and Laurence, 2014), one of the ﬁrst steps was

to deﬁne the concept term with respect to our context.

The concepts generated by the humanities scholars in

our project borrow ideas from mind maps as well as

concept maps, as they focus on a central idea that is

sometimes related to other (sub-)concepts. Overall, a

concept is deﬁned through a bunch of terms and their

word forms, which distribute over (sub-)concepts. A

prior concern of the scholars was to have a fully con-

trollable search process. To meet this need, we de-

signed the visualizations in a way that each data trans-

formation step is comprehensible. A Concept Edi-

tor was proposed, where the scholars can model their

ideas of concepts. Based on these user-deﬁned con-

cept models, we developed a concept search and de-

signed a Concept Search Results Viewer. The de-

velopment of the whole concept search system is ex-

plained in the following section.

4 CONCEPT SEARCH PIPELINE

For the sake of simplicity and better understanding,

a concept is modeled in form of a tree. Related sub-

Figure 3: Concept Search Pipeline.

concepts and speciﬁc terms and word forms are com-

bined in a hierarchical fashion to the central concept,

which is represented with the root node of the tree.

Internal nodes represent sub-concepts, and leaf nodes

are words deﬁning the associated (sub-)concepts. An

example model for the concept epilepsy is given in

Figure 4.

As illustrated in Figure 3, the approach primarily

consists of three steps. At ﬁrst, the scholar generates

a concept model using Concept Editor. The resultant

concept model is then passed on to the proposed Con-

cept Search that ranks the search results, which are

retrieved related texts, and places them into the con-

cept model. Finally, the search results are arranged in

the Concept Search Results Viewer, which aggregates

search results and serves details on demand by inter-

actively visualizing statistics about occurring terms

related to the underlying concept model in tag clouds.

4.1 Concept Editor

The Concept Editor is an interface for humanities

scholars to encode their ideas of concepts on the

screen. Figure 4 shows a screenshot of the Concept

Editor containing the concept epilepsy. Rectangular

nodes represent (sub-)concepts while oval nodes de-

note concrete terms. Nodes can be drawn via drag and

drop and the scholar can structure the concept accord-

ing to her imagination by connecting related nodes.

The node colors indicate whether a sub-concept and

its associated terms are supportive (in green) or con-

tradictory (in red) to the central concept.

In the example concept model for epilepsy, the

root node epilepsy is the central concept in blue

color. The green colored shapes indicate support-

ive sub-concepts and terms (e.g., labels for epilepsy),

whereas red shapes indicate contradictions (e.g., po-

litical terms unrelated to epilepsy). Darker shades

(e.g., morbus, febrire etc.) represent deﬁnite knowl-

edge of the scholars, whereas light shades (e.g.,

vinium, cotidiana etc.) represent assumptions. Ac-

cording to the model, texts containing the terms

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models

Figure 4: Concept Editor interface showing the concept epilepsy.

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Figure 5: Word form selection interface showing the term

quartana.

morbus or comitialis were most likely used to address

the concept epilepsy. Terms like horror (dread) and

quartana (fever) show contradiction.

Each term is connected to all possible spellings

and word-forms contained in the database. A popup

on demand provides a list and a tag cloud that allow

the scholar to observe and select word forms inher-

ent in the corpus, which are potentially relevant for

the corresponding concept. Figure 5 shows the se-

lected word forms for the term quartana. As fever is

a female term in Latin, the male form of the adjective

quartanus is excluded in that scenario. After the con-

cept is built, it is stored for persistence and forwarded

to a Concept Search module.

4.2 Concept Search

The Concept Search method is a two-stage process.

First, a hierarchical keyword search is performed on

the basis of the observed concept model. Second,

each search result is assigned to the topically best ﬁt-

ting node in the concept tree.

4.2.1 Hierarchical Keyword Search

The selected word-forms of all terms are the basis for

the search. The procedure of the keyword hierarchy

search was intentionally kept as simple as possible to

minimize pre-assumptions on how a ﬁtting concept

search would function.

Beginning with a list of all words that have been

added to the concept, each entry is searched for with a

classical string lookup on a normalized version of the

texts. Per word, all occurrences are located, grouped

with respect to the containing text and then counted

within the groups. Those individually found texts are

collected across all words. This forms a non-negative

(and usually quite sparse) integer matrix where every

row-sum and every column-sum has a value of at least

1. This representation can then be used to rank the re-

sults according to the concept hierarchy. Each occur-

ring “positive term” (green and light green) increases

the rank of a text, whereas each occurring “negative

term” (red and light red) decreases it.

4.2.2 Assigning the Results to Concept-Nodes

According to their relationship to the underlying con-

cept, the results – texts within the database – are po-

sitioned in the concept hierarchy. Instead of showing

only a list of search results, we put all results in the

concept hierarchy the scholar is familiar with. This

helps to get a notion of a text’s relevance to the con-

cept.

The assignment of a search result to a concept

node is performed as follows. Initially, the search

result is assigned to each positive leaf concept node

(green colored) for which the corresponding text con-

tains one of the concept’s “positive terms.” We do not

assign a search result to negative leaf concepts (red

colored) as they are unimportant matches for the con-

cept. Then, the concept tree is traversed bottom-up in

depth ﬁrst search fashion. A search result is moved

up the tree and placed in the parent concept node if it

meets two criteria: (1) all positive child concept nodes

contain the search result, and (2) the ratio of posi-

tive to negative words is greater than a conﬁgurable

threshold. A result fulﬁlling both criteria in all traver-

sal steps will be moved to the root concept node. If

this is not the case, a search result may be placed in

multiple concept nodes. We remove duplicate entries

and keep the best ﬁtting position for a search result,

which is the concept node with the closest distance to

the root node. If there are multiple candidates with the

same distance, we keep the position where the search

result obtains the highest ratio of positive to negative

terms. So, each search result is assigned to its best ﬁt-

ting concept node. The higher a search result is placed

in the hierarchy, the closer it is considered to the con-

cept.

4.3 Concept Search Results Viewer

The Concept Search Results Viewer supports the

analysis of the concept-related retrieved texts in two

views. The ﬁrst view shows the aggregated results

in the concept hierarchy. In the second view, the re-

lationships between individual texts and word forms

can be interactively explored. To preserve the men-

tal map, both views are derived from the user-deﬁned

concept layout and need to be recalculated every time

the user changes the underlying concept.

4.3.1 Layout Calculation

After the search results are assigned to the concept

nodes (see Subsection 4.2.2 ), the node sizes increase

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models

Figure 6: Concept Search Results Viewer showing the aggregate results view of the concept epilepsy.

with respect to the number of labels that need to be

shown. To keep the results layout consistent to the

user-deﬁned concept layout, all node sizes are equal

and remain ﬁxed (large result sets can be inspected via

scrolling). The increase of node sizes creates overlaps

between nodes. To remove these overlaps the lay-

out needs to be reﬁned while preserving the original

topology of the concept tree.

The overlap problem is solved heuristically. Ini-

tially, the original coordinate positions are assigned

according to the concept tree. Then, the concept tree

is traversed bottom up. For each node, we check if

an overlap exists with its parent node. If an over-

lap exists, the length of the edge between parent and

child node is increased by moving the child node till

the overlap resolves. This step is ﬁrst done for all

leaf concept nodes to remove overlaps with the corre-

sponding parent nodes. When applying this heuris-

tic to internal nodes, we take the bounding box of

the whole sub-tree into account. This ensures that

no overlap exists between a node’s sibling and it’s

children. After all the nodes are traversed, we obtain

a layout that preserves the topology of user-deﬁned

concept tree layout.

4.3.2 Visualizing Aggregate Result Summaries

In this view, all retrieved texts are shown in their cor-

responding node containers with a label – author and

title of the text – in a font size reﬂecting the deter-

mined rank during the concept search. The results

assigned to a node are ordered by decreasing rank, so

that the most relevant text labels are shown at the top

of lists. The aggregate result summaries view for the

epilepsy scenario is shown in Figure 6.

As this is the ﬁrst view on the results of the con-

cept search, the humanities scholar can get an idea

how the texts are placed in the concept hierarchy and

which texts seem to be most relevant to the concept.

4.3.3 Visualizing Word Summaries of Individual

Texts

To support the exploration process of the concept

search results, we provide a detailed view showing

the relationships between a partial result set (texts of

an individual node) and the occurring word forms in

these texts. To save screen space, we collapse all other

nodes except the selected one. This helps users to

concentrate on the task at hand.

The word forms occurring in the associated texts

are shown as TagPies (J

anicke et al., 2015a), which

provide an intuitive, comparative view of the term dis-

tribution of a concept node. We selected TagPies as

the humanities scholars are used to work with this vi-

sualization within the eXChange project, and it turned

out to be valuable for their workﬂows (J

anicke et al.,

2015a). Figure 8 shows a TagPie of word forms of

the terms spuma, trepidatio, concidere, vertere and

tremere, which are assigned to the what happens?

sub-concept in epilepsy symptoms.

When the user hovers a text label in the node con-

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

Figure 7: Concept Search Results Viewer showing the word forms occurring in texts belonging to the selected node. The

terms of a selected text are highlighted.

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models

Figure 8: TagPies belonging to the subconcept what hap-

pens? that is related to epilepsy symptoms.

tainer, all associated word forms are highlighted to

give the user an idea of the topical context of the

selected text in accordance to the given concept (as

shown in Figure 7).

5 DISCUSSION

When developing the concept search environment, we

closely collaborated with the humanities scholars –

historians and classical philologists – involved in the

digital humanities project eXChange. This section

provides a typical usage scenario and takes a look at

the iterative development and evaluation of the sys-

tem.

5.1 Usage Scenario

The leading example of this paper – working with

the ancient concept epilepsy – was an object of in-

terest for one of the collaborating classical philolo-

gists. She modeled the concept using the provided

editor as shown in Figure 4. Looking at the results

of the concept search (see Figure 6), she observed the

discovered texts assigned to the various nodes. Click-

ing the main concept node for epilepsy reveals the

related terms by displaying the found word forms in

the corresponding TagPies. In that scenario, she was

scrolling through the results and used the highlight-

ing functionality to get a quick overview of the terms

contained in various historical texts (see Figure 7).

Thereby, she discovered that the frequent occurrence

of the term vitium (defect) is related to a large num-

Figure 9: Concept Niederlage (defeat) modeled in the Con-

cept Editor.

ber of texts written by Seneca the Younger (see Fig-

ure 10(a)), a philosopher of the ﬁrst century AD who

wrote about ethics. Close reading underpins the ex-

pectation that Seneca the Younger used the term vi-

tium in a moral sense. When modeling the concept,

she put the term to the subconcept epilepsy labels

(see Figure 10(b)) as the archaic author Plautus used

it in a physical sense with the intention to discover

similar texts. But the frequent usage in moral con-

texts distorts the intentional meaning of the concept

epilepsy. The iterative design of the concept search

environment allows her to modify the epilepsy con-

cept by removing the term vitium from the positive

subconcept epilepsy labels to a new, negative subcon-

cept moral terms (see Figure 10(c)). After re-running

the concept search based on the improved concept

(see Figure 10(d)), the false positives do not appear

in the main concept node anymore and she can focus

on potentially more related texts.

5.2 Evaluation and Feedback

We integrated the humanities scholars of our project

at each development stage, on the one hand, to com-

municate the functionality of the system, on the other

hand, to ensure designing a usable and comprehen-

sible tool. Seven humanities scholars (6 female, 1

male) working in the project participated in ﬁve work-

shops that we organized to iteratively evaluate system

design as well the current prototypes. The ﬁrst two

workshops were focused on the design of the concept

modeling and concept search. In the later workshops

the scholars were able to play with the prototypes,

pose questions when issues occurred, and suggest us-

ability as well as conceptual improvements of the sys-

tem design. Also, we collected general impressions of

the system in interviews at the end of the workshops.

Besides the workshops, there were several meetings

held to evaluate and discuss the results of the con-

cept search system. Another example thematizing the

ancient concept Niederlage (defeat) modeled during

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

(a) False search results due to positive term vitium. (b) vitium marked as positive in the epilepsy concept.

updated epilepsy concept.

(d) Missing false positive search results due to relocation of

vitium.

Figure 10: Use case showing how iterative modeling and searching improve the concept model and search results.

these sessions is shown in Figure 9, and the results

are presented in Figure 11.

Overall, the humanities scholars liked that the vi-

sualization system stepwise turned away from being

“just a working black box.” The major reason for

this statement was that the user retains full control of

the concept creation and search process. Especially,

the iterative modiﬁcation of concepts, which supports

the user to “ﬁnally build a good concept,” was seen

very powerful. Within the above outlined usage sce-

nario, the humanities scholar stated that the “iterative

process shows, how the imagination of the epilepsy

concept becomes more and more precise to the hu-

manist.” Often mentioned was the capability of the

system to support the humanities scholar in detect-

ing conceptual errors when modeling concepts. The

results shown in the Concept Search Results Viewer

lead to “rethinking the concept model and to apply

some changes.” The combinatorial display of text ti-

tles, authors and corresponding terms was seen im-

portant when analyzing the results. Especially, the

integration of TagPies helps to receive an immediate

picture of the distribution of the terms and word forms

the underlying concept is built of.

6 CONCLUSION AND FUTURE

WORK

In this paper, we presented a novel concept search en-

vironment to be used by humanities scholars to dis-

cover historical texts containing concepts modeled ac-

cording to their ideas. The system design allows for

an iterative modiﬁcation of the humanities scholar’s

concept, so that a gradual improvement of results

is possible. Although the system is particularly de-

signed for dealing with ancient texts, we designed it

the way that it is applicable to other domains using

different text genres.

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models

Figure 11: Search results for concept Niederlage shown in the Concept Search Results Viewer.

For the collaborating humanities scholars, the

concept search system is valuable at current stage, but

several tasks to improve the search capabilities ex-

ist. While quite ﬁt for the visualization purpose, a

simple counting of word occurrences does of course

not yet constitute a reasonable ranking for result

texts in the sense of Information Retrieval. Such a

method should eventually take some form of statis-

tical signiﬁcance of word occurrences into account

and perhaps use globally-normalized measures like

TF/IDF (Baeza-Yates et al., 1999) or related rank-

ing methods such as Okapi BM25 (Robertson et al.,

1995). Even more important, it should somehow ﬁnd

a way to regard “negated” sub-concepts (and their -

possibly also negated - (sub-)concepts) as intended

by the Concept Editor. This means that special ad-

ditions have to be made to tailor the ranking functions

to boost the desired combinations of occurrences ac-

cording to the concept – maybe even in an interactive

way with feedback from the visualization.

Interestingly, the usage scenario outlined in Sec-

tion 5.1 triggered a new research question for the

humanities scholar: “Was the term vitium originally

used in a physical or in a moral sense?” Although

the current system helps to generate hypotheses, the

change of concepts and their meanings over time

in dependency of authors using speciﬁc terminolo-

gies cannot be tracked, but is very interesting for the

humanities scholars. The planned extension of the

concept search environment with a faceted browsing

mechanism could support answering these types of re-

search questions.

ACKNOWLEDGEMENTS

The authors thank Thomas Efer for maintaining

the project back-end and implementing the concept

search algorithm. This research was funded by the

German Federal Ministry of Education and Research.

REFERENCES

(2015). Bibliotheca Teubneriana Latina. Walter de Gruyter.

http://www.degruyter.com/db/btl (accessed March 19,

2015).

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

(2015). Perseus Digital Library. Ed. Gregory R.

Crane. Tufts University. http://www.perseus.tufts.edu

(accessed March 19, 2015).

Alexander, E., Kohlmann, J., Valenza, R., Witmore, M., and

Gleicher, M. (2014). Serendip: Topic Model-Driven

Visual Exploration of Text Corpora. In Visual Analyt-

ics Science and Technology (VAST), 2014 IEEE Con-

ference on, pages 173–182.

Baeza-Yates, R., Ribeiro-Neto, B., et al. (1999). Modern

Information Retrieval, volume 463. ACM press New

York.

Bah, T. (2009). Inkscape: Guide to a Vector Drawing Pro-

gram (Digital Short Cut). Pearson Education.

Blei, D. M., Ng, A. Y., and Jordan, M. I. (2003). Latent

Dirichlet Allocation. the Journal of machine Learning

research, 3:993–1022.

Booch, G., Rumbaugh, J., and Jacobson, I. (2005). Uniﬁed

Modeling Language User Guide, The (2Nd Edition)

(Addison-Wesley Object Technology Series). Addison-

Wesley Professional.

Budd, J. W. (2004). Mind Maps as Classroom Exercises.

The Journal of Economic Education, 35(1):35–46.

Cui, W., Liu, S., Tan, L., Shi, C., Song, Y., Gao, Z., Qu,

H., and Tong, X. (2011). TextFlow: Towards Better

Understanding of Evolving Topics in Text. Visualiza-

tion and Computer Graphics, IEEE Transactions on,

17(12):2412–2421.

Cui, W., Liu, S., Wu, Z., and Wei, H. (2014). How Hier-

archical Topics Evolve in Large Text Corpora. Visu-

alization and Computer Graphics, IEEE Transactions

on, 20(12):2281–2290.

Dou, W., Wang, X., Skau, D., Ribarsky, W., and Zhou,

M. (2012). LeadLine: Interactive visual analysis of

text data through event identiﬁcation and exploration.

In Visual Analytics Science and Technology (VAST),

2012 IEEE Conference on, pages 93–102.

Eisenstein, J., Sun, I., and Klein, L. F. (2014). Ex-

ploratory Thematic Analysis for Historical Newspaper

Archives. In Proceedings of the Digital Humanities

2014.

Fern

andez, M., Cantador, I., L

opez, V., Vallet, D., Castells,

P., and Motta, E. (2011). Semantically enhanced in-

formation retrieval: an ontology-based approach. Web

Semantics: Science, Services and Agents on the World

Wide Web, 9(4):434–452.

Giunchiglia, F., Kharkevich, U., and Zaihrayeu, I. (2009).

Concept search. In The Semantic Web: Research and

Applications, pages 429–444. Springer.

Guha, R., McCool, R., and Miller, E. (2003). Semantic

search. In Proceedings of the 12th International Con-

ference on World Wide Web, WWW ’03, pages 700–

709, New York, NY, USA. ACM.

Hager, P. J., Scheiber, H. J., and Corbin, N. C. (1997). De-

signing & Delivering: Scientiﬁc, Technical, and Man-

agerial Presentations. John Wiley & Sons.

ahnichen, P., Oesterling, P., Liebmann, T., Heyer, G.,

Kuras, C., and Scheuermann, G. (2015). Exploratory

Search Through Interactive Visualization of Topic

Models. In Proceedings of the Digital Humanities

2015.

anicke, S., Blumenstein, J., R

ucker, M., Zeckzer, D., and

Scheuermann, G. (2015a). Visualizing the Results

of Search Queries on Ancient Text Corpora with Tag

Pies. Digital Humanities Quarterly. To appear.

anicke, S., Franzini, G., Cheema, M. F., and Scheuermann,

G. (2015b). On Close and Distant Reading in Digi-

tal Humanities: A Survey and Future Challenges. In

Borgo, R., Ganovelli, F., and Viola, I., editors, Eu-

rographics Conference on Visualization (EuroVis) -

STARs. The Eurographics Association.

Kanter, B. (2015). Cambodia4kids.org.

https://www.ﬂickr.com/photos/cambodia4kidsorg/

6195211411 (Retrieved 2015-11-25).

Kaufman, M. (2015). ’Everything on Paper Will Be Used

Against Me’: Quantifying Kissinger. In Proceedings

of the Digital Humanities 2015.

Koch, S., John, M., Worner, M., Muller, A., and Ertl, T.

(2014). VarifocalReader – In-Depth Visual Analysis

of Large Text Documents. Visualization and Com-

puter Graphics, IEEE Transactions on, 20(12):1723–

1732.

Margolis, E. and Laurence, S. (2014). Concepts. In Zalta,

E. N., editor, The Stanford Encyclopedia of Philoso-

phy. Spring 2014 edition.

Montague, J., Simpson, J., Rockwell, G., Ruecker, S., and

Brown, S. (2015). Exploring Large Datasets with

Topic Model Visualizations. In Proceedings of the

Digital Humanities 2015.

Navigli, R. (2009). Word Sense Disambiguation: A Survey.

ACM Computing Surveys (CSUR), 41(2):10.

Robertson, S. E., Walker, S., Jones, S., Hancock-Beaulieu,

M. M., Gatford, M., et al. (1995). Okapi at TREC-3.

NIST SPECIAL PUBLICATION SP, pages 109–109.

Teevan, J., Alvarado, C., Ackerman, M. S., and Karger,

D. R. (2004). The perfect search engine is not enough:

A study of orienteering behavior in directed search.

In Proceedings of the SIGCHI Conference on Human

Factors in Computing Systems, CHI ’04, pages 415–

422, New York, NY, USA. ACM.

Wallach, H. M. (2006). Topic Modeling: Beyond Bag-of-

Words. In Proceedings of the 23rd international con-

ference on Machine learning, pages 977–984. ACM.

Ware, C. (2010). Visual Thinking for Design. Morgan Kauf-

mann.

Yi, J. S., Melton, R., Stasko, J., and Jacko, J. A. (2005).

Dust & Magnet: multivariate information visualiza-

tion using a magnet metaphor. Information Visualiza-

tion, 4(4):239–256.

A Directed Concept Search Environment to Visually Explore Texts Related to User-deﬁned Concept Models