Present Causal Relationship Retrieval
for Historical Analogy
Kenta Matsumaru1 , Ryohei Ikejiri2 , and Yasunobu Sumikawa1
1

Depat. of Computer Science, Takushoku University, 815-1 Tatemachi, Hachioji-shi,
Tokyo, Japan
198466@st.takushoku-u.ac.jp, ysumikaw@cs.takushoku-u.ac.jp
2
Interfaculty Initiative in Information Studies, The University of Tokyo, 7-3-1
Hongo, Bunkyo-ku, Tokyo, Japanikejiri@iii.u-tokyo.ac.jp

Abstract. The importance of studying history and applying this knowledge is widely recognized. In this study, we propose a search algorithm topromote historical analogy by immediately exploiting historical knowledge. Our algorithm outputs present causal relationships similar to the
input past causal relationship. It ﬁrst replaces temporal entities that arelimited to certain periods with their entity types to enhance the connection between the causal relationships of the past and present. Subsequently, it measures causal similarities on a bipartite graph of the pastand present causal relationships. Finally, the accuracy of the algorithm
is evaluated on two datasets comprising present and past causal relationships, respectively. We found that the temporal entity replacementis eﬀective in searching for causal relationships.
Keywords: Causal relationship retrieval · Historical analogy · Temporalentity1

Introduction

Historical knowledge can be beneﬁcially applied to present scenarios. For instance, it helps us understand the formation processes of modern society andgain a deeper understanding of the identity of diﬀerent countries or regions.
Indeed, many countries have history classes from elementary school onward.
Previous studies have deﬁned the conditions necessary to facilitate historicalanalogies [16], proposed an algorithm to search for past causal relationships thatare similar to the present causal relationships [22], and designed history classes
using a search algorithm [13]. In one study on designing classes, students were
motivated to connect acquired historical knowledge with the present and use
to develop solutions to contemporary problems, called historical analogy, rather
than merely memorizing past events. When such classes are regularly incorporated into the curriculum, learners gain a deeper understanding of history anddevelop ways to apply it. However, it requires the historical DB to be preparedin advance. Thus, the teacher must pre-select past events that can facilitate

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K. Matsumaru et al.

analogies and describe causal relationships in appropriate sentences. Furthermore, utilizing freshly acquired knowledge is diﬃcult for learners because theylack a comprehensive understanding of the past causal relationships output inthe search results.
Contributions. In this study, we propose a search algorithm to promotehistorical analogy using freshly acquired historical knowledge. When texts expressing the causal relationship of past events are input, our algorithm outputs
present causal relationships that are ranked according to their similarity to the
input. Compared with past studies that promote historical analogies [22, 13], thenovelty and main contribution of this study is that it develops an environment
conducive to immediately facilitates historical analogies. This novelty provides
two contributions compared to this previous study. The ﬁrst one is that thepast causal relationship is usable for the historical analogy. The second one is toeliminate need to prepare a historical database in advance. These contributions
provide an immediate connection between the past and the present for many
learners, regardless of their age or country.
We represent causal relationships by combining related events. As the purpose of this study is to facilitate historical analogies, we arranged the events inchronological order and combined them to create causal relationship. This deﬁnition of causal relationship is typically employed in research on topic detection
and tracking (TDT) [19]. To calculate the rank of each causal relationship, we
assume that events are in similar chronological order are more similar than others. The calculation is performed using the event causality relationship similarity
measurement (ECM) [21].
In texts of past events, person or organizations are named as perpetrators ofevents in a certain time period. However, these entities are not always used inpresent causal relationship descriptions. Moreover, revealing the type of entity,
which is a higher-order expression, is more eﬀective than revealing their names
in promoting analogy [10]. Therefore, our algorithm deﬁnes a temporal entity as
an entity that is used only during a speciﬁc period and replaces it with its type.
The remainder of this paper is organized as follows. Section 2 provides summaries of several related works. The algorithm proposed in this study is describedin Section 3. The results of the experimental evaluation of the proposed algorithm are discussed in Section 4, and the conclusions are presented in Section
5.

2

Related Works

2.1

History Learning Support

In the research area of history study and in the curriculum guidelines in Japanesehigh schools, developing the ability to apply acquired knowledge has become
one of the most important goals. Reviewing the results of research on history
study, the following achievements have been identiﬁed: the discovery of the basic
elements for facilitating historical reasoning [7], in depth analysis of successful

Present Causal Relationship Retrieval for Historical Analogy

3

facilitation of historical analogies [4], and the deﬁnition of a framework of events
that can be connected in the past and present [16].
Learning environments have also been proposed based on the above researchﬁndings. Ikejiri et al. proposed a competitive card game, in which players must
construct causal relationships for present social problems that are structurally
identical to certain past causal relationships. The game helps students in identifying and comparing two causal relationships and stimulating historical analogy.
Ikejiri et al. also proposed another competitive card game that requires players
to create new policies that would revitalize Japan’s economy by learning aboutpast economic policies [12]. A search engine has also been proposed to facilitate
historical analogies from information in news articles reporting present events[22]. Researchers also proposed using this search engine to design an actual history class [13].
Previous studies have identiﬁed the mechanisms that facilitate historical analogy and proposed learning environments that use these mechanisms. However,they have failed to propose methods by which students can immediately utilize freshly acquired historical knowledge. This study is orthogonal to previous
studies; while the search engine realized in this study can be used to promotehistorical analogies in real-time, the learning system proposed in the previous
study can be used to promote historical analogies over multiple reviews.
2.2

Temporal Information Retrieval

The proposed algorithm measures the similarity of causal relationships occurring at diﬀerent points on a time axis. Therefore, we survey previous studies on
temporal information retrieval (T-IR). Previous studies on T-IR include the detection of temporal representations [11], timeline generation [2], historical image
retrieval [6], and future forecasting [15, 18]. This study focuses on historical information retrieval and analogy retrieval. See survey paper [5] for more informationon other previous studies.
Most algorithms search for information that users are seeking [3, 17, 20]. For
example, a search algorithm speciﬁc to the literature review methodology of historians has been proposed [20]. According to this study, a historian ﬁrst ﬁnds an
overview of the subject and then searches for a detailed analysis. The algorithm
proposed in that study not only outputs a variety of content related to a single
query but also emphasizes the diversity of aspects in the output. A method has
also been proposed to output past as well as possible future events [1].
Our algorithm does not output the past events that are directly related tothe causal relationship provided as the input; however, it outputs similar eventsin the form of a causal structure in which similar events occur in the same order.
Several analogous search algorithms have been proposed to identify structuralsimilarities [23, 24]. These algorithms are designed to detect entities that are past
counterparts of present entity. For this purpose, they calculate the similarity ofthe entities using words used to describe each entity. Therefore, the target of thesearch is diﬀerent from that of this study. The targets of these previous studies

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K. Matsumaru et al.

are entity whereas this study focuses on causal relationships that brings together
multiple events.

3

Algorithm

Our algorithm outputs ranked present causal relationships by performing temporal entity replacement, measuring the similarity between past and present
causal relationships, and ranking the results. We assume that the present causal
relationships are stored in a database. As the purpose of this study is to output
ranked present causal relationships, we do not place any restrictions on how thepresent causal relationships are constructed. In other words, it can be generated
automatically using algorithms designed for TDT [19]. The appropriate relationships can also be manually selected and stored in the database in advance.
3.1

Temporal Entity Replacement

This study deﬁnes a temporal entity as an entity that occurs in a certain period(e.g., persons or organizations). In contrast, non-temporal entities are entities
that occur over long periods (e.g., France). The purpose of this temporal entity
replacement is to enhance the similarity between causal relationships of the pastand present by increasing the number of structural matches. For instance, one
type of causal structure that pertains to both the past and present is: this event
is a result of the actions of certain persons.
To perform temporal entity replacement, we ﬁrst extract entities from the
input text using TagMe [8]. To distinguish between temporal and non-temporalentities, we set a threshold for the number of years for which they have existence. We obtained the Wikipedia articles on all the years from 1 to 2020, which
recorded important events from those years and applied TagMe to them. Because TagMe extracts named entities by assigning a link to a Wikipedia article
to a word in the text, we collected these words and the corresponding Wikipedia
articles. For the analysis of the number of years of existence, we used the year ofbirth, establishment, and death or destruction speciﬁed in Wikipedia categories
to obtain a distribution for each entity type. If the shape of the distribution for
an entity type is unimodal and its mode is not over a few hundred years, then all
entities of that type are immediately considered to be temporal. As expected, allentities of organizations, persons, and events fell into this category. In addition
to these 3 types, countries are revealed as an entity type that can be used as atemporal entity.
We manually checked the countries collected using the above methods andfound that some of them, such as Ottoman Empire, no longer exist. To ascertain
the number of years of existence of a country, we collected the year of establishment and disestablishment of each country from the Wikipedia categories andinfoboxes. Note that since the year of disestablishments is not listed for existing
countries such as Japan and the U.S., we consider 2022 as the year of disestablishment for those countries. Fig. 1 shows the distribution of years of existence.

Present Causal Relationship Retrieval for Historical Analogy

5Num. of countries

20
15
10
5
0

0

50

100
150
200
Existence years

250

Fig. 1. Existence year distribution for countries

The vertical axis represents the number of countries, whereas the horizontal axis
represents the number of years a country has existed. This result indicates that120 years should be set as a criterion for a country to be considered as a temporalentity.
After identifying the temporal entities in the text of the event, our algorithm
replaces them with their types. For example, if the temporal entity is a country,
we replace the name of the country with “country.”
3.2

Past and Present Causal Relationship Similarity Measurement

After converting the temporal entities to their type names, we perform tokenizing, lemmatizing, normalizing, and removing stopwords. Subsequently, we create
feature vectors such as using the topic distribution obtained by applying latentsemantic analysis (LSA) to the resulting sequence of tokens. After creating afeature vector for each event, we create a bipartite graph of the two causal relationships. In the graph, we use events as nodes and the similarities between theevents as edges. Event similarity is calculated using the cosine similarity for thetwo feature vectors. We then apply ECM on the graph to determine the similarity. These procedures are performed for all combinations of the input past causal
relationships and the present ones stored in the database. Finally, we output the
top k with the highest scores.
3.3

Algorithm Overview

Algorithm 1 is an overview of the proposed algorithm. First, in lines 2∼4,
the temporal entity replacement described in Sec.3.1 is performed on the input past causal relationship texts. In lines 2∼3 TagMe is applied to the past
texts to extract entities, and subsequently ﬁlter out temporal entities using theresults of the existence year analysis. The temporal entities are replaced with
their type names in line 4. In lines 5∼7, feature vectors are created. In line 6,

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K. Matsumaru et al.

Algorithm 1 Algorithm overview
Input: A past causal relationship: HCausalityRel , A list of present causal relationships Presents
Output: A present causal relationship
1: Function HistoryToPresent (HCausalityRel , Presents)
2: Entities ← getEntities(HCausalityRel ) // Applying TagMe
3: TempEntity ← getTempEntity(Entities) // Temporal entity detection
4: Texts ← replaceTempEntity(HCausalityRel , TempEntity)
5: // Normalizing, tokenizing, lemmatization, removing stop words from the textsof past causal relationship
6: Tokens ← preprocess(Texts)
7: PastFVecs ← makeFVec(Tokens) // Feature vector creation
8: Scores ← []9: for PrstFVec ∈ Presents
10:
BGraph ← createBipartitleGraph(P astF V ecs, PrstFVec) // Bipartite graphconstruction
11:// Measuring causal relationship similarity
12:
Val ← ECM (BGraph)
13:
Scores.append(Val )14: // Return the highest sum of similarity between causal relationships.
15: Top ← top(Scores, Presents)
16: return Top

preprocessing, which includes tokenization, normalization, lemmatization, andthe removal of stopwords, is performed. In line 7, we create the feature vector
for the preprocessed text. In this evaluation, we use the topic vector of the LSA
as the feature vector for each event. Subsequently, we measure the similarityof each combination of present and past causal relationships in lines 8∼13. The
algorithm functions under the assumption that feature vectors for present causal
relationships have already been created using the same procedure as the pastcausal relationship. The feature vectors for the present causal relationships arepresented as arguments. In line 9, the present causal relationships are individually selected, and a bipartite graph is constructed from the feature vectors of thepresent and past causal relationships in line 10. Line 12 measures the similarity
between the two causal relationships on the bipartite graph. Line 13 stores theresult in a list Scores. Finally, after measuring the similarity of all the causal
relationships, line 15 returns the results converted into a form that can actually
be output.

4

Experimental Evaluation

4.1

Setting

Dataset. To evaluate the eﬀectiveness of our algorithm, we used W2E [9], which
comprises present causal relationships, and past causality data [14], which comprises past causal relationships. The W2E dataset is a large dataset comprisingPresent Causal Relationship Retrieval for Historical Analogy

7

news articles published in more than 50 prominent mass media worldwide. Thisdataset includes lists of multiple events that were manually grouped together as
topics. These events were recorded in the current events portal of the English
Wikipedia. Each past causal relationship contains texts on cause, action, andeﬀect. To create a bipartite graph, this study considered the 3 types of text as
separate events.
These datasets allocated categories to all causal relationships. All data in
the W2E dataset were also assigned to one of the following 9 categories: Armed
Conflicts and Attacks (AA), Arts and Culture (AC), Business and Economy(BE), Disasters and Accidents (DA), Health and Environment (HE), Law andCrime (LC), Politics and Elections (PE), Science and Technology (ST) andSport (S). In this study, we extracted 3,038 items from the dataset as output candidates. All past causal relationships were assigned one of the following 13 categories; Reign, Diplomacy, Production, Commerce, Religion, Literature andThought, Technology, Popular Movement, Community, Environment, Study,
War, and Disparity. In this study, 138 items from this dataset were used as
inputs.
In this study, we evaluated the output results based on whether the category names matched as described below. However, the above two datasets wereconstructed separately and therefore had diﬀerent categories. For the evaluation, we used only those categories that commonly existed in the two datasets.
Speciﬁcally, we use the following 5 categories: Diplomacy, Reign, Literature,
Environment, and Technology. As HE and ST in the W2E dataset correspond
to Environment and Technology, respectively, they were directly replaced withthose in the past causality database. The 3 categories of the W2E dataset, BE,
PE, and AC, are named diﬀerently from the above 5 categories; however, theevents in each category were identiﬁed and rewritten as follows. As the eventsin BE, especially those related to the economy, involved more than one country,
they were designated as Diplomacy. The political events in PE were designatedas Reign because they were related to national policies. Finally, numerous worksof literature are described in the text categorized as AC. Thus, this category was
designated as Literature.
Evaluation Criteria. We evaluated retrieval results by whether the input
and output categories were consistent. The actual evaluation criteria used were
macro-averaged precision (P ), recall (R), and F1 value (F 1) for the top case andmean average precision (M AP ) for the top 10 cases.
Baselines. For these datasets, we used three methods as baselines: a method
for ﬁnding the similarity of a set of words using the Jaccard coeﬃcient (Jaccard), a method using LSA and cosine similarity (Cos), and a method using
ECM without temporal entity replacement (ECM). We refer to our algorithm
as temp+ECM in the evaluation. For Jaccard and Cos, we combined all event
texts in a single past causal relationship or topic into a single document.

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K. Matsumaru et al.

Table 1. Scores of macro-averaged precision (P ), recall (R), F1 value (F 1), and mean
average precision (M AP ).

JaccardCos
ECM
temp+ECM

P
39.1%
41.7%
48.9%
54.4%

R
37.5%
39.4%
45.0%
51.4%

F1
37.7%
40.7%
46.2%
53.1%

M AP
64.7%
58.8%
67.2%
71.3%

1.0
Diplomacy
0.8
Reign
0.6
Literature
0.4
Environment
0.2
Technology
0.0
gy
lo
no
ch
Te

t
en
m

n
ro
vi
En

re
tu
ra
te
Li

y
ac
m
lo
ip

n
ig
Re

D

Fig. 2. Matched category ratios resulted by our algorithm.

4.2

Results

Table 1 shows all the scores for the 3 baselines and our algorithm. We conﬁrmedthat the F 1 scores for Jaccard, Cos, ECM, and temp+ECM were 37.7%, 40.7%,
46.2%, and 53.1%, respectively, thereby indicating that our algorithm exhibits
the best performance. In particular, it performed better than the 3 baselines interms of P , R, and F 1 scores by 15∼5.5% points. These results indicate thatwhen searching for causal relationships, the similarity of events in chronologicalorder and temporal entity replacement signiﬁcantly impact the result.
To analyze the results of the proposed algorithm, we studied the percentage
of category matches between the top causal relationship and the input. Fig. 2
shows this result. The vertical and horizontal axes represent the categories ofpast and present causal relationships, respectively. This result indicates that ahigh accuracy was obtained for the categories Diplomacy and Reign.
To analyze the reasons for the low scores in several categories, we measured
the similarity and dependence between categories using the Jaccard coeﬃcientPresent Causal Relationship Retrieval for Historical Analogy

9

0.030
Diplomacy
0.025
Reign

0.020

Literature

0.015
0.010

Environment

0.005
Technology
0.000

n
ch
Te
y

t
en
nm

og
ol

iro

v
En

e

r
tu
ra
te
Li

y
ac
m
lo
ip

n
ig
Re

D

Fig. 3. Category similarities by Jaccard

and mutual information (MI) deﬁned as follows:| TA ∩ TB || TA ∪ TB |)(∑∑p(a, b)
M I(A, B) =p(a, b) log
p(a)p(b)Jaccard (A, B) =

(1)
(2)

a∈Ab∈B

where | · | represents the size of the set “·”. TA and TB are tokens used in categories A and B, respectively. The higher the Jaccard coeﬃcient score, the moresimilar the two arguments are. The higher the MI score, the more dependent thetwo arguments are.
Figs. 3 and 4 show these scores. For the 3 categories of Literature, Technology,
and Environment, the scores between sentences in the same category were lower
than those for Diplomacy and Reign. In addition, the MI scores of Literature,
Environment, and Technology between Diplomacy and Reign tend to be relatively high. We manually checked the incorrectly retrieved results and foundthat the range of events covered by categories was not strictly consistent between the past and present datasets, even if the categories had the same name.
For example, the Diplomacy in the present dataset included news related tointernational relations and economics; however, these news events also involved
domestic governance and foreign invasions. Table 2 shows an example of category
mismatch. This example shows that both past and present events are related toeconomics; however, they were assigned the categories of Diplomacy and Reign,respectively. Such discrepancies could be reduced if potential semantic matches
between categories of diﬀerent datasets could be detected and converted into
multi-label datasets.

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K. Matsumaru et al.
0.6
Diplomacy
0.5
Reign

0.4

Literature

0.3
0.2

Environment

0.1
Technology
0.0

og
ol
y

t
en
m

n
iro

n
ch
Te

v
En

e

r
tu
ra
te
Li

y
ac
m
lo
ip

n
ig
Re

D

Fig. 4. Category dependency by MI

Finally, we checked the MAP scores for the 4 algorithms. The results arepresented in Table 1. We can see that the MAP score of the proposed algorithm was also higher than those of the baselines. In particular, a comparisonof the proposed algorithm and ECM shows that the accuracy is increased byapproximately 4% points due to temporal entity replacement.

5

Conclusions & Future WorkIn this study, we proposed an algorithm that retrieves present causal relationships that are similar to the past causal relationships that are input. This algorithm replaces the entities limited to speciﬁc periods with their types. Subsequently, it measures the similarity of causal relationships that include multiple
events sorted in chronological order. To conﬁrm the eﬀectiveness of the proposed
algorithm, we compared the F 1 and M AP scores for our algorithm against thebaselines and found that our algorithm obtained the best scores.
In future work, we hope to propose a method to gauge the potential meaningsof categories in diﬀerent datasets, as discussed in the evaluation. This approach
could improve search accuracy in situations where diﬀerent categories should bedeﬁned for diﬀerent years; however, the search would be conducted using all thedatasets to search over a broad range of time. Furthermore, we hope to developa learning environment that incorporates this search algorithm. We also envision
creating a UI that aids students in inputting sentences that consider causality. In
this manner, our proposed algorithm can be deployed in actual classes. Finally,
we hope to propose an eﬀective curriculum incorporating this learning environment into history classes. Further research is also required to conﬁrm whether

Present Causal Relationship Retrieval for Historical Analogy

11

Table 2. Example of mis prediction.

1.
2.

3.

1.

Past (Diplomacy)
Sparta comprised Spartan citizens (a minority) and slaves (the majority).
The former were always fearful of a revolt by the latter. Eﬀorts were madeto maintain unity among the citizens: the use of currency was banned so that
wealth disparities would not arise among them; land was distributed equally
to make all entirely equal; and the country was closed oﬀ to prevent outside
inﬂuences.
Thus, the unity of Spartan citizens was heightened, and Sparta became thestrongest Greek army state.
Present (Reign)
The European Central Bank reinstates the waiver that enables Greek banks
to use the country’s bonds as collateral for regular loans, for the ﬁrst time inmore than a year.

the proposed search algorithm facilitates the formation of historical analogies in
actual classrooms.
Acknowledgments. This work was supported in part by MEXT Grant-in-Aids(#22K02884).

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