#  KoCHET: a Korean Cultural Heritage corpus for Entity-related Tasks

Gyeongmin Kim, Jinsung Kim11footnotemark: 1, Junyoung Son11footnotemark: 1,
Heuiseok Lim
Korea University, Korea
{totoro4007, jin62304, s0ny<EMAIL_ADDRESS>These authors have equally
contributed to this work Corresponding author

###### Abstract

As digitized traditional cultural heritage documents have rapidly increased,
resulting in an increased need for preservation and management, practical
recognition of entities and typification of their classes has become
essential. To achieve this, we propose KoCHET\- a Korean cultural heritage
corpus for the typical entity-related tasks, i.e., named entity recognition
(NER), relation extraction (RE), and entity typing (ET). Advised by cultural
heritage experts based on the data construction guidelines of government-
affiliated organizations, KoCHET consists of respectively 112,362, 38,765,
113,198 examples for NER, RE, and ET tasks, covering all entity types related
to Korean cultural heritage. Moreover, unlike the existing public corpora,
modified redistribution can be allowed both domestic and foreign researchers.
Our experimental results make the practical usability of KoCHET more valuable
in terms of cultural heritage. We also provide practical insights of KoCHET in
terms of statistical and linguistic analysis. Our corpus is freely available
at https://github.com/Gyeongmin47/KoCHET.

##  1 Introduction

Recently there has been an increasing interest in the preservation of national
historical artifacts and traditional cultural heritage, and also grows up the
importance of effective management of them through digitization and archival.
As the amount of digitized information materials increases rapidly,
information extraction (IE) tasks in natural language processing (NLP), such
as named entity recognition (NER), relation extraction (RE), and entity typing
(ET), have become an essential and fundamental step in the field of historical
document analysis.

Despite the necessity of a well-refined entity-centric corpus specialized in
domestic cultural heritage, unfortunately, there no exists any cultural
heritage domain-specialized corpus in Korean. Moreover, conventional entity-
related systems deal only with a coarse set of entity types such as person,
location, and organization which is significantly limited in terms of
application. This absence of cultural heritage domain-specialized corpus and
narrow coverage of entity types hinders the effective digitization of domestic
historical documents because training the model with general corpus for
entity-related tasks cannot afford to learn enough significant entity types
such as pagodas, historical sites and intangible heritage, and their
relations. Furthermore, not in the cultural heritage domain, the existing
entity-related datasets supervised by the public institutions have a
complicated procedure for data acquisition, and they are also restricted from
modification and redistribution. These cumbersome procedures and restrictions
have been stumbling blocks for researchers against the rapid increase in
digitized cultural heritage materials over the past few decades.

To address these difficulties against the conservation of Korean cultural
heritage, we introduce a new dataset collection called KoCHET\- Korean
Cultural Heritage corpus for Entity-related Tasks, a high-quality Korean
cultural heritage domain-specialized dataset for NER, RE, and ET tasks. For
corpus construction, we crawled the e-museum digitized data of the National
Museum of Korea111https://www.emuseum.go.kr/ (including data from all 50
museums) as the source text which is for the interested public. We selectively
used resources from the museums in which the details of artifacts were
registered; moreover, for the completeness of the attribute data, we limited
the chronological range of the data from the prehistoric era to the Korean
Empire era, excluding the Japanese colonial period. For the annotation, the
categorization for classes and attributes appropriate was defined and
developed following the 2020 Named Entity Corpus Research
Analysis222https://www.korean.go.kr which was published under the guidelines
as institutional organizations.

As our corpus focuses on the entity features, it has more detailed and
abundant entity types including diverse cultural heritage artifacts, compared
to the existing accessible datasets that aim to deal with several downstream
tasks in addition to entity-related tasks. Furthermore, the ET of KoCHET is
the first freely available corpus for the ET task in Korea. In addition to
providing these values, this paper provides detailed statistics and linguistic
analysis of KoCHET for each entity-related task to demonstrate their
applicability and enhance understanding of the data, along with baseline
experiments with language models.

Our contributions are summarized as follows:

  * •

We introduce KoCHET designed for entity-related tasks. This guarantees a high-
quality corpus without restrictions regarding modification and redistribution.
Moreover, to the best of our knowledge, the ET corpus is the first proposed
corpus in Korean.

  * •

We categorized the detailed entity types specialized in the cultural heritage
domain, which is essential for preserving our cultural and historical
artifacts, thereby contributing as an alternative to the increased demand for
the digitalized archiving of cultural heritage documents.

  * •

We prove the applicability of our entity-abundant corpus in each task by
providing statistics and linguistic analysis, along with the experiments with
pre-trained language models.

##  2 Related Works

As domains that require expertise, such as the cultural heritage, contain
entities or relationships that rarely appear in general domains, the necessity
of a corpus specialized in the domain is obvious. Despite such demand, Korean
does not yet have a corpus specialized in the cultural heritage area, unlike
other languages.

###  2.1 General cultural heritage corpora

There have been the disclosures of corpora in an effort to preserve
traditional culture including the cultural heritage, composing data from the
perspective of the entity-related tasks that we deal with. For example, these
include a Czech NER corpus constructed based on public optical character
recognition data of Czech historical newspapers (Hubková et al., 2020), a
Chinese corpus suitable for the computational analysis of historical lexicon
and semantic change (Zinin and Xu, 2020), and an English corpus that is one of
the most commonly used large corpora in diachronic studies in English
(Alatrash et al., 2020).

###  2.2 Korean public corpora

##### The National Institute of Korean Language

, which is an institution that has established the norms for Korean
linguistics, constructed a large-scale dataset333https://stdict.korean.go.kr/
for the study of new computational linguistics of Korean (Kim, 2006).

##### AI HUB

is a massive dataset integration platform444https://aihub.or.kr/ hosted by the
National Information Society Agency (NIA)555https://www.nia.or.kr/, a
government-affiliated organization. To support the development of the Korean
artificial intelligence industry for the NLP field, the NIA disclosed domain-
specific corpora and 27 datasets have been released or are being prepared.

##### Electronics and Telecommunications Research Institute

, as part of the Exo-brain
project666http://exobrain.kr/pages/ko/result/outputs.jsp, provides corpora for
NLP tasks such as morphological analysis, entity recognition, dependency
parsing, and question answering, and guidelines for building such high-quality
corpora777https://www.etri.re.kr/. In addition to public datasets opened by
public institutions, there is a Korean dataset publicly available for free
without the requirement for an access request.

##### Korean Language Understanding Evaluation (KLUE)

dataset was recently released to evaluate the ability of Korean models to
understand natural languages with eight diverse and typical tasks (Park et
al., 2021b). The tasks include natural language inference, semantic textual
similarity, dependency parsing, NER, and RE.

##  3 KoCHET

Following the guidelines of Korean institutional organizations, KoCHET is a
domain specialized corpus for cultural heritage, which ensures quality and can
be freely accessed. In this section, we report the annotation process and
guidelines in detail.

###  3.1 Annotation Process

To improve the quality of annotations on our entity-rich corpus related to
cultural heritage, we conducted the annotation process based on expertise in
the cultural heritage domain.

##### Annotation Guidelines

The raw corpus annotated by each annotator is equally divided by the category.
The annotators were instructed to follow two types of rules by the
aforementioned entity guidelines in Section 1; one is related to tagging units
and categories, and the other is the principle of unique tagging. The minimum
unit is based on one word for the tagging units and categories. In addition,
it is applied only to cases written in Korean, where the notation is possible.
It is not tagged in the case of Chinese characters and English, but if it is
read in Korean, it is included in the tagging range. For the principle of
unique tagging, there are cases of duplication in entities that belong to two
or more semantic regions. This guideline grants a single tag to a semantically
suitable word and refers to assigning only one tag by prioritizing it
accordingly. There are two cases in which this principle should be applied.
The first case is where the entity belongs to two semantic categories
regardless of the context. The second refers to the case where it may vary
depending on the context. In both cases, tagging is determined according to
the pre-defined priority.

##### Annotator Training and Cross-Checking

We recruited 34 college and graduate annotators who have been professionally
educated on the cultural heritage domain in Korea to participate in the
annotation process. All annotators were trained for a week, and each of them
was familiarized with the annotation guideline and conducted practice
annotation on test samples. The annotation team met once every week to review
and discuss each member’s work during the annotation process. All entity types
and relations were reviewed by four cross-checking annotators, afterward, were
additionally checked by two expert supervisors. The discrepancy between
annotators on the annotated entity types and relations is also discussed and
agreed upon in the period. These procedures allowed the reliability and
validity of KoCHET on the cultural heritage objects to be improved.

###  3.2 Schema for Task Annotation

####  3.2.1 Named Entity Recognition

Label | Train | Dev | Test
---|---|---|---
Counts (%)
Artifacts (AF) | 91,453 (35.57) | 11,374 (35.54) | 11,366 (35.35)
Person (PS) | 51,758 (20.13) | 6,455 (20.17) | 6,744 (20.97)
Term (TM) | 25,781 (10.02) | 3,175 (9.92) | 3,159 (9.82)
Date (DT) | 23,636 (9.19) | 2,943 (9.20) | 3,078 (9.57)
| Political
---
location (LCP)
20,076 (7.80) | 2,375 (7.42) | 2,384 (7.41)
Civilization (CV) | 15,404 (5.99) | 1,929 (6.03) | 1,835 (5.71)
Material (MT) | 8,893 (3.45) | 1,160 (3.62) | 1,046 (3.25)
Location (LC) | 6,881 (2.67) | 857 (2.68) | 881 (2.74)
Animal (AM) | 4,376 (1.70) | 578 (1.81) | 566 (1.76)
Plant (PT) | 3,952 (1.53) | 549 (1.72) | 498 (1.55)
| Geographical
---
location (LCG)
2,821 (1.09) | 354 (1.11) | 348 (1.08)
Event (EV) | 2,045 (0.79) | 254 (0.79) | 248 (0.77)

Table 1: The counts of entities and their distributions (%) in our NER data.

As described in Table 1, we defined 12 entity types. They were tagged with the
character-level beginning-inside-outside (BIO) tagging scheme, which is the
generally adopted method for sequence labeling problems. For example, “아시아
(Asia): Geographical Location (LCG)” is tagged as “아: B-LCG,” “시: I-LCG,” “아:
I-LCG.” Therefore, we evaluated the model not only with entity-level F1 score
but also with character-level F1 score (Park et al., 2021b).

##### Label Description

  * •

Artifacts (AF) generally refer to objects created by humans corresponding to
common and proper nouns and also include cultural properties. Therefore,
artificial materials such as buildings, civil engineering constructions,
playground names, apartments, and bridges fall under this category.

  * •

Person (PS) is a category for content related to people, including real
persons, mythical figures, fictional characters in games/novels, occupations,
and human relationships.

  * •

Term (TM) includes the color, direction, shape, or form that describes an
artifact. Patterns and drawings are classified as TM, owing to the
characteristics of movable cultural properties.

  * •

Civilization (CV) is defined as terms related to civilization/culture. It
targets words classified by detailed civilizations/cultures, such as clothing
and food.

  * •

Date (DT) includes all entities related to date and time, such as date,
period, specific day, or season, month, year, era/dynasty. However, in the
case of an unclear period that cannot be tagged with a separate entity,
tagging is not performed.

  * •

Material (MT) includes a substance used as a material or an expression for the
substance. In other words, it indicates the entity corresponding to the
detailed classification of a substance (metal, rock, wood, etc.). When an
entity can be tagged as both natural objects (AM, PT) and MT, tagging as MT
takes precedence.

  * •

Geographical location (LCG), Political location (LCP), and Location (LC) are
defined as geographical names, administrative districts, and other places,
respectively.

  * •

Animal (AM) and Plant (PT) are defined as animals and plants, respectively,
excluding humans. If it is applied as a subject of a picture, it is also
included in the category of animals and plants.

  * •

Event (EV) contains entities for a specific event/accident. In principle,
social movements and declarations, wars, revolutions, events, festivals, etc.,
fall under this category and should be classified only if they exist as a
separate entity.

####  3.2.2 Relation Extraction

Unlike the other existing corpora, our corpus has the advantage of capturing
various relationships between multiple entities that are included in a
sentence because more than one relation can exist per raw sentence. We
consider the relations between annotated entities in the NER annotation
procedure. In the case of certain tokens, it can be a subject or an object
depending on the relationship with other tokens. A relationship in the form of
a self-relationship between identical tokens does not exist.

Label | Train | Dev | Test
---|---|---|---
Counts (%)
A depicts B | 14,157 (22.09) | 1,803 (22.45) | 1,711 (21.85)
A documents B | 10,214 (15.94) | 1,244 (15.49) | 1,220 (15.58)
A hasSection B | 6,542 (10.21) | 818 (10.19) | 776 (9.91)
A servedAs B | 6,546 (10.22) | 780 (9.71) | 740 (9.45)
A hasCreated B | 6,136 (9.58) | 759 (9.45) | 744 (9.50)
A OriginatedIn B | 5,456 (8.51) | 679 (8.45) | 663 (8.47)
A consistsOf B | 4,331 (6.76) | 569 (7.09) | 586 (7.48)
A isConnectedWith B | 3,489 (5.44) | 501 (6.24) | 461 (5.89)
A fallsWithin B | 3,454 (5.39) | 415 (5.17) | 483 (6.17)
A isUsedIn B | 1,906 (2.97) | 238 (2.96) | 244 (3.12)
A hasTime B | 934 (1.46) | 111 (1.38) | 95 (1.21)
A wears B | 798 (1.25) | 97 (1.21) | 86 (1.10)
A hasCarriedOut B | 112 (0.17) | 15 (0.19) | 19 (0.24)
A hasDestroyed B | 5 (0.01) | 2 (0.02) | 3 (0.04)

Table 2: Relation counts and distributions (%) for our RE corpus. Figure 1:
Visualization of all the labels that cover 84% of the entity types is shown on
the left side, and 106 general and fine-grained entities with their
distributions (%) are shown on the right side.

As shown in Table 2, our RE corpus consists of 14 labels, and these were
defined based on the Encyves ontology research of the National Culture
Research Institute888http://dh.aks.ac.kr/Encyves/wiki.

##### Label Description

  * •

“A depicts B” implies the relationship between an object and its color, shape
or pattern, etc. For example, “Green Door” corresponds to this relationship.
It can also represent a descriptive relationship such as “Picture of a place-
the place where it was taken” or “Picture of a person-the person who is the
object of the painting.”

  * •

“A documents B” implies “$\scriptstyle\sim$ records -.” ;a relationship such
as “Record-The person who records it” can be represented by this. It also
indicates the relationship like a record written on an object such as
“Postcard-Explanation" or a specific language written on a document such as
“Record-Chinese characters.”

  * •

“A hasSection B” indicates “$\scriptstyle\sim$ is located at -.” It represents
the relationship between a statue, building, or specific attraction and a
location, such as a certain city and place.

  * •

“A servedAs B" implies “$\scriptstyle\sim$ is the role of -,” which
corresponds to the relationship between a person, and his/her position or
occupation, etc.

  * •

“A hasCreated B” demonstrates, for example, “Person-Documents” or “Person-
Painting,” which refers to the relationship between a person and a document
such as a book, map, or drawing, or his/her activities to record works.

  * •

“A OriginatedIn B” means “$\scriptstyle\sim$ is discovered at –” or
“$\scriptstyle\sim$ is produced at -(time).” It indicates that cultural
property is produced at a specific time such as “Craft-Year" or is discovered
at a particular place such as “Object-Place," or is produced at a certain site
such as “Document-Place." For example, the relation between earrings and tombs
or a newspaper and the company of the newspaper fall into this.

  * •

“A consistsOf B” refers to the relation between an object and its raw
ingredients, such as soil, iron, and wood that constitute an object.

  * •

“A isConnectedWith B” represents a person-to-person association. The
relationships between two positions or a person and the position he or she
holds do not fall into this.

  * •

“A fallsWithin B” implies “$\scriptstyle\sim$ is denominated as -.” It
indicates the relationship of alternate names such as “Person-Specific name,"
or between a name and designation in front of the name, or between words that
refer to synonymous concepts such as “Verse-Poetry.”

  * •

“A isUsedIn B” indicates “$\scriptstyle\sim$ is used for the purpose of -” or
literally “$\scriptstyle\sim$ is used in -.” For example, it can also indicate
the material used for a certain object, such as “Raw material-Clothes.” The
relationship between an object and the place where the object is used, such as
a signboard and a palace, or the relationship between certain means of
performing a function and an object such as “Bowl-Rice cake” can correspond to
this category.

  * •

“A hasTime B” implies “$\scriptstyle\sim$ has happened at -.” For example, it
can indicate the relationship between a particular event and a specific date,
such as “Presidential election-1928." The relation between a specific date and
a certain work, such as the year of production of a work and the year of
construction of a building, can fall under this category, for example, “Year-
Craftwork."

  * •

“A wears B” implies “$\scriptstyle\sim$ puts - on.” For instance, not only
clothes such as school uniforms but also crafts, etc. may correspond to the
object argument.

  * •

“A hasCarriedOut B” indicates “- is caused by $\scriptstyle\sim$.” It can
represent a relationship between a specific organization or group and an event
conducted by it, such as a festival or social movement.

  * •

“A hasDestroyed B” implies the event that caused destruction such as “War-
Destroyed place," or the collapse of a country in a specific year such as
“Country-Year,” or the relationship in which a building, structure, monument,
etc. is destroyed at a particular period.

Sentence with Entity Mention | Entity Types
---|---
|  조선시대에는 전통 관습을 잇기 위한 많은 향로가 제작되었다.
---
(In the Joseon dynasty, many fragrance burners were created
for traditional customs.)
|  DT_DYNASTY, DT_DURATION
---
LCP_COUNTRY, LCP_CITY, LCP_COUNTY
LC_OTHERS, AF_DOCUMENTS
| 노란 바탕의 모란이 양쪽에 그려져 있다.
---
(The yellow background peony is drawn on both sides.)
|  PT_FLOWER, PT_TYPE, PT_OTHERS,
---
TM_SHAPE
|  19세기 후반 청주의 재정을 파악할 수 있는 자료가 있다.
---
(There are data to comprehend the finances of Cheongju in
the late 19th century.)
|  DT_YEAR, DT_DYNASTY, DT_DURATION
---

Table 3: Examples including entity mentions and their fine-grained entity
types. Entity mentions and the correct types in the given context are bold.
All fine-grained entity types are shown in Figure 1.

####  3.2.3 Fine-grained Entity Typing

Given a sentence and entity mention within it, the ET task predicts a set of
noun phrases that describe the mention type. For example, in “김홍도는 조선 후기의
화가이다. (Kim Hong-do was a painter of the Joseon era of Korea.),” Joseon should
be typed as “dynasty/Date” and not “country/Location.” This typification is
crucial for context-sensitive tasks such as RE, coreference resolution, and
question answering (e.g., “In which era was Kim Hong-do, an artist?”). Unlike
high resource languages, we found that the Korean corpus for the ET task has
not been released. In dealing with this data scarcity problem and promoting
universal studies, we release a Korean ET task corpus for the first time, to
the best of our knowledge.

The schema for the ET task was designed with reference to the data
construction process of the Fine-Grained Entity Recognition dataset (Ling and
Weld, 2012). Considering the properties of the cultural heritage domain, we
categorized the 12 general entity types aforementioned in the NER task
(Section 3.2.1) into a fine-grained set of 94 types with detailed meanings.
Particularly, the cultural taxonomy defined in the Cultural Properties
Protection Law999www.cha.go.kr was applied to AF, and the 2004 Cavalier-
Smith’s classification system (Cavalier-Smith, 2004) was applied to the
biological scope of PT and AM. All fine-grained entity types are detailed in
Figure 1.

The fine-grained entities for entity-related downstream tasks in the cultural
heritage domain enable a more detailed contextualized representation for each
entity mention than the previous typing schemas, which only predict relatively
coarse types of entities. Table 3 lists three example sentences with entity
mention that can represent several fine-grained types. Given a sentence with
an entity mention, the appropriate type that describes the role of the entity
span in the sentence should be predicted. Our fine-grained entity types can
embrace all the existing general types and categorize them in greater detail.
Accordingly, they can let models understand richly the noun phrases including
entity, compared to when the models are trained to predict only relatively
coarse types. For Figure 1, the circle on the left shows the visualization of
fine-grained entity types that possess approximately 84% among all labels in
the corpus, and the set on the right shows the detailed distributions of all
fine-grained types. Each example includes 2.94 fine-grained entities on
average; there are up to nine several fine-grained entity types per entity.
The category to which the most entities belong is “AF_DOCUMENTS," which
possesses 17.9%, and that on the second place is “PS_NAME," having 16.7%.

##### Label Description

  * •

12 general types: PS, AF, AM, CV, DT, EV, PT, MT, TM, LC, LCG, LCP

  * •

94 fine-grained types, which were mapped to the cultural heritage-specialized
fine-grained entity labels, were inspired by prior works (Ling and Weld, 2012;
Gillick et al., 2014; Choi et al., 2018).

Index |  Example sentences
---|---
1 |  앞면 좌측 하단에 ‘한번사신레꼬-드는승질상밧고거-나믈느지는안슴니다’ 문구가 있음.
2-2[0.8pt/1.2pt] |  There is a phrase ‘한번사신레꼬-드는승질상밧꼬거-나믈느지는안슴니다’(archaic Korean) in the left corner of the front side.
2 |  1면에는 안창호씨(安昌浩氏)의 연설, 편집실 여언(餘言) 등의 기사가, $\cdot\cdot\cdot$, 인쇄됨.
2-2[0.8pt/1.2pt] |  On the first page, articles such as Mr. Changho Ahn(安昌浩氏)(Chinese character)’s speech and editorial comments(餘言)(Chinese character), $\cdot\cdot\cdot$, were printed.
3 |  ‘戰爭の訓示’, $\cdot\cdot\cdot$, 등의 기사와 일본 언어학자 가나자와 쇼자부로(金澤庄三郞, 1872$\sim$1967)의 현대 국어 음운에 대한 연구물인 「朝鮮語發音篇」의 일부를 게재함.
2-2[0.8pt/1.2pt] |  ‘戰爭の訓示(Japanese)’, $\cdot\cdot\cdot$, the articles and 「朝鮮語發音篇」(Chinese character), the part of a study on the modern Korean phonology of Japanese linguist Kanazawa Shouzaburou(金澤庄三郎(Chinese character), 1872∼1967) were published.

Table 4: Example sentences contained in our corpus. These examples include not
only Korean but also Japanese and Chinese characters. Also, they contain
archaic expressions that are not used in modern times. These characteristics
make it more suitable for the learning of cultural heritage domain. Note that
we omitted some of the words in the sentence for brevity.

###  3.3 Analysis on KoCHET

####  3.3.1 Diachronic and Linguistic Analysis

There are mainly two differences between the entities in the proposed corpus
and those commonly used.

First, archaic expressions that are not used in modern times are frequently
shown in our corpus. Specifically, such expressions continually appear when
ancient documents or historical artifacts are quoted. Let us consider the
phrase “한번사신레꼬-드는승질상밧고거-나믈느지는안슴니다” in sentence 1 in Table 4. Although it is
written using syllables of modern Korean, the grammar and the vocabulary are
fairly dissimilar from those of contemporary Korean, such as word spacing and
syllabification, i.e., separation rule between the units of the word. When
translating the sentence with quotation marks into modern Korean, it can be
expressed as “한번 사신 레코드는 성질상 바꾸거나 무르지는 않습니다 (Once a record is purchased, it
cannot be exchanged or refunded due to its characteristics)."

Second, several entities contained in KoCHET written in Korean are followed by
the descriptions written in either Chinese or Japanese characters. For
example, as shown in sentence 2 in Table 4, the description with Chinese
characters in parentheses follows the entity “안창호씨," and is usually written
such as “안창호씨(安昌浩氏)." Further, Japanese characters are also present throughout
the corpus, enhancing the polyglot property of the corpus, as shown in
sentence 3. Therefore, to fully understand such expression types in our
corpus, multilingual factors of language models should be considered;
particularly in the case of token classification tasks, in which the meaning
of each token directly affects the model performance.

| Task
---
Train | Dev | Test
NER | # of examples | 89,884 | 11,245 | 11,233
# of entities | 393,076 | 32,003 | 32,153
RE | # of examples | 31,012 | 3,876 | 3,877
# of relations | 64,080 | 8,031 | 7,831
ET | # of examples | 90,558 | 11,320 | 11,320
# of mentions | 266,209 | 33,226 | 33,395

Table 5: Statistics of KoCHET for each task.

Model | NER | RE | ET
---|---|---|---
Entity F1 ($\sigma$) | Character F1 ($\sigma$) | F1 ($\sigma$) | F1 ($\sigma$)
Multilingual fine-tuned Models
Multilingual BERT | 59.81 (0.09) | 71.80 (0.12) | 80.85 (0.39) | 91.64 (0.10)
XLM-RoBERTa-base |  76.57 (0.13) |  82.69 (0.09) | 80.29 (0.53) | 91.13 (0.16)
Korean fine-tuned Models
KLUE-BERT-base | 39.31 (0.10) | 55.63 (0.15) |  82.44 (0.18) |  93.08 (0.27)
KLUE-RoBERTa-base | 38.92 (0.28) | 55.47 (0.21) | 82.42 (0.57) | 92.80 (0.17)

Table 6: Experiments results on the NER, RE, and ET tasks. F1 score (%) is
used for the evaluation metric with $\sigma$ which shows the standard
deviation of the score. We divide the baseline models into two parts: the
Multilingual models and the Korean models, marking the highest performances
with bold text.

####  3.3.2 Statistics

The overall statistics of KoCHET are showed in Table 5. For the NER corpus,
457,232 entities from 112,362 examples in total. For the RE corpus, 79,942
relations from 38,765 examples were annotated in total. For the ET corpus,
332,830 entity mentions from 113,198 examples were annotated in total. The
annotated corpus was divided into three subsets for each task, i.e., a ratio
of 8:1:1 for training, development, and testing, respectively. In this
section, we describe our corpus statistically in the order of NER, RE, and ET.

First, as shown in Table 1, we used 12 entity types for our cultural heritage
NER corpus. Due to the properties of the cultural heritage domain, the three
primary entity types, i.e., artifacts (AF), person (PS), and term (TM),
account for the majority of the total entity population. AF, PS, and TM
entities possess approximately 36%, 20%, and 10%, respectively, which are used
as crucial information in the cultural heritage domain. The AF type includes
cultural assets and historical landmarks, the TM type includes patterns or
traces engraved on certain cultural assets, and the PS type particularly
includes not only general people but also particular types of persons such as
mythical figures. On the other hand, the EV type occupies the most minor
proportion, approximately 0.8%, because our corpus especially aims to
concentrate on the cultural heritage.

Second, Table 2 demonstrates the distribution of 14 RE labels. In the case of
“A depicts B” and “A documents B,” cultural assets left in a specific form
such as records, drawings, and photographs are included, whereas “A hasSection
B" contains cultural heritage or historical landmarks located at a specific
place. Among them, “A depicts B,” “A documents B,” and “A hasSection B” are
the most relationship labels with approximately 22%, 16%, and 10% of the
total, respectively. “A depicts B” and “A documents B" include cultural assets
left in a specific form such as records, drawings, and photographs, whereas “A
hasSection B" contains cultural heritage or historical landmarks located at a
particular place. “A hasDestroyed B" has the smallest proportion with ten
relations in total because, in actual history, significant events such as the
collapse of a nation or the loss of cultural properties are not as diverse as
the types of general cultural assets.

Finally, among the fine-grained entity types, the “AF_DOCUMENTS" type, such as
historical documents, occupies the largest part with 17.9%, and “PS_NAME"
including the names of historical figures, takes second place by occupying
11.5%. On the other hand, the entity types to which belong to the AM, PT, MT,
and EV almost account for under 1.0%.

##  4 Experiment

The detailed experimental settings are in Appendix A.

##### Experimental results

According to Table 6, two tendencies are observed. One is that in the NER
task, the multilingual models, i.e., multilingual BERT and xlm-RoBERTa-base,
showed better performance by more than 30% difference in both Entity F1 and
Character F1 scores compared to the Korean models, i.e., KLUE-BERT-base and
KLUE-RoBERTa-base. The other is that in the RE and ET tasks, the performances
of the Korean models were at least 1.1% higher than those of the multilingual
models.

##### Experimental Analysis

As the token classification tasks are directly affected by segmentation (Kim
et al., 2021; Park et al., 2021a), models with linguistic knowledge of Chinese
and Japanese overperform in such tasks (Pires et al., 2019). In other words,
the multilingual models are considered to segment better each token composed
of various languages, especially in the NER corpus. In addition, in Table 7,
the Korean models, i.e., KLUE-BERT-base and KLUE-RoBERTa-base show a
significantly higher ratio of unknown tokens than the multilingual language
models. It is attributed that the NER task requires more polyglot features of
the model compared to the other tasks, i.e., RE and ET, which has the
properties of sentence classification tasks. On the other hand, as the RE or
ET task does not classify all tokens in a sentence, the correct answer can be
satisfactorily inferred from only the given Korean words; thereby, the
language models pre-trained in Korean show better performance in the two tasks
compared to the multilingual model.

Model |  | UNK_dev (%)
---
| UNK_test (%)
---
Multilingual BERT | 0.8156% | 0.7684%
XLM-RoBERTa-base | 0.1952% | 0.1810%
KLUE-BERT-base | 5.8670% | 5.9677%
KLUE-RoBERTa-base | 5.8670% | 5.9677%

Table 7: Unknown (UNK) token ratio (%) of each model for development and
testing set in the corpus. Baseline models pre-trained in Korean show the same
proportions because they use identical vocabulary and tokenizers.

##  5 Conclusion

In this paper, we introduced KoCHET \- a Korean cultural heritage corpus for
three typical entity-related tasks, i.e., NER, RE, and ET. Unlike the existing
public Korean datasets with additional restrictions, KoCHET obviated the
cumbersome prerequisite and can be freely modified and redistributed.
Furthermore, we proved the applicability of our entity-abundant corpus with
the experiments employing the various pre-trained language models and provided
practical insights regarding the statistical, diachronic, and linguistic
analysis. Above all, the most significant contributing point is that the
disclosure of our corpus is expected to serve as a cornerstone for the
development of IE tasks for a traditional cultural heritage. We hope that the
continuous effort to preserve cultural heritage with the effective management
of digitized documents containing cultural artifacts is encouraged by this
research.

## Acknowledgements

This research is supported by Ministry of Culture, Sports and Tourism and
Korea Creative Content Agency(Project Number: R2020040045), MSIT(Ministry of
Science and ICT), Korea, under the ITRC(Information Technology Research
Center) support program(IITP-2018-0-01405) supervised by the IITP(Institute
for Information & Communications Technology Planning & Evaluation), and
Institute of Information & communications Technology Planning &
Evaluation(IITP) grant funded by the Korea government(MSIT) (No. 2020-0-00368,
A Neural-Symbolic Model for Knowledge Acquisition and Inference Techniques).

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##  Appendix A Experimental Setup

As the baseline models, we employed two global language models: multilingual
bidirectional encoder representations from transformers (BERT) (Devlin et al.,
2019) and a cross-lingual language model XLM-RoBERTa-base (Conneau et al.,
2020) containing the Korean language, and two KLUE language models: KLUE-BERT-
base, KLUE-RoBERTa-base, which were recently published covering various Korean
downstream tasks. In all the model experiments, the performance of each model
was measured five times, and the average of each result was evaluated as the
final result. Further, we set our environment for the experiment with four
A6000 GPUs and 384 GB memory. The hyperparameters in the fine-tuning step were
set as follows. The learning rate and weight decay were consistently set at
5e-5 and 0.01 across all three tasks. The number of training epochs was set to
10 in NER, RE and 3 in ET. The batch size in training and testing procedures
was set to 128 in NER, RE and 256 in ET. In the case of max sequence length,
the lengths of 256 and 128 were used for each task.

We evaluated our system by employing F1 score, which is standard metric for
classification tasks. Specifically, the evaluation metrics for NER task were
Entity F1 and Character F1 based on previous research (Park et al., 2021b).
Entity F1 is a metric that is recognized as a correct answer only when all
types included in an entity are matched accurately. Conversely, Character F1
is a metric that evaluates each type of syllable in a sentence individually.
The evaluation metrics for the RE task were F1 score in the Scikit-learn
library (Pedregosa et al., 2011). As for ET, we adopted the evaluation metrics
of loose F1 score following the same evaluation criteria used in previous
works (Ling and Weld, 2012; Wang et al., 2020).


11institutetext: Dao Thanh Hai 22institutetext: School of Science, Engineering
and Technology, RMIT University Vietnam 22email<EMAIL_ADDRESS>33institutetext: Isaac Woungang 44institutetext: Department of Computer
Science, Toronto Metropolitan University, Toronto, ON, Canada 44email:
<EMAIL_ADDRESS>
# On Network Design and Planning 2.0 for Optical-computing-enabled Networks

Dao Thanh Hai  Isaac Woungang

###### Abstract

In accommodating the continued explosive growth in Internet traffic, optical
core networks have been evolving accordingly thanks to numerous technological
and architectural innovations. From an architectural perspective, the adoption
of optical-bypass networking in the last two decades has resulted in
substantial cost savings, owning to the elimination of massive optical-
electrical optical interfaces. In optical-bypass framework, the basic
functions of optical nodes include adding (dropping) and cross-connecting
transitional lightpaths. Moreover, in the process of cross-connecting
transiting lightpaths through an intermediate node, these lightpaths must be
separated from each other in either time, frequency or spatial domain, to
avoid unwanted interference which deems to deteriorate the signal qualities.
In light of recently enormous advances in photonic signal processing /
computing technologies enabling the precisely controlled interference of
optical channels for various computing functions, we propose a new
architectural paradigm for future optical networks, namely, optical-computing-
enabled networks. Our proposal is defined by the added capability of optical
nodes permitting the superposition of transitional lightpaths for computing
purposes to achieve greater capacity efficiency. Specifically, we present two
illustrative examples highlighting the potential benefits of bringing about
in-network optical computing functions which are relied on optical aggregation
and optical XOR gate. The new optical computing capabilities armed at optical
nodes therefore call for a radical change in formulating networking problems
and designing accompanying algorithms, which are collectively referred to as
optical network design and planning 2.0 so that the capital and operational
efficiency could be fully unlocked. To this end, we perform a case study for
network coding-enabled optical networks, demonstrating the efficacy of
optical-computing-enabled networks and the challenges associated with greater
complexities in network design problems compared to optical-bypass
counterpart.

##  1 Introduction

From an architectural perspective, optical networking has been shifted from
optical-electrical-optical mode to optical-bypass operations so that
transiting lightpaths could be optically cross-connected from one end to the
other end rather than being undergone unnecessary optical-electrical-optical
conversions efficient . Optical-bypass networking has then gone a long way
from a conceptual proposal to a widely adopted technology by network operators
in the last two decades all-optical . However, one of the major challenges in
scaling up the optical networks to support explosive traffic growth in a
greater efficiency manner is the fact that signal processing functions are
mostly implemented in electrical domain based on digital signal processing.
This involves a number of the well-established procedures including mainly
optical-to-electrical conversion, electronic sampling, digital signal
processing and finally, back-conversion to optical domain. Here, the critical
bottleneck lies in the electronic sampling rate and in circumventing this
major concern, solutions are directed towards migrating certain signal
processing functions to the optical domain. Indeed, photonic signal processing
/ computing technologies offer a new powerful way for handling high speed
signals thanks to its inherent merits of wide bandwidth, transparency and
energy-efficiency optical_processing_1 .

In optical-bypass networking, the basic functions of optical nodes are to add
(drop) and cross-connect the transiting lightpaths. Moreover, in cross-
connecting transiting lightpaths through an intermediate node, these
lightpaths must be maximally separated from each other in either time,
frequency or spatial domain all-optical  to avoid unwanted interference which
deems to deteriorate the signal qualities. This turns out to be a fundamental
limitation as various optical computing operations could be performed between
such transitional lightpaths to generate the output signals which are
spectrally more-efficient than its inputs. In light of recent enormous
advances in photonic computing technologies enabling the controlled
interference of optical channels for various computing capabilities agg1 ;
agg2 ; xor3 ; nc_others10 , we propose a new architectural paradigm for future
optical networks, namely, optical-computing-enabled networks. Our proposal is
defined by the added capability of optical nodes permitting the superposition
of transitional lightpaths for computing purposes to realize a greater
capacity efficiency. Specifically, we present two illustrative examples
highlighting the potential benefits of bringing about in-network optical
computing which are relied on optical aggregation agg1 ; agg2  and optical XOR
gate xor3 ; nc_others10 . The new optical computing capabilities armed at
optical nodes calls for a radical change in optical network design and
planning in order to fully reap spectral and cost benefits as well as
operational efficiency. To this end, we perform a case study for network
coding-enabled optical networks, demonstrating the efficacy of optical-
computing-enabled networks and challenges associated with greater complexities
in network design problems compared to optical-bypass counterpart.

The paper is structured as followed. In Section 2, we introduce a new concept
of optical-computing-enabled paradigm. We also highlight the applications of
two optical computing operations, namely, optical aggregation and optical XOR
whose enabling technologies have been progressing fast and their integration
to future optical networks could be foreseen. We also address the
computational impact and intricacies for network design and planning in the
paradigm of optical-computing networking. Next, as a case study to reveal more
complicated network design problems arisen in the optical-computing-enabled
network, we focus on the network coding-enabled scenarios and formulate the
routing, wavelength and network coding assignment problem in Section 3.
Section 4 is dedicated to showcase the numerical evaluations comparing our
proposal that leverages the use of optical XOR encoding within the framework
of optical-computing-enabled networks to the traditional optical-bypass
networking. The comparison is drawn on a realistic COST239 and NSFNET network
topologies. Finally, Section 5 concludes the paper.

##  2 Optical-computing-enabled Paradigm

The optical-computing-enabled paradigm is characterized by the key property
that optical nodes are empowered with the optical computing capability.
Specifically, two or more optical channels could be optically mixed together
to compute a new optical channel and thus, to attempt achieving a greater
capacity efficiency hai_tnsm ; hai_ro ; hai_springer3 ; hai_mttw21 . In light
of tremendous progresses in optical computing technologies permitting
precisely controlled interference between optical channels, in-transit
lightpaths traversing the same optical node are offered unique opportunities
to optically superimpose to each other to generate the output signals which
are spectrally more-efficient than their inputs. Such optical operations
involving the interaction of transitional optical channels pave the way for
redefining the optical network architecture, disrupting the conventional
assumption of keeping transitional lightpaths untouched. In this context,
optical-computing-enabled framework is foreseen to be the next evolution of
optical-bypass networking. In this section, we highlight the efficient use and
impact of introducing two optical computing operations, namely, optical
aggregation (de-aggregation) and optical XOR gate to optical networks. It is
noticed that the enabling technologies for realizing such two optical
computing capabilities have been accelerating and therefore technical
readiness of integration to optical nodes could be foreseen. Of course, there
are many other ways for mixing two optical channels and in the future, as
photonic computing technologies move forward, a wide range of computing
functions could be technologically realized. These advances will be expected
to have massive impacts to optical networks from both design, planning,
operation and management.

###  2.1 Optical Aggregation and De-aggregation

Aggregation of lower-speed channels into a single higher-speed one has been a
key function in the operation of optical networks. The goal of doing so is to
achieve a greater capacity efficiency by freeing up the lightpaths of the
lower wavelength utilization. Traditionally, this function has been performed
in the electronic domain by terminating optical channels, re-assembling, re-
modulating and finally back-converting to the optical domain. Clearly, there
have been many limitations of doing so and thus, it is not scalable for higher
bit-rate operations. In mitigating this major issue, the concept of optical
aggregation has recently been proposed, implemented and pushed forward agg11 ;
agg12 . The main industrial player for this revolutionary effort is INFINERA,
whose the goal is to develop a new ecosystem of devices and components, with
the capability of transforming the traditional operation of the optical nodes.
In term of functionality, an optical aggregator can add two or more optical
channels of lower bit-rate and/or lower-order modulation format into a single
higher bit-rate and higher-order modulation format one. In this example, we
consider the use of an optical aggregator and de-aggregator whose function is
to combine two QPSK signals into a single 16-QAM channel and vice-versa. Fig.
1 illustrates the schematic diagram for adding two QPSK channels of lower bit-
rate into a single 16-QAM channel of higher bit-rate. In doing so, the
spectral efficiency could thus by improved twice.

Figure 1: Schematic illustration of the aggregation of two QPSK signals into a
single 16-QAM one and vice-versa Figure 2: Traffic Provisioning in Optical-
bypass Networking Figure 3: Optical-computing-enabled Paradigm with Optical
Aggregation and De-aggregation

In leveraging the use of the aforementioned aggregator for optical networks,
we first consider the conventional way of accommodating traffic demands in
optical-bypass networking. Fig. 2 shows the routing and wavelength assignment
for two demands $a$ and $b$ of the same line-rate 100G and format QPSK. Due to
the wavelength uniqueness constraint on a link, two wavelengths are needed on
link $XI$ and $IC$. Now, let us consider the case that at node $X$, the
optical aggregation is enabled. By permitting the optical aggregation, two
100G QPSK transitional lightpaths crossing node $X$ could be optically added
to generate the output signal of 200G, which is modulated on the 16-QAM
format. In Fig. 3, it is clearly observed that by having a single wavelength
channel of 200G capacity, a greater capacity efficiency has been realized. At
the common destination node $C$, the aggregated lightpath could be decomposed
into constituent ones and such decomposition operation could be performed
either in an optical or electrical domain. It is important to note that in
order to maximize the aggregation opportunities, new network design and
planning algorithms should be developed to determine the pairing of demands
for aggregation, the respective aggregation node and more importantly, the
transmission parameters for aggregated lightpaths.

###  2.2 Optical XOR Encoding and Decoding

Technologies for realizing all-optical logic gates have been accelerating in
recent years that permit performing the bit-wise exclusive-or (XOR) between
optical signals of very high bit-rates and/or different modulation formats
xor3 ; nc_others10 . Different from the aggregation operation, the optical XOR
encoding output is kept at the same bit-rate and/or format as the inputs. A
functional description of such device is shown in Fig. 5(a), where two optical
signals of 100G QPSK on different wavelength are coded together to generate
the output X of the same bit-rate and format.

Figure 4: Traffic Provisioning in Optical-bypass Networking Figure 5: Optical-
computing-enabled Paradigm with Optical XOR Encoding and Decoding

In exploiting the optical XOR gate, we focus on the protection scenario and we
assume that there are two demands with dedicated protection. The provisioning
of such two demands are shown in Fig. 4 for the optical-bypass framework.
Because the protection lightpath of demand $a$ and demand $b$ cross the same
links $XI$ and $IC$, it therefore requires at least two wavelengths on those
links. Looking at Fig. 5 (b) for the optical-computing-enabled paradigm when
node $X$ is armed with the optical XOR encoding capability, the protection
signal of demand $a$ and demand $b$ could thus be optically encoded to produce
the signal $X=a\oplus b$. Such encoded output is routed all the way from node
$X$ to the shared destination node $C$. By doing so, only one wavelength
channel on link $XI$ and $IC$ is sufficient, resulting in a spectral saving of
$50\%$. Although the protection signals of demand $a$ and demand $b$ is
encoded, it is always possible to recover the original signal for both demand
$a$ and demand $b$ in any case of a single link failure. The recovery is as
simple as the encoding by making use of the XOR operation on the two remaining
signals. Specifically, if the working signal of demand $b$ is lost, it can be
retrieved in an another way by $b=(a\oplus b)\oplus a$.

The combination of optical encoding and dedicated protection appears to be
matched to attain a greater capacity efficiency while keeping a near-immediate
recovery speed. Nevertheless, in order to realize the encoding benefits, more
complicated network design problems emerge. The more intricacies are related
to the determination of the pair of demands for encoding and the selection of
the routes and/or transmission parameters for the encoded lightpaths.

###  2.3 Impact for Network Design and Planning

It should be noted that the optical-computing-enabled paradigm introduces more
networking flexibility by permitting the precisely controlled interference
among two or more favorable lightpaths, and this poses important ramifications
for the network design algorithms to maximize the potential benefits
hai_comletter ; hai_systems ; hai_comcom ; hai_comcom2 ; hai_rtuwo ;
hai_springer2 . In optical-bypass networking, the central problem for the
network design and planning is the routing and resource allocation. For
solving such problem, the selection of the route and assigning transmission
parameters, including the wavelength/spectrum and/or format, is determined for
each individual demand. In optical-computing-enabled networking, more
complicated network design problems arise due to the interaction of
transitional lightpaths. Specifically, in addition to determining of the route
and transmission parameters for each demand, the pairing of demands and
subsequently, the selection of route and assigning transmission parameters for
special lightpaths involving the interaction of two or many demands, must also
be identified. This represents a radical change in the network design and
planning, disrupting the conventional set of algorithms that have been
developed for optical-bypass networking in many years. In recognizing this
disruption, we call for a new framework, that is, optical network design and
planning 2.0, which encompasses new problems emerging from various ways that
transitional lightpaths could be optically mixed and accompanying algorithms
including exact and heuristic solutions for solving them. In the subsequent
section, we formulate the problem entitled, the routing, wavelength and
network coding assignment problem arising in the application of optical XOR
for optical-computing-enabled network and highlight how such problem is
different from its counterpart, that is, routing and wavelength assignment in
optical-bypass network.

##  3 Routing, Wavelength and Network Coding Assignment Problem Formulation in
Optical-computing-enabled Network

In this section, we consider the design of network coding-enabled networks to
support a set of traffic demands with minimum wavelength link cost. The
optical encoding scheme to be used is the simple XOR, where the input signals
of the same wavelength, line-rate and format, are XOR-coded to produce the
output signal of the same wavelength and format. The main advantage of such
scheme, XOR coding between signals of the same wavelength, is the elimination
of a probe signal and therefore, could be highly cost-efficient xor-model .
Moreover, for ease of operations, the encoding is restricted only on the
protection signals of demands having the same destination node and the
decoding is only taken place at the destination. Furthermore, each demand is
permitted to have maximum one encoding operation. In this framework, there are
a set of sufficient constraints on the network coding assignment for any two
code-able demands, namely: i) two demands must have common destination, ii)
two demands must use the same wavelength, iii) the link-disjoint constraint
between their working paths and between one’s working path to the another’s
protection path, iv) their protection paths must have a common sub-path whose
one end is the shared destination.

Inputs:

  * •

$G(V,E)$: A graph representing the physical network topology with $|V|$ nodes
and $|E|$ fiber links.

  * •

$D$: A set representing the traffic demands, indexed by $d$. Each demand $d\in
D$ requests one wavelength capacity (e.g., 100 Gbps)

  * •

$W$: A set representing the wavelengths on each fiber link, indexed by $w$.
The link capacity measured in number of wavelength is $|W|$

Outputs:

  * •

Routing and wavelength assignment for each lightpath

  * •

Determination of pair of demands for optical XOR encoding and determination of
respective coding nodes.

  * •

Routing and wavelength assignment for encoded lightpaths

  * •

The usage of wavelength on each link

Objective: Minimize the wavelength link usage

The mathematical model is formulated in the form of integer linear programming
(ILP). In addition to typical variables and constraints accounting for the
selection of route and assigning wavelength for each demand, new variables and
constraints emerge as the interaction of demands have been introduced and
thus, causing the mathematical model one order of magnitude computationally
harder than its counterpart, that is, the traditional routing and wavelength
assignment in optical-bypass networking Algorithm1 . In acknowledging the NP-
nature of the model, we therefore propose the following scalable heuristic
(Algorithm 1) that could be used in large networks.

1

Input: $G(V,E),D,W$

Output: $\alpha_{e,w}^{d}$, $\beta_{e,w}^{d}$, $\theta_{w}^{d}$,
$z_{e,w}^{d,v}$, $\delta_{v}^{d}$, $f_{d_{1}}^{d_{2}}$, $\gamma_{e,w}$

2

3for  _node $v\in V$_ do

4 Find demand $d\in D:r(d)=v$

5 Insert $d$ into set $X_{v}$

6

7Sort $X_{v}$ according to its size $|X_{v}|$ in descending order

8

9for  _demand $d\in X_{v}$_ do

10 Find $k$ shortest cycles (modified Suurballe algorithm) including working
and routing route for demand $d$

11

12

13Sort demand $d\in X_{v}$ according to its length of $k$ cycles in descending
order

14

/* Perform routing, encoding and wavelength assignment for all sorted demands
*/

15

16for  _$d\in X_{v}$_ do

17 Select one cycle for demand $d$ out of $k$ cycles

18 Perform encoding with a suitable demand

19 First-fit Wavelength Assignment

20

21

Algorithm 1 Heuristic Solution

##  4 Numerical Results

This section presents the numerical evaluations comparing our proposal that
leverages the use of optical XOR encoding within the framework of optical-
computing-enabled networks to the traditional optical-bypass networking. The
comparison is drawn on a realistic COST239 and NSFNET network topologies as
shown in Fig. 6. The metric for comparison is a traditional one, that is, the
wavelength link cost. Two designs, namely, WNC and NC are performed, where WNC
refers to the routing and wavelength assignment in optical-bypass networking
and NC refers to the more advanced problem, i.e., routing, wavelength and
network coding assignment in optical-computing-enabled networks.

We first test the solutions from solving the ILP models and heuristic on
small-scale topology, 6 nodes, all of degree 3 as shown in Fig. 6 (a). The
traffic is randomly generated between node pairs with the unit capacity (i.e.,
one wavelength) and the fiber capacity is 40 wavelengths. We consider three
increasing load corresponding to $30\%$, $70\%$ and $100\%$ (full mesh) node-
pair traffic exchange. The result in Table 1 (except the full mesh) is
averaged over 20 samples. For the network coding-based design in full mesh
traffic, the computation is overly long and thus, the results is obtained
after 10 hours of running. The well-studied heuristic for WNC achieves optimal
results which are on a par with its ILP model while the heuristic for NC
produces reasonably good solutions with tight gaps compared to its ILP model,
avoiding the overly long computational time. Due to the sub-optimal nature of
the heuristic algorithms, the gain obtained by these algorithms is slightly
reduced compared to the one obtained from the ILP.

Figure 6: Network Topologies under Test Table 1: Performance Comparison between Exact Solution and Heuristic One Load | ILP | Heuristic
---|---|---
WNC | NC | Gain | WNC | NC | Gain
$30\%$ | $32.6$ | $30.6$ | Max: $9\%$, Mean: $6\%$ | $32.6$ | $31.2$ | Max: $9\%$, Mean: $4\%$
$70\%$ | $75.8$ | $67.9$ | Max: $12\%$, Mean: $10\%$ | $75.8$ | $70.6$ | Max: $9\%$, Mean: $7\%$
$100\%$ | $108$ | $99$ | Max=Mean= $8\%$ | 108 | 100 | Max=Mean= $7\%$

We apply the heuristic algorithm for larger networks, NSFNET and COST239
topologies with the same setting as in the 6-node topology about traffic
generation, fiber capacity and number of traffic samples. The results are
presented in Table 2. It can be observed that up to about $8\%$ gain could be
achieved with the NSFNET network. For more densely connected COST239 network,
the lower gain is obtained, up to $5\%$. It is evident that the solution from
NC cases is always better than that from the WNC, resulting in improved
capacity efficiency. Compared to the findings on O-E-O case icc , where the
gain was reported up to $20\%$, there is reduced gain in all-optical case.
This may be due to wavelength-related constraints for network coding
assignments, curbing the coding capability among the demands. Moreover, it
should be noted that the gain is highly dependent on the structure of the
network topology, traffic and network design algorithms.

Table 2: Numerical Results for Realistic topologies Topo | Load | WNC | NC | Gain | No Coding Operation
---|---|---|---|---|---
NSFNET | $30\%$ | 318.3 | 299.5 | Max = $8\%$, Mean = $6\%$ | 9.7
$70\%$ | 730.4 | 682.4 | Max = $8\%$, Mean = $7\%$ | 24.5
$100\%$ | 1048 | 981 | Max = Mean = $6\%$ | 35
COST239 | $30\%$ | 126.2 | 123.3 | Max = $3\%$, Mean = $2\%$ | 1.4
$70\%$ | 295.6 | 285.4 | Max = $5\%$, Mean = $3\%$ | 5.1
$100\%$ | 420 | 404 | Max = Mean = $4\%$ | 8

##  5 Conclusion

This paper presents a new networking paradigm for future optical networks
named, optical-computing-enabled framework. As a potential candidate for the
next evolution of optical-bypass architecture, our proposal aims to exploit
the optical computing capability at optical nodes so that greater capacity
efficiency could be achieved. In highlighting the potential benefits of such
optical-computing-enabled framework, we brought out two revealing examples
leveraging the efficient use of optical aggregation and optical XOR gate. A
numerical case study for the network coding-enabled design was provided to
demonstrate the efficacy and the more complicated network design algorithm of
optical-computing-enabled networking compared to the optical-bypass
counterpart.

Albeit still primitive, the perspective of permitting optical mixing at
intermediate nodes heralds a reinvention of optical networking, altering the
way we think about optical network architecture. Although the experiments and
practical demonstration of optical-computing-enabled networks are still ahead,
the disconnection between the theoretical studies and the implementation
realities are only just beginning to be rectified as enabling technologies
have been more maturing and the huge profits have been foreseen. It should be
pointed out that network design algorithms play a key role in achieving the
operational efficiency of a network and thus, more robust and carefully
designed algorithms should be developed to optimize the advantages of optical-
computing-enabled networking. As for future works, we plan to develop an
ecosystem of new research problems and the accompanying algorithms,
collectively referred to as optical network design and planning 2.0, to
capture a wide range of optical computing operations between in-transit
lightpaths at their optimal usage scenarios.

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