Argumentation Trails and Topic Maps

8/14/2019 Argumentation Trails and Topic Maps

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Automatic Extraction of Topic Maps based

Argumentation Trails

Marco Bchler, Lutz Maicher*, Frederik Baumgardt, Benjamin Bock*

Natural Language Processing Group,

University of Leipzig, Germany

[mbuechler | maicher | fbaumgardt | bock]@informatik.uni-leipzig.de

Abstract. With argumentation trails we introduce an approach of finding

relevant associations between arbitrary terms. An argumentation trail between

two termsis an ordered list of co-occurrences, providing a connected path from

the origin to the endpoint of the argumentation. Within this paper the automatic

generation of argumentation trails is examined and assessed. Furthermore, the

formal representation of these trails as Topic Maps is implemented. This

enables the integration of argumentation trails with further background

information to support sensemaking or other discourse enriching techniques for

academic or political debates.

Motivation and Introduction

Small world related research on natural language corpora, hypertext structures on the

web or social networks like co-authorships has shown that the average path length

between two arbitrary nodes is generally not larger than 7. The general problem is the

discovery of the shortest path between these nodes, especially if the edges of the

graph are only partially known and the distance is greater than two.

Academic or political debates are confronted with a similar problem. In a lot of

cases there exists the supposition of a relationship between two terms of a specific

domain, which are the origin and the endpoint of an argument. But the closer

connection between both is unidentified and will become the essence of a discourse.

Our approach is the disclosure of the relevant connections between the origin and the

endpoint of an argument. We model this relationship as a connected path of terms,

based on co-occurrences. This path is called argumentation trail.

A co-occurrence is a directed edge between two terms c(ti,tj) that is extracted

automatically by using (different) statistical methods. An argumentation trail a(t1,tn)

between two arbitrary terms t1 and tnis an ordered list of co-occurrences, providing a

* Topic Maps Lab: http://www.topicmapslab.de/


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2 Bchler, Maicher, Baumgardt, Bock

connected path from t1 and tn. The distance dof an argumentation trail is the number

of co-occurrences in this list. The distance between the terms t1 and tn is the length of

the shortest argumentation trail between them.

The general problem is the calculation of the shortest and most significant

argumentation trails between two terms. Similar to the small world example above,

the argumentation trails for any distances greater than one are not obvious. The

extraction of relevant argumentation trails becomes more complex as the density of

the co-occurrence graphs and the distance between origin and endpoint increases. To

support sensemaking and other discourse supporting techniques in academic and

political debates, we introduce a method for the automatic extraction of

argumentation trails.

Topic Maps are used for representing highly networked and interlinked domains.

Furthermore Topic Maps is a semantic integration technology because each topic is ahub for all available information about a specific subject. Besides others, topic maps

are used for sensemaking and knowledge federation techniques. For these

applications, the integration of argumentation trails with further background

information is necessary.

Therefore the approach presented in this paper combines the idea of the automatic

generation of argumentation trails with the formal representation of these trails as

topic maps. In the evaluation we assess the quality of this first proof of concept.

State of the Art

A graph is an intuitive representation of relations between words. More formally a

graph can be expressed by G=(V,E) where as Vis a set of vertices (nodes, words) and

Ea subset of edges ofVxV. In Natural Language Processing (NLP) the set Vof nodes

can be comprehended as the set of a corpus' word types. The set of edges Ecan be

computed by co-occurrence analysis [Bue08, Bue09]. Typically tens or hundreds of

million co-occurrences can be extracted. That's why measures are necessary for

computing an edge's significance. In the early 1990s some basic measures like the

mutual information [CH89] were introduced. However this measure displays some

numerical problems for very rare co-occurrences. As a result, in 1993 an adoption of

the log likelihood measure was introduced by [Dun93] which can handle more

infrequent events. However, most significant edges are completely distinctive. Whilst

log likelihood ratio prefers more frequent co-occurrences the mutual information

computes less frequent edges as more significant [Bue08, Eve05]. That's why loglikelihood ratio is better for exploration and understanding a new domain by

computing more general word associations. Whereas if the domain is well-known less

frequent information becomes more relevant for users [BB04].

One elementary feature of a graph is the small world property which describes the

average path length between two different nodes [WS98, Bar00]. Research on small

worlds is based on works of Milgram [Mil67]. Several evaluations and applications

on natural language corpora, hypertext structures on the web or co-authorships on


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Automatic Extraction of Topic Maps based Argumentation Trails

publications [CS01] have shown that the average path length is very small and

generally not larger than 7.

Similar to lexical chaining argumentation trails are a minimum spanning tree of

words having same or similar contexts [MWW07, MWH08]. However, there are

differences in use cases and texts. Lexical chaining is often used in text

summarisation [BCP01] or word sense disambiguation [GK03]. Thereby a stronger

work by sliding from paragraph to paragraph [MWH08] is useful. Since ancient text

corpora are only fragmentary achieved (caused by e. g. natural decomposition and

conscious deletions of person names or cities) an approach working directly on a co-

occurrence graph is chosen.

Automatic Topic Maps Generation

Topic Maps (ISO 13250), the international industry standard for semantic information

representation and integration, is an implementation of the subject-centric modelling 1

approach [MB08]. A topic map is a subject-centric domain model, consisting of

topics, as subject proxies, and associations between them. Each topic can represent a

set of typed names for the subject. Furthermore, occurrences allow representing typed

properties of the subject. The associations between the topics are typed, role-based

and n-ary. Summarised, Topic Maps provides a subject-centric modelling approach

anda full set of basic modelling constructs, like names, occurrence and full-featured

associations for convenient domain modelling. For a more comprehensive

introduction into TM we refer to [AM05, Ma07a]. A topic map can be seen as a setor

a stream of statements about subjects [LH08].

Besides the expressive and flexible modelling constructs, Topic Maps provide a

powerful integration model [Ma07b, TMDM]. This integration model assures that

two topics representing the same subject will always be merged. Technically, the

subject of a topic is identified by a set of URIs which are called subject identifiers.

Whenever two topics in a topic map have one subject identifier in common, they are

automatically merged. Hence it is guaranteed that in a topic map there is always only

one information hub for each subject. This powerful integration model is the

fundament for the usage of Topic Maps as integration technology.

The subject-centric modelling approach supports the (semi-automatic) generation

of subject-centric web portals and other interfaces to the highly-interlinked data

[MB08]. Combined with interchange protocols like TMRAP [Ga06] or TMIP

[Ba05b], these applications simultaneously feed the web of linked data [Be06].

1 According to the Topic Maps standards a subject is anything whatsoever, regardless of

whether it exists or has any other specific characteristics, about which anything whatsoever

may be asserted by any means whatsoever [TMDM]. Summarised, a subject is anything

that can be a topic of conversation. Simplified, the subject-centric modelling enforces that for

each relevant subject exactly one proxy is created within the domain model. Consequently,

proxies become the unique information access points for all information about their subjects.


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In the context of the work presented in this paper, the generation of Topic Maps

data is an important issue. The following table summarises the general categories for

approaches of creating Topic Maps data:

generic manual

generation

Topic Maps can explicitly be written in a text editor using an

interchange format like CTM2 or XTM3. Furthermore, generic

Topic Maps editors like Ontopoly or Topincs [Ce07] can be

used. Generally the users need deep knowledge about the

concepts of Topic Maps.

domain-specific,

manual generation

In domain specific Topic-Maps-based portals or applications

like Musica migrans [Ma08] or Fuzzzy [LK08] the users

simply create data by using web forms or other interfaces.

The Autonomous Topic Maps approach [Ma07a] fits to thiscategory, too. The users dont need any knowledge about

Topic Maps.

automatic

generation

(Nearly) all data can be represented as topic maps, so any

application might produce topic maps data for its domain.

Automatic generation is implemented by the approach

presented in this paper or similar work [BM06].

transformation (Nearly) all existing data can be seen as topic map (through

subject-centric glasses). Providing Topic Maps views for

legacy data supports the integration of heterogeneous data.

Hereby a differentiation between materialized (ETL) and

non-materialized(non ETL) topic maps views is established

[Ba04].

For the global interoperability and usability of generated Topic Maps data two

issues are important: (1) the domain ontology and (2) the used subject identifiers at

type and instance level [Ma07a].

The domain ontology formalises the domain knowledge behind the data and can be

used for the optimisation and generation of the data consuming applications [Bo08].

The ontology of the Topic Maps data created by the work presented in this paper is

shown in Figure 2.

For the integration of the generated topic maps with other information about the

represented subjects, adequate subject identifiers must be used at the type and, very

important, at the instance level [Ma07b]. In the methodology sectionthe approach for

choosing the subject identifiers in the argumentation trails is sketched.

2 http://www.isotopicmaps.org/ctm/3 http://www.topicmapslab.de/glossary/XTM


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Methodology

Exploring argumentation trails in a semantic network is closely related to searching

the k-shortest paths from a source to a target node in an undirected graph - where the

number of paths k is substituted by a maximum path length. The k-shortest paths

problem is applicable in many fields and has been extensively studied, with the

number of publications approaching 100. The four most widely recognized methods

are those of Yen [Yen71], Lawler [Law72], Katoh [KIM82] and Hoffman [HP59].

Yen's algorithm is a naive usage of Dijkstra's shortest path algorithm, with complexity

O(kn3), where k is the number of paths and n the number of nodes in the graph.

Lawler and Katoh improve upon Yen by compartementalization of the paths, Lawler

by a constant factor and Katoh with complexity O(kn 2). Even before Yen, Hoffman

introduced a different idea with the pre-calculation of shortest-path for all nodes,

resulting in complexity O(kn2).

With highly data-rich environments, as described in this paper, memory constraints

become an issue as well. Thus, of the above algorithms only Yen was feasible, but

much too slow. Early trials demonstrated the need for a custom-made method, as

required by the specific problem.

Drastic reduction of the search space was necessary. In the following approach to

explore argumentation trails with maximum length of 3, we utilise topologic attributes

that help us reduce the actual amount of data.

Instead of searching for paths between source s0 and target t0 nodes, connections

and overlaps between the neighborhoods ( Ns = {s1,..,sn}, Nt = {t1,..,tm} ) of both

endpoints are being searched. For each neighbor of the source or target nodes to be

included in an argumentation trail, it must be incident to, or part of the neighborhoodsurrounding the endpoint on the opposite side of the trail. Thus, in a Graph G=(V,E)

we search for nodes v that are either member of both Ns and Nt (vNs Nt) or

incident to a node in the opposite neighborhood ((v, ti) E for vNs, or (v,si) E for

vNt).

Fig. 1: Path selection on the topology of the endpoints' neighborhoods


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Calculations are performed within the JUNG2 framework4, visualizations within

theprefuse5 toolkit.

For each combination of nodes in the co-occurrence graph, if neither node has

degree 0, two HashSets are built - each containing the incident nodes of either the

source or the target. The smaller neighborhood is selected and its incident nodes

compared with the HashSet of the opposite neighborhood, including source or target

itself. If a match is found and hence an argumentation trail exists, both nodes and the

connecting edge are inserted into a new graph-structure for the resulting

argumentation trails. This graph is then being completed with the edges between

source or target and their neighborhoods. The resulting structure is a graph containing

all argumentation trails of maximum length 3 between source and target.

For the endpoint with the lesser degree, a search on every node O(n) incident to the

nodes in the neighborhood O(n) is performed. For a single set of paths between twonodes, the computational complexity is O(n2), with a very low constant factor.

The conversion of selected argumentation trails to topic maps and the subsequent

export into the XTM-format are carried out by a composition of the prefuse- and the

tinyTiM-frameworks6. The figure below summarizes the TMCL schema7 of the

created topic maps. Each node in a prefuse-graph is exported as a topic of type

Concept. The subject identifiers of these topics are composited by the namespace

concept (see footnote 8) and the correctly encoded term. Each edge between two

nodes is exported as an association of type Argumentation Step. The frequency of a

concept is exported as an occurrence Frequency of the according topic. The

significance of the relationship between two concepts is exported as an occurrence

Significance of the topic which reifies the according argumentation step. Finally, an

association between the source and target of the argumentation trail is created, tofacilitate orientation within the topic map. Additionally, some metadata about the

argumentation trail is added to the topic map by using the Dublin Core vocabulary as

it is described in [Mai08].

Fig. 2: Schema (TMCL)8of the Topic Maps export for argumentation trails

4 http://jung.sourceforge.net/5 http://prefuse.org/6 http://www.topicmapslab.de/projects/tiny_TiM7 http://www.isotopicmaps.org/tmcl/


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Results Graph and argumentation trail properties

The underlying co-occurrence graph is based on a corpus of about 5.5m sentences and

87m word tokens. A co-occurrence of the graphs shown in table 1 is significant if it

occurs at least three times and has a minimum log likelihood ratio of 6.63. All

columns of table 1 labeled by 2) to 7) are different subgraphs of 1). In columns 2) to

4) the minimum word frequency is 2. Additionally, the 100, 300 and 500 most

frequent words were excluded. Column 5 of table 1 shows a smaller subgraph only

based on named entities. Whilst column 6 expands all named entities of column 5 by

normalised9 equal words, column 7 works on both a normalised corpus and named

entity list.

Comparing the average degree of the underlying co-occurrence graph in row e) and

the average degree of the edge reduced argumentation trail in row g) it is obvious that

the path finding algorithm reduces the degree dramatically. However, the average

degree of a node in the argumentation trail in column g) is significantly smaller than

the degree of the inner nodes of an argumentation trail h) for trails with two inner

nodes and i) for trails with only one inner node. This is caused by a more central role

of hubs within an argumentation trail.

Table 1: Some properties of argumentation trails inclusive the characteristic

features of the underlying co-occurrence graph10 11

8 The schema is created with the TMCL editor Onotoa (http://onotoa.topicmapslab.de/). The

used graphical notation is a non-normative GTM level 1 syntax

(http://www.isotopicmaps.org/gtm/) - proposed by the Topic Maps Lab and implemented by

Onotoa. The namespace eaqua must be resolved to http://psi.eaqua.net/ontology/ and thenamespace concept must be resolved to http://psi.eaqua.net/corpora/[corpusname]/..

9 Normalised: All letters will be made lowercase and diachritics will be removed.10 Column labels: 1) Complete graph, 2) TOP 100 stop words and words with a frequency of 1

are removed , 3) SW=300, min. freq=2, 4) SW=500 min. freq. 2, 5) only Named Entities

(NE), 6) Normalised Named Entities, 7) Named Entities Normal. Corpus11 Row labels: a) Number of nodes, b) Number of co-occurrences, c) Number of significant co-

occurrences, d) Percentage, e) Average degree, f) Number of trails, g) Average degree, h)

Average degree of internal node (trail length 2), i) Average degree of internal node (trail

length 3)

1) 2) 3) 4) 5) 6) 7)

a) 538,572 388,929 363,359 353,618 1,149 4 ,4 87 2,178

b) 57,762,474 34,818,138 25,615,956 21,004,538 15,436 126,188 152,856

c) 30,382,422 21,739,476 17,687,582 15,462,940 14,876 69,858 84,124

d) 0.53 0.62 0.69 0.74 0.96 0.55 0.55

e) 56.41 55.90 48.68 43.73 12.95 15.57 38 .62

f) 361.094 7.958.240 3.087.581

g) 15.34 9.93 7.70 6.79 7.03 7.77 9.93

h) 31.34 21.08 14.33 11.45 7.02 10.15 12 .31

i) 301.38 362.56 285.86 231.39 55.66 76.06 81 .86

> 108 > 108 > 108 > 108

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One more result of table 1 is shown in column 5). Row d) describes the ratio of

significant co-occurrences and found co-occurrences. In table cell 5d) this ratio is

significantly larger than all other ratios. That's why the next section is reduced to this

data set.

Use Case argumentation trails for Classical Studies

In Classics there are many use case scenarios for argumentation trails. On the one

hand you can use such trails for exploring new domains (e. g. new centuries) by

looking for the way in which different terms are related. On the other hand ancient

texts are strongly fragmented. For those cases you can e. g. observe a person A andyou know the context B of this fragmentary document. Using argumentation trails

you can observe how both concepts belong together based on other texts of the same

time frame. Furthermore, you can filter the found trails more rigorous than in figure

3b) by using other words from the fragmentary text. As a result of this you can get a

virtual expansion of the document's story.

Figure 3: a) Connection between two words with a low number of trails b)

Large trail cloud between two words

Typically, one is to observe trails like in picture 3) in such graphs. From a common

start and end point trails are to be found differing in only one node - 3. column in

image 3a. The different nodes of the black and red trails of figure 3a are Krates andHerodot. Looking for both words in the corpus one can find 46 sentences which

contain both words. The counterexample for this is shown in figure 3b. The black and

red trails have only the common starting and ending point. Based on this two

completely different argumentation trail threads exist.


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Further Work and Conclusion

As mentioned in the introduction this paper is a proof of concept. We examined the

feasibility of the automatic extraction of argumentation trails and their usage as

discourse enriching technique in academic or political debates.

The automatic generation has been identified as difficult. However, some very

interesting results have been achieved and should be basis of further research.

As shown in table 1, the number of trails needs to be reduced dramatically. This

might be achieved by e. g. semantic pre-clustering or by authors restrictions.

Semantic pre-clustering causes trails to be rejected if every node is part of another

and completely different semantic cluster. In opposite of lexical chainings [MWH08]

this step is necessary because it's difficult to build a reliable document based

summary (text fragments).Author restrictions can be used to reject trails if edges of

a trail are computed by completely different sets of authors or work.

Furthermore, trails containing network hubs should be weighted lower to avoid

forging results. This could be shown in table 1 as well. All of these complexity

reduction approaches are necessary to compute trails on more complex graphs.

In the field of visualization a stronger clustering of trails is necessary. As depicted

in figure 3a) there exist two almost equal trails differing only in two nodes. By

clustering such trails to more globally relevant argumentation trail threads, the

understanding of more complex trail clouds as shown in figure 3b can be done easier

and faster.

Additionally, typing of nodes will be done by typed significant terms (e. g. literary,

geographic or dating classifications) [BHG08]. The same holds for typing or naming

the edges in the argumentation trails. Such kinds of enrichment will additionallysupport a stronger integration to Topic Maps. Generally, the work in this paper does

not cover the problem of integrating the generated argumentation trails (as topic

maps) with further background information in sufficient detail.

Argumentation Trails and Topic Maps

Based on the historical roots of Topic Maps, the technology focuses on the

aggregation of information to subjects (esp. at the instance level like persons,

projects, etc.). The idea is collecting and documenting information about a subject

from different perspectives, whereby contradictions are expected. The integration

of facts and discourses about subjects is a long standing tradition in Topic Maps,

which is today coined as sensemaking [Pa08] and knowledge federation.

Argumentation trails support discourses, ease the creation of new hypothesis and

open new views to the data. Combined with further background information they are

a tool for sensemaking as discourse supporting tool in academic and political debates.

By using Topic Maps and adequate subject identifiers, the concepts in the

argumentation trails can be (instantly) integrated with other data or applications

dealing with the same subjects.


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Argumentation Trails and Topic Maps