Food Data Integration by using Heuristics based on Lexical

and Semantic Similarities

Gorjan Popovski

1,2 a

, Gordana Ispirova

1,2 b

, Nina Hadzi-Kotarova

, Eva Valen

1,2,4 c

Tome Eftimov

2 d

and Barbara Korou

c Seljak

2 e

zef Stefan International Postgraduate School, 1000 Ljubljana, Slovenia

Computer Systems Department, Jo

zef Stefan Institute, 1000 Ljubljana, Slovenia

Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University, 1000 Skopje, North Macedonia

School of Health Sciences, Faculty of Health and Medicine, Priority Research Centre in Physical Activity and Nutrition,

The University of Newcastle, Callaghan, Australia

Keywords:

Data Normalization, Food Data Integration, Lexical Similarity, Semantic Similarity, Word Embeddings.

Abstract:

With the rapidly growing food supply in the last decade, vast amounts of food-related data have been collected.

To make this data inter-operable and equipped for analyses involving studying relations between food, as one

of the main environmental and health outcomes, data coming from various data sources needs to be normalized.

Food data can have varying sources and formats (food composition, food consumption, recipe data), yet the

most familiar type is food product data, often misinterpreted due to marketing strategies of different producers

and retailers. Several recent studies have addressed the problem of heterogeneous data by matching food

products using lexical similarity between their English names. In this study, we address this problem, while

considering a non-English, low researched language in terms of natural language processing, i.e. Slovenian.

To match food products, we use our previously developed heuristic based on lexical similarity and propose two

new semantic similarity heuristics based on word embeddings. The proposed heuristics are evaluated using a

dataset with 438 ground truth pairs of food products, obtained by matching their EAN barcodes. Preliminary

results show that the lexical similarity heuristic provides more promising results (75% accuracy), while the

best semantic similarity model yields an accuracy of 62%.

1 INTRODUCTION

State-of-the-art data fusion approaches enable inte-

gration of various data sources to produce more con-

sistent, usable, and accurate information than those

provided by any individual data source. However,

before using fused data for predictive modelling, we

must ﬁnd an efﬁcient way of linking unstructured

data attributes that are shared across the multiple data

sources. To enable this, a data normalization pro-

cess is required as a pre-processing step before start-

ing with some further analyses (Pramanik and Hus-

sain, 2019). By applying data normalization the same

concepts that might be represented with different text

https://orcid.org/0000-0001-9091-4735

https://orcid.org/0000-0001-8558-6909

https://orcid.org/0000-0001-8560-920X

https://orcid.org/0000-0001-7330-1902

https://orcid.org/0000-0001-7597-2590

descriptions (i.e. names) or standards are linked to-

gether.

There are many studies performed in the biomed-

ical domain, where different data sources that in-

clude phenotype and genotype information are linked

together in order to explore some hidden relations.

The biomedical domain is well-researched, as a re-

sult of the existence of extensive biomedical vocab-

ularies, standards, and resources that are available

(Aronson, 2006). The Uniﬁed Medical Language

System (UMLS) (Bodenreider, 2004) integrates and

distributes key terminology, classiﬁcation and cod-

ing standards, to promote the creation of more effec-

tive and inter-operable biomedical information sys-

tems and services (Schuyler et al., 1993). It also con-

sists of tools for normalizing English strings, generat-

ing lexical variants, and creating indexes. This means

that having the text description of a biomedical con-

cept, we can ﬁnd its matching from the UMLS vocab-

ulary by using the lexical tools.

208

Popovski, G., Ispirova, G., Hadzi-Kotarova, N., Valen

c, E., Eftimov, T. and Seljak, B.

Food Data Integration by using Heuristics based on Lexical and Semantic Similarities.

DOI: 10.5220/0008990602080216

In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 5: HEALTHINF, pages 208-216

ISBN: 978-989-758-398-8; ISSN: 2184-4305

However, the food domain is still low-resourced

regarding the availability of resources that can be

used for developing artiﬁcial intelligence-based mod-

els. The food supply has evolved in recent years,

alongside the increasing demand for nutritional and

other food-related components. From recent studies

involving nutrition huge amounts of data have been

collected. In order for this data to be reusable and in-

teroperable and equipped for data analysis, it needs to

be harmonized and integrated. Data harmonization is

the process of bringing together data of varying differ-

ent formats, naming conventions, columns, and trans-

forming it into one cohesive data set. One way of

performing data harmonization is to match concepts

to an existing and widely used domain-speciﬁc ontol-

ogy. In the food and nutrition domain, this translates

to matching food concepts to the few food ontolo-

gies that exist such as FoodOn (Grifﬁths et al., 2016),

OntoFood and SNOMED CT (Donnelly, 2006). How-

ever, a recently published study (Popovski et al.,

2019) showed that all of them were developed for

some speciﬁc problems and their coverage is lim-

ited. Regarding other approaches for data harmoniza-

tion and normalization in the food and nutrition do-

main, there is a semi-automatic system for classifying

and describing foods according to FoodEx2 ((EFSA),

2015), known as StandFood (Eftimov et al., 2017),

which can be used for data normalization of food con-

cepts. The limitation of StandFood is that currently, it

works only with English foods’ names.

Food data can have various sources and formats:

food composition data, food consumption data, recipe

data, etc. The most commonly used type is food prod-

uct data. However, this type of data is often misin-

terpreted as a result of the vast and very competitive

marketing system nowadays. Different producers and

retailers manipulate product names to achieve better

marketing. The misinterpretation can also occur due

to the vast variety of diet styles that have emerged re-

cently.

In this paper, we focus on linking food-related

concepts provided in a non-English (i.e. Slovenian)

language which are extracted from two online gro-

cery stores. By linking food concepts from multiple

data sources, which often provide complementary in-

formation about food products, we can complete or at

least enrich the available information. This can also

be helpful in the process of missing value imputation

in food composition databases (FCDBs), especially

for branded food products.

It is important to note that the Slovenian language

is a low-resourced language from the perspective of

availability of natural language processing tools such

as part-of-speech tagging (POS) (Voutilainen, 2003),

chunking, lemmatization, which has represented an

additional challenge while working with textual data.

To link food products using their text description,

we use lexical and semantic similarity as heuristics.

The lexical similarity focuses on the syntactic and

morphological similarity of the compared text, while

the semantic similarity focuses on their context sim-

ilarity. In Section 2, we provide a critical overview

of the related work. Next, in Section 3, our proposed

methodology is explained in detail, followed by an

explanation of the data in Section 4. Additionally,

the experimental results and discussion are given in

Section 5, where we also provide some directions for

future work.

2 RELATED WORK

One of the challenges while working on text similar-

ity is that the same concept can be mentioned using

phrases with a variety of structures, which is a con-

sequence of how people express themselves. In order

to combine the information for the same concept that

is represented in different ways, we should apply text

normalization methods. Text normalization methods

are based on text similarity measures.

Text similarity measures operate on string se-

quences and give us a metric of similarity (or dissimi-

larity) between two text strings. Text similarity deter-

mines how distant two texts are both in surface (i.e.

lexical similarity) and meaning (i.e. semantic similar-

ity).

Normalization methods based on text similar-

ity measures are well presented in (Aronson, 2001;

Savova et al., 2010). Several normalization methods

that are based on ranking technique are available, with

the goal to rank the candidate matches and then to ﬁnd

the most relevant match (Collier et al., 2015). Nor-

malization methods can also utilize machine learning

(ML) algorithms to improve results, which was shown

in the gene normalization task as part of BioCre-

ative II (Morgan et al., 2008) and BioCreative III (Lu

et al., 2011). Regarding the food and nutrition do-

main, methods for normalization of short text seg-

ments (e.g., names or descriptions of nutrients, food

composition data, food consumption data) have re-

cently been proposed (Eftimov and Seljak, 2015; Ef-

timov et al., 2017; Ispirova et al., 2017; Eftimov et al.,

2018) by using two approaches: two approaches: (i)

standard text similarity measures; and (ii) a modiﬁed

version of Part of Speech (POS) tagging probability-

weighted method, ﬁrst proposed in (Eftimov and Sel-

jak, 2015).

Food Data Integration by using Heuristics based on Lexical and Semantic Similarities

209

2.1 Lexical Similarity

Lexical similarity can be calculated either on the char-

acter or word level. Most of the lexical similarity

measures do not take into account the actual mean-

ing behind words or the entire phrases in context, but

focus on how many characters or words overlap.

Let D

and D

be two pieces of text. Some of

the standard lexical similarity measures are (Metzler

et al., 2007):

• The Levenshtein distance counts the number of

deletions, insertions and substitutions necessary

to turn D

into D

• The Optimal String Alignment distance is like the

Levenshtein distance but also allows transposition

of adjacent characters. Each substring may be

edited only once.

• The full Damerau-Levenshtein distance is like the

optimal string alignment distance except that it al-

lows for multiple edits on substrings.

• The longest common substring is deﬁned as the

longest string that can be obtained by pairing char-

acters from D

and D

while keeping the order of

characters intact.

• A q-gram is a subsequence of q consecutive char-

acters of a string. If x (y) is the vector of counts

of q-gram occurrences in D

), the q-gram dis-

tance is given by the sum over the absolute differ-

ences |x

− y

• The cosine distance is computed as 1 −

x·y

||x||||y||

where x and y were deﬁned above.

• Let X be the set of unique q-grams in D

and Y the

set of unique q-grams in D

. The Jaccard distance

is deﬁned as 1 −

|X∩Y |

|X∪Y |

• The Jaro distance is deﬁned as 1 −

+ w

(m−t)

), where |D

| indicates the num-

ber of characters in D

, m is the number of char-

acter matches and t the number of transpositions

of matching characters. The w

are weights asso-

ciated with the characters in D

, characters in D

and with transpositions.

• The Jaro-Winkler distance is a correction of the

Jaro distance. It uses a preﬁx scale p which gives

more favourable ratings to strings that match from

the beginning for a set preﬁx length l.

• The skip-grams are generalization of n-grams in

which the components (typically words) need not

be consecutive in the text, but may leave gaps that

are skipped over.

2.2 Semantic Similarity

Semantic similarity is a metric that deﬁnes the dis-

tance between two pieces of text based on their mean-

ing or semantic content. Calculating semantic simi-

larity is related to representational learning (i.e. learn-

ing embeddings), which has become an important re-

search task for learning representation of symbolic

data. The idea of representational learning is to rep-

resent each piece of text (e.g., word, sentence, para-

graph, depending on the problem) as a vector of con-

tinuous numbers. In the case of learning word em-

beddings, the learned vector captures the context of a

word in a piece of text, as well as semantic and syn-

tactic similarity, relation with other words, etc. To

ﬁnd the similarity between two words, we should cal-

culate the similarity between their vectors. To do this,

we can ﬁnd the angle between their vectors. The co-

sine distance between two words represented by their

vectors x and y can be calculated using the following

equation:

cos(x, y) =

||x||

||y||

. (1)

2.2.1 Word2vec Embeddings

In order to include the semantic information in these

representations, Mikolov et al. (Mikolov et al., 2013a;

Mikolov et al., 2013b) presented the word2vec model,

which learns high-quality distributed vector represen-

tations that capture a large number of precise syn-

tactic and semantic word relationships. These repre-

sentations are also known as embeddings. Using this

model, each token (i.e. words) is represented as a vec-

tor of continuous numbers.

2.2.2 GloVe Embeddings

GloVe (Pennington et al., 2014) is an unsupervised

learning algorithm for obtaining vector representa-

tions for words, which is based on aggregated global

word-word co-occurrence statistics from a corpus,

and the resulting representations are linear substruc-

tures of the word vector space.

3 METHODOLOGY

To match the information about the same food prod-

ucts from different data sources, we ﬁrst preprocess

the data. Next, we match the food products by apply-

ing lexical similarity measures, followed by matching

them with regard to semantic similarity. Finally, we

compare the mapping results by evaluating them on a

HEALTHINF 2020 - 13th International Conference on Health Informatics

210

set of pairs that represent the ground truth, which are

pairs matched by their EAN barcodes.

3.1 Lexical Similarity

Let D

and D

be two pieces of text. First POS tag-

ging, also called grammatical tagging, is applied to

each of them to identify the part-of-speech tags such

as nouns (NN, NNS, NNP, NNPS), verbs (VB, VBD,

VBG, VBN, VBP, VBZ), adjectives (JJ, JJR, JJS),

cardinal numbers (CD), etc (M

arquez and Rodr

ıguez,

1998). Let us deﬁne

= {tokens f rom D

that belong to one word class},

(2)

where i = 1, 2. The word classes are: nouns, adjec-

tives, verbs, adverbs, prepositions, determiners, pro-

nouns, conjunctions, modal verbs, particles, and nu-

merals. For example, Y

can be a set of all tokens from

that are tagged as nouns. In such case, the set con-

sists of all tokens that are tagged as NN, NNS, NNP,

and NNPS.

The next step is to deﬁne which of the extracted

word classes (morphological POS tags) are signiﬁcant

to describe the domain to which the text belongs. The

set of nouns is crucial because nouns carry most of the

information in the text, while all other word classes

(adjectives, verbs, numbers, etc.) only give an addi-

tional explanation. After extracting the set of nouns

and the sets of other word classes that are signiﬁcant

for the domain, lemmatization (Korenius et al., 2004)

is applied to each of them. To ﬁnd string similarity

between both pieces of text, a probability event is de-

ﬁned as a product of independent events

X = N

∏

j=1

, (3)

where N is the similarity between the sets of nouns

found in both pieces of text, k is the number of ad-

ditional word classes that are selected and are signif-

icant for the domain, and Z

is the similarity between

the sets of word class, j, found in both text. The ad-

ditional word classes can be adjectives, verbs, etc.

Because these events are independent, the proba-

bility of the event X can be calculated as

P(X) = P(N)

∏

j=1

P(Z

). (4)

To calculate it, the probabilities of the independent

events need to be deﬁned. Because the problem looks

for the similarity between two sets, it is logical to

use the Jaccard index, J, which is used in statistics

for comparing similarity and diversity of sample sets

(Kosub, 2019). For the similarity between the nouns,

the Jaccard index is used, while for the similarity be-

tween the additional word classes the Jaccard index in

combination with Laplace probability estimate (Cest-

nik et al., 1990) is used. This is because, in some short

segments of text, the additional information provided

by other word classes can be missed, so there will be

no zero probabilities. The probabilities are calculated

P(N) =

∩ N

∪ N

P(Z

) =

∩ Z

| +1

∪ Z

| +2

. (5)

By substituting Equations 5 into Equation 4, we ob-

tain a weight for the matching pair.

If we focus on the food domain, or speciﬁcally

on the food matching problem, let D

and D

be the

(Slovenian) names of two selected food products. As

we said before, the nouns carry most of the informa-

tion, while the additional word classes that describe

the food domain are adjectives, which explain the

food item in more detail (e.g., frozen, fresh), and the

verbs, which are generally related with the method of

preparation (e.g., cooked, drained). Let us deﬁne

= {nouns extracted f rom D

= {ad jectives extracted f rom D

= {verbs extracted f rom D

} (6)

(7)

where i = 1, 2.

To ﬁnd the similarity between the names of food

products, an event is deﬁned as a product of two other

events

X = N · (A +V ), (8)

where N is the similarity between the nouns found in

and N

, and A + V is the similarity between the

two sets of adjectives and verbs handled together as

and A

. The adjectives and verbs are han-

dled together to avoid different forms with the same

meaning. Additionally, lemmatization is applied for

each extracted noun, verb and adjective, and the sim-

ilarity event uses their lemmas.

Because these two events are independent, the

probability of the event X can be calculated as

P(X) = P(N) · P(A +V ). (9)

The probabilities are calculated as

P(N) =

∩ N

∪ N

P(A +V ) =

|(A

∪V

) ∩(A

∪V

)| +1

|(A

∪V

) ∪(A

∪V

)| +2

(10)

By substituting Equations 10 into Equation 9, we ob-

tain a weight for each matching pair.

Food Data Integration by using Heuristics based on Lexical and Semantic Similarities

211

3.2 Semantic Similarity

For mapping the food products from both datasets

considering semantic similarity, we decided to apply

two different word embedding techniques – word2vec

(Mikolov et al., 2013a) and GloVe (Pennington et al.,

2014). For the model training we used the lemmas of

the words contained in the names of the food prod-

ucts. The reason for learning vector representations

for the lemmas and not the whole words is the fact that

one word, grammatically, can have different cases in

Slovene. Lets have f p is the name of the food prod-

uct, which is consisted of n words:

f p =

word

, word

, ..., word

(11)

After obtaining the lemmas of each word:

f p =

lemma

, lemma

, ..., lemma

(12)

We then apply the two algorithms and obtain vector

representations for each lemma (i.e. word) in the food

product name:

E[lemma

] =

, x

, ..., x

(13)

Where a ∈ {1, ..., n}, and d is the dimension of the

generated word vectors, manually deﬁned for the both

of the algorithms. After obtaining the vector represen-

tations, the next step is to apply a heuristic for merg-

ing the vectors for all the lemmas of a name, in order

to obtain the vector representation for the whole food

product name. We chose to work with two heuristics:

1. Average – Calculating the vector representation

for the food product name as an average from the

vector representations of the lemmas of the words

from which it consists of:

average

[ f p] =

+ ... + x

, ...,

+ ... + x

(14)

2. Sum – Calculating the vector representation of the

food product name as a sum from the vector repre-

sentations of the lemmas of the words from which

it consists of:

sum

[ f p] =

+ ... + x

, ..., x

+ ... + x

(15)

Finally, to perform the matching, we calculate the

cosine similarity between the vector representations

of the food product.

3.2.1 Word2vec Embeddings

The only numeric parameters that varied between the

different word2vec models were the dimension size

and the sliding window size. Values for the sliding

window were chosen to be [2, 3, 5], while the dimen-

sions were [100, 200]. Additionally, the feature ex-

traction algorithms included Bag of Words and Skip-

gram. By combining these parameter values, a total

of 12 word2vec models were trained.

3.2.2 GloVe Embeddings

Analogous to the word2vec parameter choice, the

same values were used for the numeric parameters of

GloVe, i.e. [2, 3, 5] for the sliding window and [100,

200] for the number of dimensions. Thus, a total of

six models were trained.

In both cases, the sliding windows were chosen

according to the average number of words per food

product, which rounded equals to nine.

4 DATA

In this section we explain the data collection process,

after which we elaborate on the data pre-processing

step.

4.1 Data Collection

The data about food products used in this study were

scrapped from the web sites of two food retailers (for

convenience purposes let us name them: Retailer

and Retailer

). Each website contains some, but not

complete, information about each food product, such

as the food product name in Slovenian, the EAN bar-

code, the food label, the lists of ingredients and al-

lergens, and the name of the producer. For the food

products for which we have their food product names

and EAN codes, we constructed datasets containing

these two pieces of information about each product

(the format of the datasets is shown in Table 1). It

needs to be pointed out that the food names were sim-

ilar, but not the same (e.g. bread is named by one re-

tailer as “bel kruh”, i.e “white bread” in English, and

by another retailer as “p

seni

cni kruh, bel”, i.e. “wheat

bread, white”).

Where f p is the food product name, bc is the cor-

responding EAN code, and n is the number of food

products in each dataset – for Retailer

, n = 1, 836

and for Retailer

, n = 6, 587.

HEALTHINF 2020 - 13th International Conference on Health Informatics

212

Table 1: Dataset format.

Food product name EAN code

...

4.2 Data Pre-processing

Having the datasets in the format presented in Table 1,

before applying the algorithms for obtaining semantic

similarity or calculating lexical similarity with Equa-

tion 10, the data needed to be pre-processed. The ﬁrst

step was to perform POS tagging on the food product

names. Since we are working with words in Slove-

nian, the POS tagger that is used is for Slovenian

(Grcar et al., 2012). The Slovenian tagger outputs

the tokens in three types of data: word form, lemma,

and morph-syntactic description or tag. We use the

lower case lemmas for each word. The data con-

sists of words spanning across multiple morpholog-

ical types. However, only the lemmas nouns, adjec-

tives, and verbs convey semantic information. There-

fore, these are the only three types that are consid-

ered while calculating lexical similarity and training

the word embedding models.

5 EVALUATION

In order to produce a dataset consisting of ground

truth values, we matched the food products by us-

ing their corresponding EAN codes. With this, we

obtained 438 food products that are available in both

retailers’ catalogues.

Since Retailer1 has signiﬁcantly fewer food prod-

ucts to offer, we ﬁnd the ﬁve most similar food prod-

ucts from Retailer2 and check whether one of them

corresponds to the food product matched by the EAN

code. If so, we count this as a positive example, oth-

erwise as a negative one.

For computing the similarity between the food

products, we ﬁxed the dimensionality to 200, used

a sliding window of 5 for both the word2vec and

GloVe models. Additionally, word2vec was trained

using CBOW. Lastly, the lexical similarity measure

was computed according to formulas 9 and 10.

The hyper-parameter choice was made after evalu-

ating the models described in Section 3.2. The model

with the best empirical results proved to be the ones

with a dimensionality of 200 and a sliding window of

5. Therefore, we use this model in our ﬁnal evalua-

tion.

To gain some insight regarding the embedding

training process, it is useful to look at the values of

0.000

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.010

0.011

0.012

0.013

0.014

0.015

0.016

0.017

0.018

0.019

0.020

0.021

0.022

0.023

0.024

0.025

0.026

0.027

0.028

0 200 400 600 800 1000 1200 1400

Number of iterations

Loss

Loss while training GloVe for each iteration (epoch)

Figure 1: The loss function value plotted over the number

of iterations (epochs) while training GloVe embeddings for

the dataset.

the loss function for each training iteration (epoch).

On Figure 1 these values are plotted. It is evident that

the loss improvement plateaus after a certain point,

so it is computationally beneﬁcial to stop the training

process after this plateau is reached. This also pre-

vents over-ﬁtting the training data, which is important

if new data is added for future evaluation. In this case,

the plateau is somewhere around iteration 800, which

is where it is favorable to stop the training process.

5.1 Results and Discussion

In Table 2, we present the results of the evaluation

on the dataset of 438 food products having similar,

but not the same, food names and the same EAN bar-

codes. It is interesting to note that both summing and

averaging the vector embeddings provided identical

predictive results. Additionally, in Table 3, the accu-

racy of each model is presented.

Table 2: Evaluation results for each model.

Word2vec GloVe Lexical sim.

Positives 271 238 329

Negatives 167 200 109

Table 3: Accuracy for each model.

Model Accuracy

Word2Vec 0.61872

GloVe 0.54338

Lexical 0.75114

Food Data Integration by using Heuristics based on Lexical and Semantic Similarities

213

Looking at Table 2 it follows that out of a total of

438 food products, 271 were in the top ﬁve predic-

tions when using the word2vec model; 238 were in

the top ﬁve predictions when using the GloVe model

and 329 were in the top ﬁve predictions when using

the lexical model.

Further insight into the matching evaluation can

be obtained by counting how many food products

were not found (Negatives) for all models. Specif-

ically, we count how many food products were not

positively matched by any model at all. Taking this

into consideration, 355 out of a total of 438 products

in the evaluation set were positively matched by at

least one of the models. These results are additionally

presented in Table 4.

Table 4: Accuracy for each model.

Total positively matched Accuracy

355 0.81050

For example, in Table 5 the top ﬁve matches for

a food product (in this case “jogurt mu borovnica

1,3mm 1l”, i.e blueberry fruit yogurt) from each

model is presented. In this example, one of each ﬁve

matches is a positive match in the ground truth evalu-

ation dataset.

Additionally, even if the matches from the se-

mantic models do not include the ground truth prod-

uct, they still convey signiﬁcant semantic information

about the food products. In Table 6 we provide one

such example, where it is evident that all ﬁve matches

are related to the food product of interest. In this

example, all food products are related to “sir”, i.e.

cheese. Therefore, the semantic models are not lim-

ited by the lexical information of the food product

name and can be used to match food concepts in cases

where there is low lexical similarity, but the semantic

similarity is high.

One thing to notice is that using lexical similar-

ity as a heuristic will always yield better results when

considering the task of matching branded food prod-

ucts, while semantic similarity as a heuristic can pro-

vide more insight when considering other tasks, such

as matching food data for imputing missing nutrient

values from food composition databases.

One weakness of the semantic models is that they

are using embeddings on a word level. For our fu-

ture work, we are planning to explore more advanced

textual representational models such as BERT (De-

vlin et al., 2018), RoBERTa (Liu et al., 2019), XL-

Net (Yang et al., 2019), and ALBERT (Lan et al.,

2019). There are pre-trained models for English text

for all of these embedding methods. However, in or-

der for these methods to be used with Slovenian text,

we should acquire more data and train the correspond-

ing models. Additionally, the same methodology de-

scribed in this paper can be generalized and applied to

any language, provided sufﬁcient pre-trained models,

or data to train the required models on, exist.

6 CONCLUSIONS

The problem of food data integration becomes espe-

cially important with one of the 2030 development

goal of the United Nations, which states “End hunger,

achieve food security and improved nutrition and pro-

mote sustainable agriculture” (Lartey, 2015). With

the huge amount of food and nutrition-related data

that is collected in the last 10 years, there is a need

for data normalization techniques that will link these

data sets.

In this paper, we propose two heuristics that can

be used for matching food products represented by

their non-English descriptions (i.e. Slovenian). To

give a matching score of a pair of food products, the

ﬁrst one is based on lexical similarity, and the match-

ing score is a probability event deﬁne as a product

of similarity between the set of nouns that appear in

their names and the joint set of adjectives and verbs.

The second one is based on semantic similarity and

uses word embeddings. For it, ﬁrst vector represen-

tations (i.e. embeddings) for the lemmas of nouns,

adjectives, and verbs, which appear in food products

names, are learned. After that, the vector representa-

tion of a food product name can be calculated as an

average or sum from the vector representations of the

lemmas of the words from which it consists of. The

matching score of a pair of food products is the cosine

similarity between the vector representations of their

names.

We evaluated the proposed heuristics by mapping

food products from two online grocery stores. We

compared the results for the proposed heuristics using

a data set of 438 food product pairs, which present

the ground truth. They were obtained by matching

the food products from every pair based on their EAN

codes. By applying the proposed heuristics, for the

ﬁrst food product from every pair, we returned the 5

most similar food products, and we checked whether

one of them corresponds to the second food product

from the pair. Experimental results showed that the

best semantic models achieve an accuracy of 62%,

while the lexical model outperforms this with an ac-

curacy of 75%. Additionally, if all the models are

considered together, an accuracy of 81% is obtained.

For our future work, we are planning to explore

more advanced textual representational methods (i.e.

HEALTHINF 2020 - 13th International Conference on Health Informatics

214

Table 5: Positive match food product examples.

(a) Word2Vec model

Food product: jogurt mu borovnica 1,3mm 1l

Match 1: sadni jogurt borovnica 1,3 m. m. mu 500g

Match 2: sadni jogurt borovnica super 150g

Match 3: sadni jogurt s chia semeni crni ribez borovnica 1,5 m. m. meggle 330g

Match 4: tekoci jogurt borovnica mu 1l

Match 5: sadni bio jogurt s senenim mlekom borovnica 150g

(b) GloVe model

Food product: jogurt mu borovnica 1,3mm 1l

Match 1: tekoci jogurt borovnica mu 1l

Match 2: tekoci jogurt kramar 500g

Match 3: grski jogurt z borovnico 0 m. m. total 170g

Match 4: sadni jogurt borovnica 1,3 m. m. mu 500g

Match 5: lca jogurt nula 150 g borovnica 3,3 m. m.

Food product: jogurt mu borovnica 1,3mm 1l

Match 1: tekoci jogurt borovnica mu 1l

Match 2: sadni jogurt borovnica 1,3 m. m. mu 500g

Match 3: sadni jogurt borovnica 1,2 m. m. lca 180g

Match 4: grski jogurt z borovnico 0 m. m. total 170g

Match 5: sadni jogurt borovnica super 150g

Table 6: Positive match food product examples.

(a) Word2Vec model

Food product: topljeni sir kiri navadni 100g

Match 1: topljeni sir slovenka 200g

Match 2: topljeni sir 140g

Match 3: naravni topljeni sir president 140g

Match 4: topljeni sir camembert president 125g

Match 5: topljeni sir gauda v listicih kaeserei champignon 150g

(b) GloVe model

Food product: topljeni sir kiri navadni 100g

Match 1: topljeni sir slovenka 200g

Match 2: topljeni sir klasik zdenka 140g

Match 3: topljeni sir klasik zdenka 280g

Match 4: topljeni sir cardas zdenka 140g

Match 5: topljeni sir v listicih klasik 150g

embeddings methods), and also use the information

from graph-based embeddings to improve the match-

ing process.

ACKNOWLEDGEMENTS

This work was supported by the project from the

Slovenian Research Agency (research core funding

No. P2-0098), and the European Union’s Hori-

zon 2020 research and innovation programme (grant

agreements No. 863059 and No. 769661).

Information and the views set out in this publica-

tion are those of the authors and do not necessarily re-

ﬂect the ofﬁcial opinion of the European Union. Nei-

ther the European Union institutions and bodies nor

any person acting on their behalf may be held respon-

sible for the use that may be made of the information

contained herein.

Food Data Integration by using Heuristics based on Lexical and Semantic Similarities

215

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