Visual Analysis and Exploration of Entity Relations in Document

Collections

Markus John

, Florian Heimerl

, Ba-Anh Vu

and Thomas Ertl

Institute for Visualization and Interactive Systems, University of Stuttgart, Stuttgart, Germany

Department of Computer Sciences, University of Wisconsin-Madison, Madison, U.S.A.

Keywords:

Exploratory Visual Text Analytics, Digital Humanities, Document Visualization, Natural Language Process-

ing.

Abstract:

Interactive text visualization can help users explore and gain insights into complex and often large document

sets. One popular visualization strategy to represent such collections is to depict each document as a glyph

in 2D space. These spaces have proven effective, especially when combined with interactive exploration

methods. However, current exploratory approaches are largely limited to single areas of a 2D spatialization,

lacking support for important comparative exploration and analysis tasks. In this paper, we extend a ﬂexible

focus+context exploration technique to tackle this challenge. In particular, based on practical tasks from the

digital humanities, we focus on exploring and investigating relationships between entities in large document

collections. Our approach uses natural language processing to extract characters and places, including infor-

mation about their relationships. We then use linked views to facilitate visual analysis of extracted information

artifacts. Based on two usage scenarios, we demonstrate successful applications of the approach and discuss

its beneﬁts and limitations.

1 INTRODUCTION

Recently, visual text analysis has gained a lot of at-

tention. This is not surprising given the ever increas-

ing amount of digitized texts. In the domain of dig-

ital humanities, web portals such as Project Guten-

berg

or Google Books

provide easy access and of-

fer new opportunities to derive high-quality informa-

tion from text. Natural language processing (NLP)

can be used to automatically extract information from

text, such as entities or important topics, which can

then be abstracted and visualized. Interactive visual-

ization offers a large collection of effective methods

to explore, analyze, and understand such abstractions.

Well-known approaches have been introduced, for ex-

ample, for extracting named entities and visually ex-

ploring their relationships (Stasko et al., 2008), and

for analyzing large collections of annotated text (Cor-

rell et al., 2011).

An established visualization technique for large

text collections is to depict each document as a glyph

on a 2D plane. An early example (Wise et al., 1995)

creates a 2D spatialization of documents in order to

http://www.gutenberg.org

https://books.google.com/

support analysts to better understand document simi-

larities. There are many other approaches in this con-

text, which offer interaction methods for well-deﬁned

information needs. However, approaches that enable

users to freely explore and navigate 2D spatializations

on different level of abstractions are rare. DocuCom-

pass (Heimerl et al., 2016), which consists of an in-

teractive focus+context approach based on the magic

lens metaphor, is a prominent one. It offers several

methods to characterize and summarize documents

and allows users to freely explore and analyze the 2D

space.

The method we present is based on the DocuCom-

pass design, and extends it in several aspects. Based

on close collaborations with humanities scholars, we

have derived practical analysis scenarios and tasks

for literary texts. The need for such methods also

became obvious in an initial user feedback session

of DocuCompass, during which several participants

with a humanities background expressed their interest

in such approaches. In particular, our collaborators

are concerned with the analysis of novels. Practical

examples are the Middle High German novel Parzi-

val (Von Eschenbach et al., 2003), which consists of

several books, or the epistolary novel The Sorrows of

244

John, M., Heimerl, F., Vu, B-A. and Ertl, T.

Visual Analysis and Exploration of Entity Relations in Document Collections.

DOI: 10.5220/0006614902440251

In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 3: IVAPP, pages

244-251

ISBN: 978-989-758-289-9

Young Werther (Von Goethe, 1991), which comprises

a collection of letters. The complexity of such mate-

rials and the fact that many literary works of interest

consist of a collection of different texts underlines the

importance of support for comparative analysis sce-

narios. As an initial step during analysis, getting an

overview of the occurring entities, the relationships

between them, and their development during the sto-

ryline is important.

To tackle these challenges, we created an interac-

tive design that affords exploration and investigation

of relationships between entities in a document col-

lection. The approach uses NLP methods to extract

entities and terms, which provide a ﬁrst impression

about their relationships. Based on the results, we de-

velop visual abstractions that afford tasks relevant to

the concept of “distant reading” (Moretti, 2005). Vi-

sual abstractions of text documents convey useful in-

formation and assist users in getting a general under-

standing of the information a document contains with-

out reading the whole text (Koch et al., 2014). This

can be especially helpful when analyzing a large doc-

ument collection. At the same time, however, users

need full access to the source texts for “close read-

ing” to verify ﬁndings or hypotheses. For this reason,

we include visual abstractions that enable users to di-

rectly access text passages a particular information ar-

tifact is extracted from.

The main contributions of this work are: i) We ex-

tend DocuCompass with additional NLP methods to

extract named entities and information about their re-

lationships. ii) In addition, we provide linked views,

which support a comparative exploration of entity re-

lations in document collections and facilitate a distant

and close reading analysis J

anicke et al. (2015). iii)

Usage scenarios show successful applications of the

approach and demonstrate its beneﬁts and limitations.

2 RELATED WORK

Since our approach is based on DocuCompass, which

support the exploration of 2D document spatializa-

tions using magic lenses, we ﬁrst summarize existing

approaches in this area. Next, we review visual text

analytics approaches that focus on the analysis of ex-

tracted named entities and their relationships.

2.1 Spatialization of Texts and Magic

Lenses

An established way to represent large document col-

lections is to map each document as a glyph in

2D space. These spatializations are often based on

meta data, such as geo-locations (MacEachren et al.,

2011), or on the vector space model, which repre-

sents each document as a (high-dimensional) vec-

tor. There exist many approaches, such as princi-

pal component analysis (PCA) (Wold et al., 1987)

or the t-distributed stochastic neighbor embedding (t-

SNE) (Van der Maaten and Hinton, 2008), which map

high-dimensional vectors into 2D by optimizing pair-

wise distances to represent document similarities.

However, there are only a few approaches that

support free exploration of these landscapes. Docu-

Compass tries to ﬁll the gap by providing an easy-

to-use exploration method for 2D document special-

izations using magic lenses (Tominski et al., 2014).

In recent years, magic lenses have been introduced

in different areas. For example, Kr

uger et al. (2013)

present an approach, which expands the exploration

lens metaphor to support complex ﬁlter queries and

the analysis of movement data. Ellis and Dix (2006)

introduce Sampling Lens, which suppress data items

to reduce visual clutter underneath lenses in a scatter

plot.

There are only few magic lens approaches that

support the exploration and navigation of text col-

lections. (Bosch et al., 2013) offer a magic lens to

analyze geo-located micro blog messages in order to

ﬁnd topics connected to speciﬁc events. The Visual

Classiﬁer (Heimerl et al., 2012) provides a lens that

enables users to explore certain regions of a 2D land-

scape in order to get a ﬁrst impression of the contents

of the focused documents. With TopicLens, Kim et al.

(2017) present an interface, which computes topic

models of documents underneath the lens in real time

and it shows keywords of the different topics next to

the lens.

2.2 Visual Text Analytics

Over the last decades, multiple visual text analyt-

ics approaches have been developed. For example,

FeatureLens (Don et al., 2007), a system that pro-

vides several linked views and support users with

exploring frequent text patterns in document collec-

tions. Another popular visualization technique is

“ThemeRiver” (Havre et al., 2002), which visualizes

thematic changes over time in large document collec-

tions. With Parallel Tag Clouds, Collins et al. (2009)

introduce a method that uses multiple word clouds to

visualize differences amongst facets of large text cor-

pora.

There are also many visual text analytics ap-

proaches that particularly support the analysis of ex-

tracted named entities and the relation between them.

(Oelke et al., 2013) present an approach that supports

Visual Analysis and Exploration of Entity Relations in Document Collections

245

the analysis of prose literature. It uses a visual litera-

ture ﬁngerprinting method (Keim and Oelke, 2007) to

visually abstract entities, the relation between them,

and their evolution during the plot. However, it is not

possible to directly access the text in order to investi-

gate ﬁndings or hypotheses.

John et al. (2016) introduce an approach that ex-

tracts named entities from literary text for an inter-

active co-occurrence analyses. It offers several views,

including word clouds, graphs, and plot visualizations

that facilitate distant and close reading analyses. An-

other similar approach is POSvis (Vuillemot et al.,

2009). It provides multiple coordinated views that

support the analysis of the vocabulary in the vicin-

ity of one or more named entities. However, both sys-

tems have been designed primarily for intra-document

analysis, whereas we support inter-document analy-

sis.

Jigsaw (Stasko et al., 2008) offers several views

that support users in exploring, analyzing, and under-

standing large document collections. It automatically

extracts named entities and allows to track and ex-

plore their relationships across the document collec-

tion. Another approach closely related to our work

is NEREx (El-Assady et al., 2017). It offers an in-

teractive framework to explore and analyze relation-

ship between named entities in verbatim conversa-

tional transcripts. It offers several linked views, in-

cluding network graphs, visual query interfaces, and

text views to reveal thematic and temporal structures

in the text. Both approaches enable users to explore

and analyze document collections, however, they do

not support the exploration of text corpora on ar-

bitrary levels of granularity. Our approach enables

users to activate, adjust, and navigate several different

lenses in order to explore and analyze 2D document

landscapes. Thus, users can adjust their exploration

strategy to a more ﬁne- or coarse-grained analysis in

order to focus and analyze different data sets easily.

3 VISUAL ANALYSIS

Our approach extends DocuCompass and offers effec-

tive means to facilitate the exploration of entity rela-

tions in document collections. It provides NLP meth-

ods for extracting characters and places including in-

formation about their relationships. The approach en-

ables analysts to visually explore these entities and

their relationships in a close and distant reading anal-

ysis based on a co-occurrence analysis. In the follow-

ing, we ﬁrst summarize the most important details and

features of DocuCompass, and subsequently present

the additional capabilities of our new approach.

3.1 DocuCompass

DocuCompass is a ﬂexible focus+context exploration

technique for 2D spatializations. It provides magic

lenses and ﬁlls the gap between visualization and in-

teraction techniques that provide large scale overview

and detailed inspection of a document collection. An-

alysts can freely move a magic lens by clicking and

dragging. Its size can be adjusted by using the mouse

wheel. This way, users can explore text corpora on

different levels of granularity. When analysts focus

on a document subset with a lens, DocuCompass dis-

plays visual abstractions or text labels, which summa-

rize the main content of the documents underneath the

lens. Those are shown next to the lens. The visual ab-

stractions comprise bar charts to depict the number of

citations over time for scientiﬁc articles, or heat maps

to provide a preview of term distributions. To get an

overview of the important keywords of the focused

document set, DocuCompass offers the term weight-

ing schemes document frequency (df), term frequency

— inverse document frequency (tf-idf), and G

. Fur-

thermore, the approach provides local and global nav-

igation support for an exploratory analysis. Global

navigation supports users with information to identify

and explore areas similar to the focused one. Local

navigation helps users to optimally place and adjust

the lens by providing information about the focused

document set. For global navigation, DocuCompass

offers heat maps, which provide an overview of how

frequent a term is used in other areas. In addition,

users can hover over or click on a term to highlight

all documents that contain this term. To support local

navigation, the approach offers a clustering algorithm

that provides users with information about the simi-

larity structure of the focused document set to assist

local navigation.

3.2 Text Processing

Once a document collection is loaded into the sys-

tem, it is processed in a linguistic analysis pipeline.

We expand the existing DocuCompass pipeline, con-

sisting of tokenization, sentence splitting, lemmatiza-

tion, and keyword extraction methods such as tf-idf or

, with part-of-speech (POS) tagging and named en-

tity recognition (NER). For both implementations, we

use Stanford CoreNLP

. Using NER, entities, such as

characters and places, can be extracted automatically

and thus relations between them can be identiﬁed

across the plot. By default, an entity co-occurs with

another entity if they appear in the same or neighbor-

ing sentences. This can be adapted if necessary. POS

https://stanfordnlp.github.io/CoreNLP/

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

246

Figure 1: The main workspace consists of (a) the 2D document spatialization view using t-SNE, (b) the entity network view,

and (c) the text view or POS tag explorer.

tagging classiﬁes words in the documents, for exam-

ple, as nouns, verbs, adjectives, or adverbs. Based on

the results, we provide users with information about

verbs and adjectives that co-occur with the extracted

entities. This way, users can get an overview which

terms are used to describe or characterize an entity or

a relation between entities.

3.3 Visual Approach

After the documents have been linguistically ana-

lyzed, the main workspace is shown as depicted in

Figure 1. It consists of a 2D document spatializations

view, an entity network view, and the text or POS tag

explorer. We use t-SNE to map the high-dimensional

vector into a 2D document landscape. Once the 2D

landscape is created, we provide the same features as

DocuCompass (Section 3.1). However, we extend the

approach in several aspects. We provide the possibil-

ity to show the extracted characters and places next to

the lens (Figure 1

). This helps users to get a quick

overview of the entities that appear in the focused

document set. In addition, we show the relationships

between entities of multiple lenses in a graph view as

depicted in Figure 1

. For this, we assign a unique

color to each of the added lenses. Since we currently

allow ﬁve different lenses, we deﬁned a color scheme

consisting of ﬁve distinct colors, using the qualita-

tive color scheme of ColorBrewer 2.0

. The assigned

colors are consistently used throughout the visualiza-

tions. Furthermore, we enable users to directly access

text passages in order to further analyze relationships

between entities (Figure 1

After users have activated a lens, the entity net-

work view is updated and can be explored. This view

is based on the prefuse library Heer et al. (2005) and

contains a force-directed graph visualization that rep-

resents relationships between the extracted characters

and places, as depicted in Figure 1

. The network

view also supports interactive features, such as pan-

ning, zooming, or re-arranging to support the explo-

ration of the entity network. The rectangular nodes

represent characters, the ellipsoidal nodes places, and

the edges co-occurrences between the entities in the

text. The color of the nodes represent the respec-

tive lenses. If entities are mentioned under different

lenses, we assign the color of the lens under which

the entity occurs most. In addition, we provide two

visual representations to indicate how relevant the en-

tity is in each focused data set. Users can switch be-

tween stacked bar charts (Figure 2

) and bar charts

(Figure 2

). This helps to get a quick overview of

the occurring entities, their relationships, and their oc-

currences in the focused data sets.

http://colorbrewer2.org

Visual Analysis and Exploration of Entity Relations in Document Collections

247

Figure 2: We provide two visual representations, (a) stacked

bar charts and (b) bar charts, to show the distribution of

entities in the different focused document sets.

Figure 3: The POS tag explorer provides an overview of

adjectives and verbs that co-occur with selected entities.

By hovering over a node or an edge, the con-

nected entities are highlighted in red. This supports

users in identifying relationships, especially when it

is a graph with many relations. Furthermore, users

can click on a node or an edge to further investigate

the entity relations in the text view or POS tag ex-

plorer. Additionally, we highlight all documents in

the 2D document landscape that contain the selected

entities. Thus users can easily identify and explore re-

gions with the same occurring entities. The POS tag

explorer is based on a word cloud view and lists all

adjectives and verbs that co-occur with the selected

entities as depicted in Figure 3. Verbs are shown in

green and adjectives in orange. The font size of the

visualized words is scaled proportionally to their oc-

currence frequency. The tabs are named after the cor-

responding entities and colored according to the re-

spective lens. This gives users a ﬁrst impression of

the main terms that describe an entity or the relation-

ship between two entities.

By clicking on a term, users can further analyze

the occurrences in the text view as depicted in Fig-

ure 4. In addition, users can switch at any time to the

text view through a context menu. This view allows

users to work with the text directly and, again, high-

lights entities in red, verbs in green, and adjectives in

orange. In addition, the text view shows a vertical ﬁn-

gerprint next to its scrollbar to represent the distribu-

tion from the respective occurrences. This helps users

ﬁnd and analyze text passages faster. Using the tabs,

users can easily switch between different passages.

4 USAGE SCENARIOS

In the following, we present two usage scenarios that

demonstrate the capabilities of the approach by ana-

lyzing a modern and old English novel. A ﬁctitious

Figure 4: Text view with selected place London and adjec-

tive ﬁne.

literary scholar has read both novels a long time ago

and is now trying to retrace the storyline and the rela-

tions between the main characters with the help of the

approach.

For the analysis, we split both novels into smaller

parts based on their paragraphs. Subsequently, we

used t-SNE to create the 2D document landscape,

where each glyph represents a paragraph. Further-

more, we provide the aforementioned visualizations

and interactive features (Section 3.1).

4.1 Analysis of “The Lord of the Rings:

The Fellowship of the Ring”

In our ﬁrst usage scenario, we present an analysis of

the novel “The Fellowship of the Ring”. It is the

ﬁrst of three volumes of the novel “The Lord of the

Rings” by J. R. R. Tolkien and was published in 1954.

The storyline is about the fellowship that consists of

nine members: four Hobbits, two Men, one Elf, one

Dwarf, and one wizard and their journey to the land

of Mordor, where they seek to destroy a magical ring.

In the ﬁrst step, the literary scholar activates a lens

and explores the entities next to the lens. While an-

alyzing different document sets, she encounters the

name Rivendell. She remembers that Rivendell ap-

pears in the storyline, however she cannot recollect

in which context. In order to get more information

about Rivendell, she selects the place to highlight the

corresponding node in the entity network view. That

way, she can easily identify that there is a strong re-

lation between Rivendell and Frodo, a hobbit and the

protagonist. In addition, she ﬁnds that a strong rela-

tionship between Frodo and Frodo’s uncle Bilbo exist.

To ﬁnd out more, she selects the edge between them

to list all co-occurrences in the text explorer as de-

picted in Figure 5

. By analyzing the text passages,

she discovers that this is the point of the plot where

Frodo and Bilbo meet again in Rivendell, since they

left their hometown. As a result, she remembers that

Rivendell is the place where Frodo is brought after he

was nearly killed by Nazguls, servants of Sauron, the

original owner of the ring. To learn more, she clicks

IVAPP 2018 - International Conference on Information Visualization Theory and Applications

248

Figure 5: (a) The entity network view that represents the

relationship between Rivendell, Frodo, and Bilbo, (b) the

text view shows the occurrences of Rivendell in the focused

document set, and (c) the entity network view depicts the

relation between the places Rivendell and Bruinen.

the term Rivendell in the 2D document landscape to

highlight all documents that contain this place. Sub-

sequently, she activates another lens to explore the

highlighted documents in the spatializations with the

help of the entity network view. Initially, she ﬁnds a

document collection that contains only a small num-

ber of occurrences. The literary scholar then adjusts

the lens in order to cover a larger number of relevant

documents. This way, she locates an interesting docu-

ment set and by analyzing the text passages (Figure 5

) she ﬁnds more evidence for her assumption. Dur-

ing the analysis, she detects another place, Bruinen,

that is related to Rivendell, as shown in Figure 5

With the aid of the text explorer, she ﬁnds out that

Frodo is carried on a horse towards the Ford of Bru-

inen to Rivendell, with the Nazguls in pursuit.

4.2 Analysis of “Harry Potter and the

Sorcerer’s Stone”

In the second usage scenario, the ﬁctitious scholar an-

alyzes “Harry Potter and the Sorcerer’s Stone” by J.

K. Rowling. It is the ﬁrst volume of the Harry Potter

series and was published in 1997. The plot is about

the adventures of the young wizard Harry Potter in

his ﬁrst year at the Hogwarts School of Witchcraft

and Wizardry and the ﬁrst encounter between him and

Lord Voldemort, a dark wizard who killed Harry’s

parents.

To retrace the course of the novel and the rela-

tion between the main characters, the literary scholar

adds a lens to explore the 2D document spatializations

by means of the entity network view. While explor-

ing, she notices that the person Vernon has a relation

to Harry Potter and is surprised since she cannot re-

member him as depicted in Figure 8. To ﬁnd out more

about him, she activates the word cloud view with the

occurring verbs and adjectives that provide ﬁrst in-

Figure 6: Word cloud view of the verbs and adjectives that

co-occur with Harry and Vernon.

Figure 7: The text view with the selected entities Harry and

Vernon and the adjective nastily.

sights into their relationship. By analyzing the word

cloud, she identiﬁes the terms nastily and viciously,

which seem to indicate a negative relationship be-

tween Harry and Vernon, as shown in Figure 6. To

investigate this in more detail, she selects both terms

and analyzes the relevant text passages in which the

terms Harry and Vernon co-occur, as shown in Fig-

ure 7. She learns that Vernon is the uncle of Harry

and that he always treats him spitefully. As a next

step, she again explores the entity network view and

realizes that two subgraphs exist (Figure 8). By fur-

ther analyzing the occurring persons and their rela-

tionships, she ﬁnds that the ﬁrst subgraph (Figure 8

) represents the world of Muggle (non-magical),

where Harry Potter lived until he was 11 years old.

The second subgraph (Figure 8

) depicts the rela-

tions of Harry to entities of the world of wizards.

The usage scenarios show that our approach fa-

cilitates analysts in exploring and analyzing named

entities in document collections. The implemented

automatic and visual methods support users to gain

insights and to generate and verify hypotheses.

5 DISCUSSION AND FUTURE

WORK

The presented approach is effective at supporting ex-

ploration and comparative analysis of entities and

their relationships in document collections. However,

there are several remaining challenges that we would

like to discuss here.

Our approach can be ﬂexibly expanded by addi-

tional visualizations, such as a plot view Liu et al.

(2013). It can convey a coarse idea of the storyline

and helps to get an overview of the dynamic relation-

ships between entities.

Visual Analysis and Exploration of Entity Relations in Document Collections

249

Figure 8: The entity network depicts two subgraphs: (a)

entities of the Muggle and (b) of the world of wizards.

Furthermore, we want to visualize the temporal

development of such a network and the possibility to

compare different states of networks. This could be

realized by either a juxtaposed or superimposed ap-

proach Beck et al. (2014). The former place small

multiples next to each other. Users then, for exam-

ple, have to trace a node over several small diagrams.

Superimposed approaches, on the other hand, can be

stacked on top of each other and differences can be

marked by color or stroke.

Another missing aspect for analysis is the rep-

resentation of the temporal information. Especially,

when working with time-dependent storylines, such

as the volumes of “Harry Potter”. Therefore, we

aim to provide temporal information of the differ-

ent focused document set. One option could be to

complement the entity network with several ﬁnger-

prints that show the occurrences of entities and link

them to different focused documents. In addition,

we plan to map the temporal context for each glyph

in the 2D document landscape similar to the idea of

ClockMap Fischer et al. (2012).

Current co-occurrence detection is based on

named entity recognition and a straightforward dis-

tance measure between them. This works well for

most cases, but nor for all, especially those involving

anaphora or different names for the same person. To

improve the current detection method, we plan to in-

clude coreference resolution in the future, which can

ﬁnd all expressions that refer to the same entity in a

text.

NLP approaches are typically trained on large and

contemporary corpora and cannot be expected to pro-

vide state-of-the-art results for historical texts. This

can lead to uncertainties and errors in the preprocess-

ing steps and subsequently in the visualizations. To

tackle this challenge, we want to communicate the

uncertainty in the visualization to make users aware

of it. A possibility could be to provide visual cues,

such as color saturation to indicate the uncertainty.

In addition, we will let users adapt and correct errors

interactively to improve the performance of the NLP

techniques.

The presented approach provides ﬁrst insights and

serves as a basis for discussion with our literature ex-

perts. Based on this feedback and insights, we want

to further improve the approach in close cooperation

with them. This way, we can tailor speciﬁc features

and visualizations in a formative process to better sup-

port their analysis.

6 CONCLUSION

In this work, we have presented an approach for ex-

ploring and investigating relationships between enti-

ties in document collections. It provides NLP meth-

ods to automatically extract characters and places, in-

cluding information about their relationship. These

extracted information can be analyzed in a close and

distant reading fashion. To support this, we offer

linked views that facilitate exploration of entity rela-

tions. Analysts can activate, adjust, and freely navi-

gate multiple lenses to explore the occurring entities

in a 2D document space. In addition, an entity net-

work view shows relations between entities of differ-

ent document subsets and enables users to directly ac-

cess text passages to further investigate the relation-

ships between them. Two usage scenarios provide

ﬁrst insights and show the applicability and useful-

ness of our approach.

ACKNOWLEDGMENTS

This work was funded by the German Federal Min-

istry of Education and Research (BMBF) as of the

Center for Reﬂected Text Analysis CRETA at Uni-

versity of Stuttgart.

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