=Paper=
{{Paper
|id=Vol-2604/paper4
|storemode=property
|title=Towards Structuring of Electronic Marketplaces Contents: Items Normalization Technology
|pdfUrl=https://ceur-ws.org/Vol-2604/paper4.pdf
|volume=Vol-2604
|authors=Olga Cherednichenko,Olha Yanholenko,Maryna Vovk,Nataliia Sharonova
|dblpUrl=https://dblp.org/rec/conf/colins/CherednichenkoY20
}}
==Towards Structuring of Electronic Marketplaces Contents: Items Normalization Technology==
https://ceur-ws.org/Vol-2604/paper4.pdf
Towards Structuring of Electronic Marketplaces
Contents: Items Normalization Technology
Olga Cherednichenko1[0000-0002-9391-5220], Olha Yanholenko1[0000-0001-7755-1255],
Maryna Vovk 1[0000-0003-4119-5441], Nataliia Sharonova1[0000-0002-8161-552X]
1 National Technical University “Kharkiv Polytechnic Institute”,
2, Kyrpychova str., 61002 Kharkiv, Ukraine
olha.cherednichenko@gmail.com, olga.yan26@gmail.com,
marihavovk@gmail.com, nvsharonova@ukr.net
Abstract. The E-commerce industry is going strong and is bringing a great profit
to its stakeholders. However, there is probably no buyer of the e-marketplace who
has not faced the issues connected with inappropriate search results or inadequate
filtering and recommendation of irrelevant products. Modern search and collab-
orative filtering algorithms of e-commerce systems do work well with the input
data of high quality but the reality is that often items’ description contains inac-
curacies and incompleteness, which negatively affects the results. The given pa-
per suggests the concept of e-marketplace items normalization which goal is to
provide the unified and standardized patterns of items inside the system that can
be used by search and filtering algorithms. Items normalization is implemented
based on the algebra of predicates models specified in this work. The case study
deals with constructing normalized models of knapsacks items from the online
sports store. The developed models allowed to build 141 normalized item pat-
terns with a unified set of attributes and their values.
Keywords: E-commerce Marketplace; Item Normalization; Item Attributes;
Natural Language Processing; Predicate; Reference model.
1 Introduction
E-commerce positions in the global economy keep on strengthening. This is confirmed
by the constant growth of the world online retail sales which increased by 15% in 2019
compared to 2018 [1]. The share of the world online sales in the total retail sales has
also increased by 1% [1]. All the forecasts predict the future growth of these indicators.
To be successful and to attract more clients, e-marketplaces have to support their buyers
in the best possible way. This support should include efficient tools of product search,
filtering, representation and comparison which will make the purchase process easy and
comfortable. As the number of sellers and items being sold on the e-marketplaces is
growing, the volume of data stored and processed by e-commerce information systems
is increasing drastically. In this context, two situations can be considered. Firstly, in the
case of global e-marketplaces that serve as a platform where a seller and a buyer meet
each other, users can create multiple offers of the same product on the seller side. Thus,
Copyright © 2020 for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
a single real-world object can be presented in different ways in the offers of one or
many sellers. Secondly, in the case of e-shop belonging to a single company that sup-
posedly does not contain duplicate items of a single product, still there is a risk of hav-
ing an incomplete and inaccurate description of the product. In both cases the arbitrary
form of the item description stored by the e-commerce system sophisticates the pro-
cessing of this data. This leads to negative buyers’ experience due to bad search results.
To improve the quality of the data that is used as an input by filtering, clustering and
other algorithms of the e-commerce systems it is suggested to develop a formalized
model of item’s description which will allow avoiding possible ambiguities and inac-
curacies in its representation [2, 3]. The given study suggests calling this process as the
item’s normalization. Its goal is to represent the item in a unified way so that item’s
attributes with their values could be matched with the pattern view of the given type of
product. Having the pattern model of a product, it will be easy to correct errors and fill
in missed values reducing the degree of incompleteness of the initial data.
The rest of the paper is organized in the following way. Section 2 substantiates the
problem statement and provides the general scheme of items normalization. Section 3
reviews the research in the given field. The reference model of items normalization are
given in section 4. A case study of normalization of items of the sports online store is
presented in section 5. Results of the experiment and conclusions are discussed in Sec-
tions 6 and 7 respectively.
2 Problem Statement
In the given paper the process of creating a full, accurate and unified form of the e-
marketplace item is called normalization. Item normalization can be decomposed into
several levels. Let’s denote the set of items as I. Each item 𝑖𝑖 ∈ 𝐼𝐼 is characterized by the
set of attributes 𝑋𝑋 = (𝑥𝑥1 , 𝑥𝑥2 , … , 𝑥𝑥𝑛𝑛 ), where n is the number of attributes. Each attribute
𝑗𝑗
takes values 𝑥𝑥𝑖𝑖 , where 𝑗𝑗 = (1,2, … , 𝑚𝑚). On the lowest level of normalization, it is nec-
𝑗𝑗
essary to switch attribute’s values 𝑥𝑥𝑖𝑖 to the unified view. If an attribute is Weight, for
example, then the normalized value would be the number complemented by the unit of
measurement (e.g., 500 g). On the middle level of normalization, the ambiguity of at-
tributes’ names should be reached. For this purpose, it is necessary to conduct a seman-
tic analysis and to substitute synonymous names with a single unified one. For example,
if the item’s attribute is called “name”, “brand”, “title”, then one of the values should
be selected as a uniform. On the highest level of normalization, the item description
should be complemented with the missed values of attributes based on the data availa-
ble from the quality sources.
Normalized representation of an item should be stored by the e-commerce system
and used while performing its basic functions. The normalization process is aimed at:
1) creating a normalized item’s model from data gathered from the item’s description
on the web site and 2) complementing this model with the missed attributes and their
values, thus getting a full and unified item’s representation. The detailed flow of actions
that should be performed during normalization is shown on Fig. 1.
Fig. 1. Process of items normalization
So the goal of this paper is to improve search, filtering and other procedures of the e-
commerce systems by means of items normalization based on mathematical models of
the algebra of predicates. Normalized items are the unified internal representation of
the products and are internally used by e-commerce algorithms.
3 Related Works
Big volumes of information that need to be gathered, processed and stored in the e-
commerce area caused the intensive development of data mining methods. Electronic
marketplaces with their infinite number of items have already been a subject of research
for the paper authors [4, 5]. And we have the intention to follow up on our previous
researches. Grouping similar products on the trading platforms according to their de-
scriptions is studied in [4]. In order to study item similarity, researches [5] try to analyze
item descriptions on e-commerce markets and it is found out that the k-means algorithm
works well only for uniformly distributed data by categories, but this is not suitable for
the segmentation of heterogeneous descriptions.
In the paper [6], it is explored how natural language processing methods can help to
check contradictions in facts. The authors proposed an approach based on factual infor-
mation systematization. As a result, it is proposed to use predicate algebra to create a
model of searching and extracting factual data [7]. In the time when the size of data-
bases increases, the complexity of the matching process becomes one of the major chal-
lenges for record normalization. Different indexing techniques have been developed for
record normalization and deduplication [8, 9]. Such a problem belongs to the tasks of
record linkage. Researchers [10, 20] solve this issue using a learning algorithm. The
authors in the work [11] have developed a framework for solving the task of product
record normalization. Paper [12] is devoted to studying and analyzing the problem of
record normalization over a set of matching records.
The study [13] demonstrates a duplicate detection method for bio-informatics data-
bases. The papers [14, 15, 16] explored a set of normalization techniques to achieve
better translation quality. Researchers in [17] suggest the flexible query-time record
linkage and fusion framework. In the paper [18] authors described the rule-based
method for deduplicating article records across databases and include an open-source
script module that can be deployed freely.
Thus, we can conclude that a lot of authors worked on normalization on trading plat-
forms and in other domains. Different approaches were developed. The study shows
that there is substantial room for additional research on this topic. Our task is to research
how the normalization of product description dimensions can be solved in order to pro-
vide complete information for a buyer on e-commerce marketplaces.
4 Reference Model of Items Normalization
The Intelligence Theory task is to designate the natural information processes that take
place in human thinking. The Intelligence Theory assists logical mathematics, which
covers the wider scope of questions [19]. It has such sections, which have not yet been
used by informatization. The first stage of formalization of human intelligent processes
is the construction of a thesaurus. Thesaurus contains words of the language that are
used for normalization of both attributes’ titles and their values. In information retrieval
thesauruses, lexical units of text are replaced by descriptors. The general scheme of
item’s normalized view is shown in Fig. 2.
Fig. 2. Items normalization reference model
Figure 3 shows a data flow diagram (DFD) that shows total data flows when solving a
normalization task.
Fig. 3. Data flow diagram
The main notion of logical mathematics is a mathematical relation. A logical network
accomplishes different operations on relationships. Relations show the attribute con-
nections of the objects. Relations are general instruments for the object description. In
order to demonstrate relationships, people use natural human language. Communi-
cating with people, we express to them the sense of the sentence, which is an attitude.
Defined relations can symbolize some notions. Each artifact and process of the out-
world can be represented by relationships. We unrestricted select some non-empty set
U and call its elements as objects. The set U as such is called the universe of objects. It
can be either finite or infinite.
We suggest a model that is built on the comparator identification method. this
method gives the opportunity for data and the template matching. The relation between
the words and their location in the text are the main points of the approach. This method
performs the process of extraction in that way as a human do it [19].
5 Case Study
The case study of the given work is based on the data of the online store Hervis Sports
(https://www.hervis.at/store) that is specialized in sports clothes and equipment. The
store belongs to a single company. The website of this e-shop is in German. The web
crawler component launched on the website has gathered all web pages that contain
knapsacks being sold. The number of items at the moment of the experiment is – 141.
Let’s introduce 𝑌𝑌 = (𝑦𝑦1 , 𝑦𝑦2 , … , 𝑦𝑦141 ) objects of the real world.
Since there is a single seller (web site owner) in this e-commerce system, each knap-
sack model is present once on the site. So there are no duplicates of the same product
on the site. However, the way of representing the same type of product (in our case -
knapsack) differs from item to item. The example of the two knapsack item pages is
shown on fig. 4.
Fig. 4. Items description (A- Deuter, B - Vaude)
From the preliminary analysis of the collected items, we can see that the description of
knapsacks contains different attributes (Title, Technology/Material, Equipment, Vol-
ume, Dimensions, Weight, Load Range, etc.). Knapsack A has Weight attribute and
doesn’t have Load Range attribute while knapsack B does have it. Therefore, the de-
scription of items may contain different sets of attributes.
Additionally, the values of attributes are presented in a different way. Although Vol-
ume is commonly measured in liters, for example, knapsack A has Volume value fol-
lowed by “Liter” and knapsack B – followed by “l”. Among the collected items there
are other variations of liter designation, like “L”, “liter”, “litre”. Similarly, Weight at-
tribute has values complemented with different units of measurement (“kg”, “g”, “G”,
“KG”). Dimensions attribute may have different forms of value representation as shown
in Fig. 2 and its units of measurement are different as well (“cm”, “mm”). Moreover,
an attribute itself may have different names across items. For instance, Dimensions at-
tribute has the following names: “Maße”, “Dimension”, “Abmessung”, “Größe”,
“Grösse”, “Maßen”. The whole list of possible attributes’ names extracted by the web
crawler with their example values is given in Fig. 5.
Table 1 contains all 24 variants of attributes’ names and their English translation
since the normalized item’s model is going to have its values in English. After normal-
izing attributes’ names we have got 17 unique attributes 𝑋𝑋 = (𝑥𝑥1 , 𝑥𝑥2 , … , 𝑥𝑥17 ) intro-
duced.
{"Brand": "Kohla Zugspitze 26",
"Price": "€ 69,99",
"Technologie/ Material": "Surround Ventilationssystem",
"Ausstattung": "Stretch- EInschubtasche an der Front, 2
Deckeltaschen, inkl. Regenhülle mit Reflektoren, 2 seit-
liche Trinkflaschenhalterungen, Hüft- und Brustgurt mit
Seitentasche und Fingerriemen",
"Sonstiges": "Stocklhalterung",
"Lastbereich": "0 - 4 kg",
"Maße": "43 x 22 x 16 cm",
"Volumen": "9,0 l",
"Gewicht": "740 g",
"Rückensystem": "MOTION V Frame™ Rückensystem, 2-Lagen
EVA-Rückenpolster, Rückenlänge: L (48,5 cm)",
"Funktion": "Trinksystem kompatibel",
"Ausstattug": "abnehmbare Kompressionsriemen, Deck-
eltasche, verstaubare Befestigungsschlaufen für Eispickel
oder Trekkingstöcke",
"Material": "Dynajin 210, 30% Polyester / 70% Polyamid",
"Dimension": "40 x 13 x 17 cm",
"Technologie/Material": "Removable Airbag System 3.0",
"Hinweis": "Kartusche ist nicht im Lieferumfang enthal-
ten",
"Abmessung": "28 x 24 x 15 cm",
"Gewich": "2,26 kg",
"Füllung": "Stickstoff (nur Werkbefüllung möglich)",
"Arbeitsdruck": "300 bar",
"Größe": "75 x 36 x 30 cm",
"Austattung": "Raincover für den ganzen Rucksack, easy
handle Zipper, hochwertige Qualitäts-Zipper von SBS",
"Abmessungen": "500x142x280mm",
"Liter": "30L",
"Volumen/Gewicht": "30L / 1930g",
"Grösse": "43 / 24 / 19 (H x B x T) cm",
"Maßen": "45x31x25cm"
}
Fig. 5. Attributes’ names
Table 1. Matching of German and English attributes’ manes
German (DE) English (EN) Num- Normalized Des-
ber of name of at- ig-
occur-
tribute (EN) na-
rences
tion
(DE,
EN)
Marke Brand 141 Brand 𝑥𝑥1
Preis Price 141 Price 𝑥𝑥2
Technolo- Technol- 106 Technol- 𝑥𝑥3
gie_Material ogy_Material ogy_Mate-
rial
Ausstattung Equipments 120 Equipments 𝑥𝑥4
Sonstiges Other 94 Other 𝑥𝑥5
Lastbereich LoadRange 2 LoadRange 𝑥𝑥6
Maße Dimensions 52 Size 𝑥𝑥7
Volumen Volume 101 Volume 𝑥𝑥8
Gewicht Weight 76 Weight 𝑥𝑥9
Rückensystem BackSystem 12 BackSystem 𝑥𝑥10
Funktion Function 59 Function 𝑥𝑥11
Material Material 30 Material 𝑥𝑥12
Dimension Dimension 3 Size 𝑥𝑥7
Hinweis Note 3 Note 𝑥𝑥13
Abmessung Dimension 9 Size 𝑥𝑥7
Gewich Weight 1 Weight 𝑥𝑥14
Füllung Filling 1 Filling 𝑥𝑥15
Arbeitsdruck WorkingPres- 1 Work- 𝑥𝑥16
sure ingPressure
Größe Size 8 Size 𝑥𝑥7
Abmessungen Dimensions 1 Size 𝑥𝑥7
Liter Liter 1 Volume 𝑥𝑥8
Volu- Vol- 1 Vol- 𝑥𝑥17
men_Gewicht ume_Weight ume_Weight
Grösse Size 1 Size 𝑥𝑥7
Maßen Size 1 Size 𝑥𝑥7
All these examples of different description of the same attributes/values/units of
measurement allow concluding that information about the products in this e-commerce
system is stored in a non-unified form. This leads to an inadequate work of search and
filtering algorithms of the system. For example, if the knapsack was added to the system
with the Volume equal to “9 Litres” and the system is able to process only items with
Volume values ended by “L”, then this specific knapsack will never be displayed in the
filtering results for all 9-liter knapsacks. Thus, to perform properly the system requires
a normalized description of all items which will provide adequate and accurate results
of search, filtering, and comparison.
From the other point of view, if a product doesn’t contain Volume value at all, it
does not mean that it does not have it. It was just missed while adding the item to the
system. In this case, such particular knapsack also does not have many chances to be
shown in the search results. Having a normalized form of such item will allow to define
the missed values and to complement them with the information from the patterns. In
the role of a pattern, we can consider official documents about the product, its quality
certificates and specifications, description from official sites of the manufacturers, etc.
Assigning available values to attributes 𝑋𝑋 = (𝑥𝑥1 , 𝑥𝑥2 , … , 𝑥𝑥17 ), we can define each
item in a unique normalized way. For example, attribute 𝑥𝑥1 can take values 𝑥𝑥11 =“2117”,
𝑥𝑥12 =“ABS”, 𝑥𝑥13 =“APTEM”, 𝑥𝑥14 =“BCA”, 𝑥𝑥15 =“Babolat”, 𝑥𝑥16 =“Black Crevice”,
𝑥𝑥17 =“Deuter”, 𝑥𝑥18 =“Dynafit”, 𝑥𝑥19 =“Kilimanjaro”, 𝑥𝑥110 =“Kohla”, 𝑥𝑥111 =“Mammut”,
𝑥𝑥112 =“Salomon”, 𝑥𝑥113 =“Vaude”, 𝑥𝑥114 =“Wheel Bee”. Attribute 𝑥𝑥8 can take values
𝑥𝑥81 =“≤10L”, 𝑥𝑥82 =“>10L and ≤20L”, 𝑥𝑥83 =“>20L and ≤30L”, 𝑥𝑥84 =“>30L and ≤50L”,
𝑥𝑥85 =“>50L and ≤70L”, 𝑥𝑥86 =“>70L”. Having assigned all values to all attributes, it is
possible to build the relation 𝐿𝐿(𝑋𝑋, 𝑌𝑌) and define it unambiguously for each of 141 items.
Normalization of items requires constructions of relations:
𝐿𝐿(𝑥𝑥1 , 𝑥𝑥2 , … , 𝑥𝑥17 , 𝑦𝑦1 ) = 1,
𝐿𝐿(𝑥𝑥1 , 𝑥𝑥2 , … , 𝑥𝑥17 , 𝑦𝑦2 ) = 1,
…
𝐿𝐿(𝑥𝑥1 , 𝑥𝑥2 , … , 𝑥𝑥17 , 𝑦𝑦141 ) = 1.
The normalization of attributes’ values was performed based on the comparator iden-
tification of the input values and units of measurement. For example, the comparator
function for defining attribute units of measurement looks like:
L, if E(a,L)⋁E(a,l)⋁E(a,Litre)⋁E(a,litre)⋁E(a,Liter)⋁E(a,liter),
kg, if E(a,kg)∨E(a,Kg)∨E(a,K ),
f(a)= �
…
cm, if E(a,cm)∨E(a,Cm)∨E(a,CM),
where E is a predicate of equivalence (identification) that defines one of the possible
values of units of measurement entered to the system.
The results of normalization of Size attribute is shown on Fig. 6.
6 Discussion
As a result of the given research, we developed a reference model in order to give items
descriptions from e-commerce marketplaces in the way of formal representation. The
predicate representation of goods characteristics allows using any natural language for
filing in items description by the seller. Thus, the seller is less obliged to be strict in the
form of an item attribute description. The developed approach gives the opportunity to
solve the issue of normalization in commodity designation. The given findings are the
basis of a two-layer information system. One layer presents how the product features
are shown for a customer and the second layer of how the internal system sees them.
Fig. 6. Size attribute values normalization
7 Conclusions and Future Work
The main idea of the given research is that collaborative filtering, items search and
matching processes of e-commerce business work well if the data they are dealing with
is full and precise. But in the real world, the description of products on the e-market-
places is far from the ideal. Thus, buyers may see irrelevant searching results while
looking for some products. To improve this situation, the given work introduces the
notion of items normalization as a process of constructing complete and accurate pat-
terns of items being sold. Normalized items are treated as the high-quality input data
for internal algorithms of e-commerce systems.
The presented models of items normalization allow: 1) to form the set of unique
attributes of items; 2) translate attributes’ values to a unified form; 3) build a relation
between an item and attributes that uniquely defines a real-world product. The devel-
oped models were tested on the experimental set of knapsacks from the online sports
store. The case study represents the results of attributes and their values normalization.
As a future direction of this research, it is planned to evaluate the performance of
searching algorithms taking as an input row items’ description and normalized patterns.
Also the presented findings can be used for further development of items matching
models. And finally, it would be interesting to explore the use of normalized items in
the problem of e-marketplace localization.
8 References
1. How High Will E-Commerce Sales Go? http://www.cbre.us/real-estate-services/real-estate-
industries/omnichannel/the-definitive-guide-to-omnichannel-real-estate/by-the-num-
bers/how-high-will-e-commerce-sales-go
2. Razia Sulthana, A., Ramasamy, S.: Ontology and context based recommendation system
using Neuro-Fuzzy Classification. Computers & Electrical Engineering February (2018).
3. Ya, L. The Comparison of Personalization Recommendation for E-Commerce. International
Conference on Solid State Devices and Materials Science, Physics Procedia 25, pp. 475-478
(2012).
4. Cherednichenko, O., Vovk, M., Kanishcheva, O., Godlevskyi, O.: Towards Improving the
Search Quality on the Trading Platforms. In: S.Wrycza, J. Maslankowski(Eds): 11th
SIGSAND/PLAIS 2018, LNBIP 333. pp. 21-30. Springer (2018).
5. Cherednichenko, O., Vovk, M., Kanishcheva, O., Godlevskyi, O.: Studying Items Similarity
for Dependable Buying on Electronic Marketplaces. Proc. 2nd Int. Conf. On Computational
Linguistics and Intelligent Systems (COLINS), Volume I: Main Conference CEUR-WS.
Vol. 2136. pp.78-89. Lviv, Ukraine, (2018).
6. Sharonova, N., Doroshenko, A., Cherednichenko, O.: Issues of Fact-based Information
Analysis. Proc. 2nd Int. Conf. On Computational Linguistics and Intelligent Systems
(COLINS), Volume I: Main Conference CEUR-WS. Vol. 2136. pp. 11-19. Lviv, Ukraine,
(2018).
7. Bondarenko, M. F., Shabanov-Kushnarenko, U. P.: Theory of intelligence: a Handbook
SMIT Company, Kharkiv (2006).
8. Christen, P. A Survey of Indexing Techniques for Scalable Record Linkage and Deduplica-
tion. IEEE Transactions on Knowledge and Data Engineering, 24(9), pp. 1537–
1555. (2012).
9. Lusetti, M. Ruzsics, T., Gohring, A.: Encoder-Decoder Methods for Text Normalization.
Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects,
pp. 18–28 Santa Fe, New Mexico, USA (2018).
10. Bilenko, M., Basu, S., & Sahami, M. (n.d.).: Adaptive Product Normalization: Using Online
Learning for Record Linkage in Comparison Shopping. Fifth IEEE International Conference
on Data Mining (2005).
11. Tak-Lam Wong, An Unsupervised Approach for Product Record Normalization across Dif-
ferent Web Sites. Proceedings of the 23rd national conference on Artificial intelligence -
Volume 2, pp. 1249–1254 (2008).
12. Dong, Y., Dragut, E. C., & Meng, W.: Normalization of Duplicate Records from Multiple
Sources. IEEE Transactions on Knowledge and Data Engineering. (2018).
13. Chen, Q., Zobel, J., Verspoor, K.: Evaluation of a Machine Learning Duplicate Detection
Method for Bioinformatics Databases. Proceedings of the ACM Ninth International Work-
shop on Data and Text Mining in Biomedical Informatics - DTMBIO ’15. (2015).
14. Banerjee, P., Kumar Naskar, S., Roturier, J., Way A., Josef van Genabith. Domain Adapta-
tion in SMT of User-Generated Forum Content Guided by OOV Word Reduction: Normal-
ization and/or Supplementary Data? European Association for Machine Translation. (2012).
15. Clark, E., & Araki, K.: Text Normalization in Social Media: Progress, Problems and Appli-
cations for a Pre-Processing System of Casual English. Procedia - Social and Behavioral
Sciences, 27, pp. 2–11. (2011).
16. Kreimeyer, K., Foster, M., Pandey, A., Arya, N., Halford, G., Jones, S. F., Botsis, T.: Natural
language processing systems for capturing and standardizing unstructured clinical infor-
mation: A systematic review. Journal of Biomedical Informatics, 73, pp. 14–29. (2017).
17. Rezig, E. K., Dragut, E. C., Ouzzani, M., Elmagarmid, A. K., & Aref, W. G.: ORLF: A
flexible framework for online record linkage and fusion. 2016 IEEE 32nd International Con-
ference on Data Engineering (2016).
18. Jiang, Y., Lin, C., Meng, W., Yu, C., Cohen, A. M., & Smalheiser, N. R.: Rule-based dedu-
plication of article records from bibliographic databases. Database, (2014).
19. Bondarenko M. F., Shabanov-Kushnarenko U. P.: Brain-like structures: A reference book
Naukova dumka, Kyiv (2011).
20. Vysotska, V., Burov, Y., Lytvyn, V., Oleshek, O.: Automated Monitoring of Changes in
Web Resources. In: Advances in Intelligent Systems and Computing, 1020, pp.348–363.
(2020).