ADA: Automatic Data Annotation for Data Ecosystems

Abstract

Data ecosystems have emerged as versatile platforms for managing and analyzing data from diverse sources, facilitating integration, collaboration and governance across organizations and systems. Annotated data are crucial for eficient and efective large-scale data ecosystems. However, there is a lack of full-fledged automatic annotation approaches for data ecosystems, with manual annotation by experts being the current requirement. Addressing specific annotation requirements of data ecosystems, we introduce ADA, an approach for automatic data annotation. ADA applies a semantic representation model called Data Product Description Object (DPDO) in JSON-LD and combines state-of-the-art models for metadata embeddings within an annotation pipeline. The approach extends technical metadata by essential concepts for data ecosystems, such as data provenance, quality, and accessibility. The efectiveness of ADA was evaluated using competency questions and data sets from diverse domains within the GAIA-X data ecosystem.

Keywords

• Data ecosystems
• Automatic data annotation
• Metadata
• Ontology

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CEUR ceur-ws.org

1. Introduction

Data ecosystems consist of centralized or decentralized platforms for managing and analyzing data from various sources, e.g., structured data, text, or images [ 1 ]. They are designed to facilitate data integration, sharing, collaboration, and governance across diferent systems, applications, CEUR Workshop Proceedings 3https://www.plattform-i40.de/IP/Navigation/EN/Manufacturing-X/Manufacturing-X.html annotations for data ecosystems [ 6, 7, 8 ], there is a lack of full-fledged automatic annotation approaches for data ecosystems. So far, manual annotation by experts is required [ 9 ] (e.g., labeling training data, assigning data to developed concepts). Tackling the specific annotation requirements of data ecosystems with respect to data provenance, quality and context, accessibility, availability, and contractual information of open domain data is beyond the scope of existing research on automatic data annotation [ 2 ], e.g., [ 10, 11, 12 ]. In this work, we introduce ADA – an approach for automatic data annotation for data ecosystems. ADA works with a semantic representation model called Data Product Description Object (DPDO) operationalized in JSON-LD and combines multiple state-of-the-art models for metadata embeddings within an annotation pipeline (e.g., ontology development and knowledge graph population[ 13 ], metadata harvesting and extraction [ 14 ]). ADA builds up on existing ontological standards (e.g., Data Catalogue Vocabulary 4) and extends technical metadata by essential concepts for data ecosystems, e.g., data provenance, quality, and accessibility. ADA supports open domain structured data sets, i.e., tabular data (CSV format). The approach was exemplified within an annotator service for automatic data annotation in data ecosystems. We were able to evaluate ADA by means of competency questions as well as data sets of diverse domains listed by the GAIA-X data ecosystem2 extracted from Kaggle5, e.g., agriculture, construction, energy, geoinformation, or culture.

2. Automatic Data Annotation for Data Ecosystems (ADA)

In order to satisfy the requirements of data ecosystems, we first developed the DPDO which serves as semantic foundation for the annotation process. Our automatic annotation pipeline consists of 3 components: Analyzer, controller, and provision engine (see. figure 1).

Semantic representation model: Based on existing literature [ 15, 16, 17, 18, 19 ] and publicly available vocabularies (schema.org6, Data Quality Vocabulary7, Open Vocab8, 4https://www.w3.org/TR/vocab-dcat-3/ 5https://www.kaggle.com/datasets 6https://schema.org/ 7https://www.w3.org/TR/vocab-dqv/ 8https://vocab.org/open/ Data Catalog Vocabulary9, DCMI Metadata Terms10, GAIA-X ontology11 12), the semantic representation models information on data in data ecosystems within five facets 13 (see figure 2). Potential data consumers require a product description in terms of context and metadata (e.g., topic, datatypes, data size). Data quality (e.g., referencing existing data quality standards such as ISO 8000 14, metrics for the calculation of a quality score or accuracy) lays the foundation for the usability of data. Information on accessibility (i.e., access URL, technical support) and timeliness (i.e., last data modification, historical information on data versioning) enables the actual usage of the data. A data business transaction between ecosystem participants requires contractual information (i.e., contract description, price specifications, sanctions) and information about usage rights (e.g., license). Furthermore, trust between participants can be established by having information on the data provenance (i.e., name, locality, and contact of the data provider).

Analyzer component: Using the GUI, users can upload their data intended to be annotated. Within our demonstrator, they are additionally provided with sample JSON files that need to be uploaded alongside the data as additional input on the provider, contractual details, and data quality in order to fill the DPDO. In a real-life scenario, while participating in a data ecosystem, this information is expected to be filled once and then to be stored within respective platforms as described by existing concepts 1 2. The analyzer (see figure 1) then reads all files in order to extract the information required by the DPDO. Embedded metadata within the data file are harvested as described in [ 14 ] (i.e., title of the file, file size) or extracted directly from the file’s content as in [ 14 ] (i.e., column names, data types). Column names and title of the file are used to generate a thematic allocation and to disambiguate the content of the data file by using them as lemmas to search for synsets and hypernyms in BabelNet15. Furthermore, the additional input JSON files are parsed and given alongside all other extracted information to the controller.

Controller component: The controller (see. figure 1) assigns all gathered information by the analyzer to a semantic description. The DPDO is operationalized in the form of JSON-LD, serving as a marker template that is filled rule-based with the results of the processed input ifles. The controller automatically maps extracted metadata, synsets and hypernyms to entities 9https://www.w3.org/TR/vocab-dcat-3/ 10https://www.dublincore.org/specifications/dublin-core/dcmi-terms/ 11https://gaia-x.gitlab.io/gaia-x-community/gaia-x-self-descriptions/core/core.html 12https://gaia-x.gitlab.io/technical-committee/service-characteristics/widoco/participant/participant.html 13A complete overview of modeled entities is given in our repository https://github.com/InformationServiceSystems/ pairs-project/tree/main/Modules/ADA 14https://www.iso.org/standard/81745.html 15https://babelnet.org/ of the product description facet (see figure 2). Information on the user registry is mapped to the trust description facet and contractual information to the business and usage description facet. The additional user input is mapped onto remaining entities of all facets of the specified semantic scheme of the DPDO.

Provision engine: The provision engine (see. figure 1) transforms the generated JSON-LD ifle into a graph to be stored within the knowledge graph database Neo4j 16 using a Cypher script. While we could have used a triplestore for storing our knowledge graph, one reason why we chose Neo4j is, that information on entities and relationships can be stored without creating extra nodes, leading to a more condensed representation of the data, which is particularly relevant in the case of large-scale data ecosystems. The resulting knowledge graph serves then as data catalogue or knowledge base within a data ecosystem 1 2 3 that can be explored and queried.

Evaluation: We evaluated our approach using 16 competency questions (CQ) extracted from related work within the domain of semantic annotations and data ecosystems. For deriving these competency questions, we specifically focused on information to be required by stakeholders within data ecosystems. Examples would be ’Which context is the focus of the dataset?’, ’Which datatypes are used?’, ’What’s the size of the dataset?’17. Furthermore, we annotated publicly available data sets17 matching listed domains by the GAIA-X ecosystem, which we extracted from Kaggle (i.e., agriculture, energy, construction, finances, geo data, industry, culture, education, mobility, public sector, smart living). We investigated the resulting knowledge graph based on the defined CQ with the help of Cypher queries. We were able to answer all 16 CQs.

3. Conclusion

In this paper, we proposed ADA, an approach for automatic data annotation in data ecosystems. ADA leverages the semantic representation model DPDO (JSON-LD) and integrates state-ofthe-art models for metadata embeddings, enabling seamless integration, sharing, governance, exploration, and large-scale data analysis within data ecosystems. ADA supports open domain structured data sets, i.e., tabular data in CSV format, and can be used by annotating experts and non-experts. By extending technical metadata with essential concepts such as data provenance, quality, and accessibility, ADA addresses the specific annotation requirements of data ecosystems and their stakeholders. The evaluation of ADA through competency questions and diverse tabular data sets demonstrates its efectiveness in supporting open domain structured data sets within the GAIA-X data ecosystem and beyond18. 16https://neo4j.com/ 17All competency questions, references of related work, used data sets, executed queries, and query results are listed within our repository. 18Demonstration is given within a screencast https://youtu.be/2af0_IButIA; Code of the service and evaluation results can be found within our GitHub repository https://github.com/InformationServiceSystems/pairs-project/ tree/main/Modules/ADA.

4. Acknowledgement

This work was partially funded by the German Federal Ministry of Economics and Climate Protection (BMWK) under the contracts 01MK21008D and 01MK20015A.

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