Heterogeneous Data to Knowledge Graphs Matching
Creators: name: Abdelmageed, Nora
Publisher: name: Zenodo
Year: 2023
Subjects: Knowledge Graphs
Knowledge Graph Matching
Large Language Models
Named Entity Recognition
Relation Extraction
Semantic Annotation
Entity Linking

description: PhD Disputation Slides for Nora Abdelmageed (me). This is a part of public defense held on 29th June 2023 at Friedrich Schiller University Jena Germany. In the following, you find the abstract of the thesis itself. ==========Thesis Abstract====================== Many applications rely on the existence of reusable data. The FAIR (Findability, Accessibility, Interoperability, and Reusability) principles identify detailed descriptions of data and metadata as the core ingredients for achieving reusability. However, creating descriptive data requires massive manual effort. One way to ensure that data is reusable is by integrating it into Knowledge Graphs (KGs). The semantic foundation of these graphs provides the necessary description for reuse. In the Open Research KG, they propose to model artifacts of scientific endeavors, including publications and their key messages. Datasets supporting these publications are essential carriers of scientific knowledge and should be included in KGs. We focus on biodiversity research as an example domain to develop and evaluate our approach. Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. Understanding such a domain and its mechanisms is essential to preserving this vital foundation of human well-being. It is imperative to monitor the current state of biodiversity and its change over time and to understand its forces driving and preserving life in all its variety and richness. This need has resulted in numerous works being published in this field. For example, a large amount of tabular data (datasets), textual data (publications), and metadata (e.g., dataset description) have been generated. So, it is a data-rich domain with an exceptionally high need for data reuse. Managing and integrating these heterogeneous data of biodiversity research remains a big challenge. Our core research problem is how to enable the reusability of tabular data, which is one aspect of the FAIR data principles. The desired descriptions that enable successful data reuse are represented as a KG. However, today, creating such descriptions requires considerable manual effort. In the biodiversity domain, a research team searches for all datasets relevant to their research question. This happens via searches in data repositories, literature, and personal connections. On the one hand, the publications found are then read to find essential references. On the other hand, metadata about datasets are extracted from them, and the information is downloaded in the case of data repositories. All of this information is then manually collated. This process could take several months. Thus, providing well-described ata in a KG would drastically reduce the required effort. In addition, this transformation enables querying the original dataset and its secondary descriptions using structured query language like SPARQL. In this thesis, we aim to enable the automatic integration of information from various data sources within the biodiversity domain by combining semantic web technologies and machine learning techniques to extend and enrich existing KGs. We include tabular datasets by matching their components: cell, column, and column-pair to their counterparts from a KG. In addition, we enrich the resultant KG from tabular data by leveraging auxiliary information like existing metadata and the associated publications. Working towards this goal, we introduce various approaches to transform each data source into KGs. The contributions of this thesis are: 1) We propose a framework ‘JenTab’ to match tabular data to KG. We developed JenTab in the scope of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) challenge, and we provide further studies and analysis in this thesis. JenTab is a top system that tackles the tasks of transforming tabular data into KGs. 2) We construct a tabular data benchmark ‘BiodivTab’ to evalute JenTab and the existing state-of-the-art systems. BiodivTab is a biodiversity-specific benchmark that consists of 50 tables, we constructed it using manual annotations and data augmentation. 3) We design a conceptual model ‘BiodivOnto’ to determine the most commonly used concepts and their relations in the biodiversity domain. 4) We develop a model ‘BiodivBERT’ to extract entities and relations from unstructured text. 5) We construct two corpora ‘BiodivNERE’ to evaluate BiodivBERT on two downstream tasks: Named Entity Recognition (NER), and Relation Extraction (RE). Finally, 6) We develop an approach ‘Meta2KG’ to transform semi-structured data, and metadata into KGs.