# Enhancing Data Space Semantic Interoperability through Machine Learning: a
Visionary Perspective

Zeyd Boukhers  Fraunhofer Institute for Applied Information Technology FIT and
University Hospital Cologne, Institute for Biomedical Informatics Konrad-
Adenauer-Straße, 53757Germany43017-6221<EMAIL_ADDRESS>0000-0001-9778-9164 ,  Christoph Lange  Fraunhofer Institute for Applied
Information Technology FIT and RWTH Aachen University Konrad-Adenauer-Straße,
53757Germany43017-6221<EMAIL_ADDRESS>0000-0001-9879-3827 and  Oya Beyan  University of Cologne, Faculty of Medicine
and University Hospital Cologne, Institute for Biomedical InformaticsKerpener
Straße 62 50937Germany<EMAIL_ADDRESS>0000-0001-7611-3501

(20 February 2007; 12 March 2009; 5 June 2009)

###### Abstract.

Our vision paper outlines a plan to improve the future of semantic
interoperability in data spaces through the application of machine learning.
The use of data spaces, where data is exchanged among members in a self-
regulated environment, is becoming increasingly popular. However, the current
manual practices of managing metadata and vocabularies in these spaces are
time-consuming, prone to errors, and may not meet the needs of all
stakeholders. By leveraging the power of machine learning, we believe that
semantic interoperability in data spaces can be significantly improved. This
involves automatically generating and updating metadata, which results in a
more flexible vocabulary that can accommodate the diverse terminologies used
by different sub-communities. Our vision for the future of data spaces
addresses the limitations of conventional data exchange and makes data more
accessible and valuable for all members of the community.

data spaces, semantic interoperability, machine learning

††ccs: Information systems Data exchange††ccs: Information systems Data access
methods††ccs: Information systems Semantic web description languages

##  1\. Introduction

Data spaces are a cutting-edge solution to the difficulties encountered in
data exchange and integration. They act as a federated platform for sharing
and exchanging data among various entities, providing the necessary tools and
security measures to ensure data can be safely shared and consumed. The
ultimate aim of data spaces is to enhance the accessibility and usage of data
by a wide range of stakeholders, fostering innovation and research in the
digital economy. These innovative infrastructures empower all users by making
data integration simpler, more adaptable, and more efficient. conventional
data exchange systems, data spaces have the advantage of easily onboarding new
members.

The theoretical importance of data spaces lies in their ability to seamlessly
integrate data sources that have diverse schemas and structures, without the
requirement of a shared schema or extensive upfront effort to standardize the
data. This is made possible through the extensive use of semantic technologies
(Alexiev, 2022), which help align the data and make it usable in a consistent
manner. As a result, using data spaces reduces the time and effort needed to
establish a data integration system, while offering increased flexibility and
scalability. However, achieving satisfactory levels of semantic
interoperability in data spaces is a complex and ongoing process. It requires
ongoing development of standards, tools, and techniques for data integration
and processing.

International Data Spaces111https://internationaldataspaces.org/ and
Gaia-X222https://gaia-x.eu/ are two prominent initiatives in Europe and
globally that are designed to bring together various stakeholders to create a
secure, interoperable, and decentralized data infrastructure. The ultimate
objective of IDS and Gaia-X is to establish a trustworthy, secure, and
efficient data infrastructure that supports data-driven innovation while
preserving privacy and security. These initiatives focus on providing
infrastructure services that increase trust among entities, advance data
sovereignty, and protect data privacy. As a result, they are expected to
simplify the process of accessing and utilizing data securely and
transparently for businesses and organizations.

As the number of organizations and individuals participating in various data
spaces continues to increase, the demand for effective data management and
exchange solutions will also rise. The key to successful data exchange lies in
the mutual understanding of the data being shared, which is referred to as
_semantic interoperability_. Thus, finding ways to enhance semantic
interoperability is of utmost importance as the number of members joining
different data spaces continues to grow.

Although machine learning has shown promise in addressing the above-mentioned
interoperability aspects of data exchange in general, its application to Data
Spaces has not been fully explored. This paper aims to shed light on the
potential of using machine learning to improve semantic interoperability in
Data Spaces, making it the go-to solution for data exchange.

In this paper, we present our perspective on how machine learning solutions
can be utilized to improve the semantic interoperability of a data space,
using IDS as a concrete example. Although there are numerous aspects of
semantic interoperability, we concentrate on six key challenges that are
prevalent in data exchange. It is crucial to note that this paper does not
make any definitive claims, but instead offers a comprehensive overview and
framework for integrating machine learning solutions directly into data
spaces.

##  2\. Related Work

The concept of Data Spaces has been in existence for several decades, but in
recent times, it has gained significant attention, and considerable effort is
being devoted to facilitating data exchange in today’s data-driven ecosystem,
such as International Data Spaces
(IDS)333https://www.fraunhofer.de/en/research/lighthouse-projects-fraunhofer-
initiatives/international-data-spaces.html and the Common European Data Spaces
(Scerri et al., 2022). So far, the primary focus in practical data spaces has
been on legal, technical, and metadata interoperability, with little attention
given to the semantic aspect of data, as only a few studies have been
conducted in this area (Alexiev, 2022). This means that in terms of semantic
interoperability, the current focus is on metadata only with the assumption
that it exists. However, data semantic interoperability has been studied for
decades. For example, Ouksel et al. (Ouksel and Sheth, 1999) discussed the
issue of finding accurate information in a complex, heterogeneous information
system like the Internet and Web. They proposed a framework for
interoperability that involves relating information to real-world entities and
acknowledges the changing nature of semantics. More than one decade later,
Kiljander et al. (Kiljander et al., 2014) discussed the need for common
approaches to enable high-level interoperability between heterogeneous IoT
devices to realize pervasive computing and IoT visions. It divides the
interoperability challenge into two levels: connectivity and semantics. The
connectivity level covers traditional Open System Interconnection (OSI) model
layers from the physical to the transport layer. The semantic level covers
technologies needed for enabling meaning-sharing between communicating
parties. The authors stated that semantic level interoperability has been
identified as a main goal in the Semantic Web and that semantic web technology
can be used to represent knowledge about the physical world in IoT-related
projects.

Semantic interoperability in data exchange has been also addressed in specific
domains. Lin et al. (Lin et al., 2011) evaluated the usage of Logical
Observation Identifiers Names and Codes (LOINC) and its impact on the
interoperability of laboratory data from different institutions that use LOINC
codes. Heterogeneous data formats have been discovered among different
institutions for the same laboratory tests using LOINC codes. After
investigating the common problems that arise when aggregating such data, they
suggest that more guidance on best practices in coding laboratory results is
needed to achieve greater interoperability.

##  3\. ML-enhanced Data Spaces

Semantic interoperability in data spaces is a complex issue that involves
multiple aspects, as illustrated in Figure 1. While machine learning has the
potential to improve each of these aspects, traditional approaches have
primarily utilized machine learning techniques in isolation, rather than
within the broader context of data spaces. It is vital to consider the full
spectrum of semantic interoperability aspects and integrate machine learning
in a comprehensive and holistic manner within the data space environment.

Figure 1. Semantic interoperability aspects in data spaces that machine
learning can enhance

Figure 2 presents an overview of the ML-enhanced data space in the
International Data Spaces environment, showcasing six key aspects of data
management among three stakeholders, including data providers and consumers
and service providers. These aspects are:

  * •

Automatic Metadata Extraction ( 1): A machine learning model can automatically
extract essential attribute values from the data if metadata is not already
available, helping data providers to prepare their data for exchange and
consumption without the need for manual metadata preparation.

  * •

Ontology and Vocabulary Alignment ( 2): The vocabulary of the data space is
aligned with the vocabulary of the data provider, enabling data consumers to
understand the data being exchanged. This eliminates the need for members in
the data space to adopt the same internal vocabulary, which can often be a
challenging task.

  * •

FAIRness Evaluation ( 3): The FAIRness level of the data is assessed based on
provided or extracted metadata, allowing the data provider to improve the
FAIRness of their data and allowing the data consumer to understand the ease
of use of the data.

  * •

Data Quality Assessment & Enhancement ( 4): The quality of the data is
evaluated and improved if possible, based on the format of the data. Machine
learning can be used to evaluate and enhance structured and tabular data,
however, it’s important to recognize that the quality metrics may vary
depending on the format of the data. For example, it might be challenging to
assess the quality of unstructured data (e.g., a corpus of documents).

  * •

Privacy Preserving ( 5): ML-based anonymization and masking techniques can be
applied to data that contains private, sensitive, or personal information to
make it shareable. Sensitive data can be automatically detected or provided by
the data provider, allowing data providers to share their data without any
privacy concerns.

  * •

Compatibility Improvement ( 6): The data is transformed into a readable format
for the data consumer. In cases where data is being merged with the consumer’s
data, the consumer will communicate the structure and format, enabling the
data to be transformed accordingly. This allows the consumer to make use of
the received data without having to put in additional effort to read and
understand it.

Figure 2. An overview of an ML-enhanced Data Space with three members. (1)
Automatic Metadata Extraction, (2) Ontology and Vocabulary Alignment, (3)
FAIRness Evaluation, (4) Data Quality Assessment & Enhancement, (5) Privacy
Preserving, (6) Compatibility Improvement.

In the following, we discuss each of these aspects:

###  3.1. Automatic Metadata Extraction

Metadata plays a vital role in data exchange as it enables data consumers to
understand the data and determine if it meets their needs. However, many data
providers may be hesitant to provide the necessary metadata due to a lack of
capacity or knowledge to prepare it for their resources. This can be a
significant obstacle in data exchange, as it limits the ability of consumers
to access and utilize the data they need.

To overcome this challenge, machine learning can be leveraged to (semi)
automatically extract metadata from resources. Machine learning algorithms can
be trained on a dataset of resources and their corresponding metadata,
allowing them to learn the patterns and relationships between the data and the
metadata. These algorithms can then be applied to new resources to extract the
relevant metadata. This approach has the advantage of being able to handle
complex and nuanced relationships between the data and the metadata. It can
also be easily updated and adapted as the data and its needs evolve. However,
it is important to note that a typical challenge in data spaces is that the
resources have different, heterogeneous formats.

Different resources being exchanged in data spaces can have varying metadata
properties, and it may be necessary to utilize different machine learning (ML)
models for different resources and metadata attributes. For instance, in the
case of document corpora, Natural Language Processing (NLP) techniques can be
employed to extract titles and descriptions. Specifically, automatic metadata
extraction techniques such as those in (Boukhers and Bouabdallah, 2022;
Tkaczyk, 2017) can be utilized to extract metadata from each document, such as
_Publication Date_ , _Author_ , _Language_ , etc. This metadata can then be
used to derive the metadata for the entire collection, such as _Publication
Range_ , _Authors_ , _Languages_ , etc.

###  3.2. Ontology and Vocabulary Alignment

The International Data Spaces Reference
Architecture444https://internationaldataspaces.org/use/reference-architecture/
highlights the importance of common vocabularies for effective data exchange
within a data space. However, in practice, data providers may have their own
unique vocabularies, making it difficult to align them with the vocabulary
used in the data space. This can be due to the cost and effort involved in
mapping their existing vocabularies to the data space vocabulary, or due to
the fact that a data provider may participate in multiple data spaces with
different vocabularies.

To tackle these challenges, machine learning algorithms can be utilized to
support automatic mapping between the local vocabulary of a data provider and
the vocabulary used in the data space. This allows for seamless and
interoperable data exchange, without requiring data providers to adopt a new
vocabulary.

Machine learning-based methods for ontology alignment (Nezhadi et al., 2011)
and ontology matching (Doan et al., 2004) can be applied to automatically map
concepts and terms from one ontology or vocabulary to another. These
algorithms use techniques such as semantic similarity measures (Sousa et al.,
2022), graph-based methods (Shenoy et al., 2013), and deep learning models
(Khoudja et al., 2018; Iyer et al., 2020; Bento et al., 2020) to identify
correspondences between concepts in different ontologies or vocabularies. The
goal is to produce a mapping that enables data exchange between systems using
different ontologies or vocabularies while preserving the meaning of the data.

###  3.3. FAIRness Evaluation

The FAIR (i.e., Findable, Accessible, Interoperable and Reusable) principles
are becoming increasingly important in data exchange and sharing. These
principles aim to ensure that data resources are easily discoverable,
accessible, can be easily integrated with other data sources, and can be
reused for multiple purposes. Compliance with these principles makes it more
likely that data will be used and reused, as it increases the overall quality
and usability of the resource.

Evaluating the FAIRness of a resource is a crucial step in determining its
fitness for use, as it helps to identify any potential barriers to reuse. This
can include issues such as licensing restrictions, data access conditions, and
data interoperability issues. Conducting this evaluation in advance can save
valuable time and resources, as it helps to avoid the need for costly
negotiations or lengthy wait times for access to data that may not be suitable
for the intended use.

As discussed in Section 3.2, the use of shared vocabularies, such as
ontologies, is important for increasing the findability and interoperability
of resources. However, only using mapping techniques (see Section 3.2) may not
be enough, as internal ontologies that describe the metadata may not be
represented using common classes. To address this issue, machine learning
techniques, such as BERTmap (He et al., 2022), can be used to assess the level
of compatibility between the provider’s ontology and the data space’s
ontology. Additionally, rule-based and semantic web technologies can be used
to evaluate the structure of the metadata, further increasing the overall
FAIRness of the resource.

###  3.4. Data Quality Assessment & Enhancement

Data quality is a crucial concern for data consumers, as it impacts the
trustworthiness and usefulness of the data. Unfortunately, metadata alone
cannot provide any indication of the quality of the data. To ensure the
quality of data, various dimensions must be considered, including accuracy,
completeness, correctness, validity, integrity, and uniqueness. The importance
of each dimension may vary depending on the intended use of the data and the
needs of the data consumer.

Accuracy refers to how closely the data reflects the real-world phenomenon it
represents. Completeness refers to the extent to which all necessary data is
present. Correctness pertains to the degree to which the data adheres to
established rules, such as those related to syntax, semantics, or data
constraints. Validity refers to the degree to which the data follows the
predefined format, structure, and domain. Integrity is the degree to which the
data is protected against unauthorized changes. Lastly, uniqueness refers to
the degree to which each data item is distinct and identifiable.

To ensure data quality, data providers must take steps to assess and improve
the quality of their data. This can include implementing data validation and
quality checks, using techniques like data profiling and data cleaning, and
implementing data governance policies and procedures. Data consumers should
also take steps to assess the quality of the data they receive, such as
evaluating the data’s source and provenance, performing data quality checks,
and monitoring the data for anomalies.

Machine learning algorithms can play a crucial role in ensuring the quality
and accuracy of data. One way they achieve this is by comparing the data to
other sources to validate its accuracy. Additionally, machine learning
algorithms can be trained to identify patterns and anomalies in the data
(Agrawal and Agrawal, 2015; Pang et al., 2021), helping to flag any potential
inaccuracies or errors.

Another benefit of using machine learning algorithms is the ability to
complete missing data. By analyzing patterns and relationships in the data,
machine learning models can make predictions about missing values and fill
them in (Thomas and Rajabi, 2021; Raja and Thangavel, 2020; Hasan et al.,
2021). This is especially useful in cases where it would be time-consuming or
challenging to manually fill in missing data.

Furthermore, machine learning techniques can also be applied to identify and
remove duplicates in data, improving the overall uniqueness and consistency of
the data (Park et al., 2022; Tarun et al., 2021; Christen and Goiser, 2019).

###  3.5. Privacy Preserving

Private and sensitive data, such as personal information, medical records, and
financial data, is often subject to strict regulations and guidelines for
protection and access. In order for different systems to exchange and use
private data, they must be able to accurately interpret and understand the
meaning and context of the data, and ensure that it is being used in
compliance with applicable laws and regulations. ensuring semantic
interoperability for private data requires a combination of technical
solutions, such as secure data exchange protocols and data anonymization
techniques, and strict governance and compliance mechanisms.

To achieve this, data providers can use machine learning techniques to
automatically detect private and sensitive data in their systems (Ray et al.,
2021; Ahmed et al., 2021) and take appropriate actions to mask (Torra, 2022)
or anonymize (Majeed and Lee, 2020) the data. This can help protect
individuals’ privacy while enabling data sharing and interoperability. For
example, techniques such as data de-identification, data masking, and
differential privacy can be used to remove identifying information from data
while preserving its usefulness for analysis.

###  3.6. Compatibility Improvement

Also, when the same vocabulary and ontology are used by the data provider and
consumer, resources are not semantically interoperable if they are not
compatible with the consumer system of their resource to be integrated with.
To overcome the incompatibility of resources in data exchange, solutions
include data mapping and data transformation. Machine learning techniques have
shown great performance in these tasks.

Resources are not semantically interoperable when they cannot be understood or
used by the systems that need to access them. This can occur when the
resources have different data formats or structures, making it difficult for
systems to integrate and make use of the information.

To overcome the incompatibility of resources in data exchange, solutions
include data mapping (Li et al., 2018) and data transformation (Sajid et al.,
2019). Data mapping is the process of aligning the data elements from one
resource to the corresponding elements in another resource. Data
transformation is the process of converting data from one format or structure
to another. Both of these solutions can help to make resources compatible and
enable data exchange. Machine learning can also be used to convert data from
one format to another, such as natural language text to structured data (Verma
et al., 2020).

### Discussion

The enhancement of semantic interoperability of data spaces is a complex task
that involves different facets and approaches. In this paper, we have focused
on specific aspects that can be improved through the use of machine learning
in the context of International Data Spaces. To achieve this, we propose the
development of machine learning-powered software that can be easily integrated
into the Data Spaces connectors as smart data apps. This will make the
software more user-friendly and accessible, allowing for seamless integration
into the existing system.

In addition, with the growing popularity of Gaia-X in Europe and beyond, this
software can also be provided as a service within the Gaia-X framework,
offering members a valuable resource for improving semantic interoperability.
By integrating machine learning into the data spaces, organizations can ensure
that their data is properly structured, and their systems can effectively
communicate and exchange information with other systems, resulting in more
efficient and effective data management and exchange.

##  4\. Conclusion

In this paper, we presented our innovative perspective on enhancing semantic
interoperability in data spaces through the use of machine learning. Our focus
was on six crucial aspects of interoperability within the International Data
Spaces architecture, and we highlighted the significance of each of these
aspects and how machine learning can improve their impact on successful data
exchange.

As a follow-up to this work, we plan to test some of the concepts and
solutions presented in this paper by integrating them into real-world data
exchange scenarios in both the International Data Spaces and Gaia-X
architectures. This will provide valuable insights into the practical
implementation and effectiveness of our proposed approach, and help to further
advance the state of the art in data interoperability and exchange.

## References

  * (1)
  * Agrawal and Agrawal (2015) Shikha Agrawal and Jitendra Agrawal. 2015.  Survey on anomaly detection using data mining techniques.  _Procedia Computer Science_ 60 (2015), 708–713.
  * Ahmed et al. (2021) Hadeer Ahmed, Issa Traore, Sherif Saad, and Mohammad Mamun. 2021.  Automated detection of unstructured context-dependent sensitive information using deep learning.  _Internet of Things_ 16 (2021), 100444.
  * Alexiev (2022) Vladimir Alexiev. 2022\.  Data Spaces vs Knowledge Graphs. In _Data Spaces & Semantic Interoperability Workshop_. TRUSTS Trusted secure data sharing space.
  * Bento et al. (2020) Alexandre Bento, Amal Zouaq, and Michel Gagnon. 2020\.  Ontology matching using convolutional neural networks. In _Proceedings of the Twelfth Language Resources and Evaluation Conference_. 5648–5653.
  * Boukhers and Bouabdallah (2022) Zeyd Boukhers and Azeddine Bouabdallah. 2022.  Vision and natural language for metadata extraction from scientific PDF documents: a multimodal approach. In _Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries_. 1–5.
  * Christen and Goiser (2019) Peter Christen and Karl Goiser. 2019.  Towards automated data linkage and deduplication.  _Computer_ 16, 17 (2019), 22–24.
  * Doan et al. (2004) AnHai Doan, Jayant Madhavan, Pedro Domingos, and Alon Halevy. 2004.  Ontology matching: A machine learning approach.  _Handbook on ontologies_ (2004), 385–403.
  * Hasan et al. (2021) Md Kamrul Hasan, Md Ashraful Alam, Shidhartho Roy, Aishwariya Dutta, Md Tasnim Jawad, and Sunanda Das. 2021.  Missing value imputation affects the performance of machine learning: A review and analysis of the literature (2010–2021).  _Informatics in Medicine Unlocked_ 27 (2021), 100799.
  * He et al. (2022) Yuan He, Jiaoyan Chen, Denvar Antonyrajah, and Ian Horrocks. 2022\.  BERTMap: a BERT-based ontology alignment system. In _Proceedings of the AAAI Conference on Artificial Intelligence_ , Vol. 36. 5684–5691.
  * Iyer et al. (2020) Vivek Iyer, Arvind Agarwal, and Harshit Kumar. 2020\.  VeeAlign: a supervised deep learning approach to ontology alignment.. In _OM@ ISWC_. 216–224.
  * Khoudja et al. (2018) Meriem Ali Khoudja, Messaouda Fareh, and Hafida Bouarfa. 2018\.  Ontology matching using neural networks: survey and analysis. In _2018 international conference on applied smart systems (ICASS)_. IEEE, 1–6.
  * Kiljander et al. (2014) Jussi Kiljander, Alfredo D’elia, Francesco Morandi, Pasi Hyttinen, Janne Takalo-Mattila, Arto Ylisaukko-Oja, Juha-Pekka Soininen, and Tullio Salmon Cinotti. 2014.  Semantic interoperability architecture for pervasive computing and internet of things.  _IEEE access_ 2 (2014), 856–873.
  * Li et al. (2018) Dingcheng Li, Ming Huang, Xiaodi Li, Yaoping Ruan, and Lixia Yao. 2018.  MfeCNN: mixture feature embedding convolutional neural network for data mapping.  _IEEE transactions on nanobioscience_ 17, 3 (2018), 165–171.
  * Lin et al. (2011) Ming-Chin Lin, Daniel J Vreeman, and Stanley M Huff. 2011\.  Investigating the semantic interoperability of laboratory data exchanged using LOINC codes in three large institutions. In _AMIA Annual Symposium Proceedings_ , Vol. 2011. American Medical Informatics Association, 805\.
  * Majeed and Lee (2020) Abdul Majeed and Sungchang Lee. 2020.  Anonymization techniques for privacy preserving data publishing: A comprehensive survey.  _IEEE access_ 9 (2020), 8512–8545.
  * Nezhadi et al. (2011) Azadeh Haratian Nezhadi, Bita Shadgar, and Alireza Osareh. 2011\.  Ontology alignment using machine learning techniques.  _International Journal of Computer Science & Information Technology_ 3, 2 (2011), 139.
  * Ouksel and Sheth (1999) Aris M. Ouksel and Amit Sheth. 1999.  Semantic interoperability in global information systems.  _ACM Sigmod Record_ 28, 1 (1999), 5–12.
  * Pang et al. (2021) Guansong Pang, Chunhua Shen, Longbing Cao, and Anton Van Den Hengel. 2021.  Deep learning for anomaly detection: A review.  _ACM computing surveys (CSUR)_ 54, 2 (2021), 1–38.
  * Park et al. (2022) Jisung Park, Jeonggyun Kim, Yeseong Kim, Sungjin Lee, and Onur Mutlu. 2022.  $\\{$DeepSketch$\\}$: A New Machine $\\{$Learning-Based$\\}$ Reference Search Technique for $\\{$Post-Deduplication$\\}$ Delta Compression. In _20th USENIX Conference on File and Storage Technologies (FAST 22)_. 247–264.
  * Raja and Thangavel (2020) PS Raja and KJSC Thangavel. 2020.  Missing value imputation using unsupervised machine learning techniques.  _Soft Computing_ 24, 6 (2020), 4361–4392.
  * Ray et al. (2021) Soumya Ray, Kamta Nath Mishra, and Sandip Dutta. 2021\.  Sensitive data identification and security assurance in cloud and IoT based networks.  _Int J Comput Netw Inf Secur_ 13 (2021), 1–25.
  * Sajid et al. (2019) Saliha Sajid, Bjørn Marius von Zernichow, Ahmet Soylu, and Dumitru Roman. 2019\.  Predictive data transformation suggestions in grafterizer using machine learning. In _Metadata and Semantic Research: 13th International Conference, MTSR 2019, Rome, Italy, October 28–31, 2019, Revised Selected Papers_. Springer, 137–149.
  * Scerri et al. (2022) Simon Scerri, Tuomo Tuikka, Irene Lopez de Vallejo, and Edward Curry. 2022.  Common European Data Spaces: Challenges and Opportunities.  _Data Spaces: Design, Deployment and Future Directions_ (2022), 337–357.
  * Shenoy et al. (2013) K Manjula Shenoy, KC Shet, and U Dinesh Acharya. 2013\.  Secured ontology matching using graph matching. In _Advances in Computing and Information Technology: Proceedings of the Second International Conference on Advances in Computing and Information Technology (ACITY) July 13-15, 2012, Chennai, India-Volume 2_. Springer, 11–18.
  * Sousa et al. (2022) Rita T Sousa, Sara Silva, and Catia Pesquita. 2022.  The Supervised Semantic Similarity Toolkit.  In _The Semantic Web: ESWC 2022 Satellite Events: Hersonissos, Crete, Greece, May 29–June 2, 2022, Proceedings_. Springer, 42–46.
  * Tarun et al. (2021) Sashi Tarun, Ranbir Singh Batth, and Sukhpreet Kaur. 2021\.  A Scheme for Data Deduplication Using Advance Machine Learning Architecture in Distributed Systems. In _2021 International Conference on Computing Sciences (ICCS)_. IEEE, 53–60.
  * Thomas and Rajabi (2021) Tressy Thomas and Enayat Rajabi. 2021.  A systematic review of machine learning-based missing value imputation techniques.  _Data Technologies and Applications_ 55, 4 (2021), 558–585.
  * Tkaczyk (2017) Dominika Tkaczyk. 2017\.  New methods for metadata extraction from scientific literature.  _arXiv preprint arXiv:1710.10201_ (2017).
  * Torra (2022) Vicenç Torra. 2022\.  Privacy for Data: Masking Methods.  In _Guide to Data Privacy: Models, Technologies, Solutions_. Springer, 159–209.
  * Verma et al. (2020) Saurav Verma, Khushboo Jain, and Chetana Prakash. 2020\.  An Unstructured to Structured Data Conversion using Machine Learning Algorithm in Internet of Things (IoT). In _Proceedings of the International Conference on Innovative Computing & Communications (ICICC)_.