2023 FromLanguageModelstoLargeScaleF
- (Cenikj et al., 2023) ⇒ Gjorgjina Cenikj, Lidija Strojnik, Risto Angelski, Nives Ogrinc, Barbara Koroušić Seljak, and Tome Eftimov. (2023). “From Language Models to Large-scale Food and Biomedical Knowledge Graphs.” In: Scientific reports, 13(1).
Subject Headings: Biomedical Knowledge, Biomedical Knowledge Graph.
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Abstract
Knowledge about the interactions between dietary and biomedical factors is scattered throughout uncountable research articles in an unstructured form (e.g., text, images, etc.) and requires automatic structuring so that it can be provided to medical professionals in a suitable format. Various biomedical knowledge graphs exist, however, they require further extension with relations between food and biomedical entities. In this study, we evaluate the performance of three state-of-the-art relation-mining pipelines (FooDis, FoodChem and ChemDis) which extract relations between food, chemical and disease entities from textual data. We perform two case studies, where relations were automatically extracted by the pipelines and validated by domain experts. The results show that the pipelines can extract relations with an average precision around 70%, making new discoveries available to domain experts with reduced human effort, since the domain experts should only evaluate the results, instead of finding, and reading all new scientific papers.
Introduction
Noncommunicable chronic diseases (NCDs) account for more than 70% of deaths worldwide. Cardiovascular diseases account for most NCD deaths (17.9 M people annually), followed by cancerrs (9.3 M), respiratory diseases (4.1 M), and diabetes mellitus (1.5 M)1,2. As the leading cause of death globally, most of the deaths that happen from cardiovascular diseases (CVDs) are due to heart attacks and strokes3. A lot of scientific evidence indicates that between the most important risk factors for heart disease and stroke are unhealthy diet, alcohol and tobacco consumption, and physical activity. Among all the factors that contribute to the development and progression of CVDs, diet is one of the major ones4,5. It has been shown that eating more fruit and vegetables and decreasing the salt in diet reduce the risk of CVDs.
Further, although there is a lot of knowledge about dietary effects on CVDs and broadly on NCDs, there are still many unresolved research questions. Such questions are not easy to be answered because food and nutrition in relation to diseases are described by various concepts and entities that interact in various ways6. For instance, there are many foods (described by food entities) made up of components (described by chemical entities)7 that may fight NCDs (described by disease entities) while others can be harmful8. These impacts are dependent on the combination of foods and their chemicals, the state of the food (e.g., raw/cooked, fresh/molded, etc.), the cooking method (e.g., steamed, grilled, baked, etc.), the health status of the person consuming food (e.g., healthy, ill, allergic) and others9. As there are many combinations of these factors, collecting and structuring the relations between all the concepts and entities describing the impacts of food on NCDs is a very complex work exceeding human capabilities. And taking into account the fact that research in this field is still progressing, the related knowledge evolves on a daily basis, making it challenging to follow. Such knowledge further opens possibilities to use Artificial Intelligence (AI) methods to aid in the early detection (prediction) of NCDs as well as their progression. However, before developing predictive AI methods, unstructured (textual) data available in cohorts, electronic health records (EHRs), registries, and scientific and grey literature needs to be structured and normalized/linked to domain semantic resources and further included in knowledge bases (KBs) which can be utilized for predictive modeling and integrated into health systems which will make the information easily accessible to medical professionals. To this end, user interfaces play a critical role in ensuring that healthcare professionals can effectively utilize AI systems to provide high-quality care to their patients10.
A Knowledge Graph (KG) is a type of KB, where knowledge is stored in the form of entities characterized by some attributes, and relations connecting the entities. Conventional methods of KG construction can be broadly categorized into manual, and automatic, or semi-automatic methods. The benefits of manual creation and curation approaches are their high precision and reliability11, however, due to the high amount of effort required by domain experts, they also have lower recall rates, poor scalability and time efficiency12. Automatic and semi-automatic KG construction is enabled by text-mining methods, which are able to extract entities and relations which can be structured as a KG.
In the biomedical domain, automatic and semi-automatic structuring of textual data in the form of KGs is an active research area, which typically involves the use of Information Extraction (IE) pipelines consisting of multiple components. These components include Named Entity Recognition (NER) methods, which extract specific types of entities from raw text, Named Entity Linking (NEL) methods, whose goal is to map entity mentions to entries in a given KB, and Relation Extraction (RE) methods, which aim to automatically detect relations between entities13. Over the past 20 years, significant progress has been made in creating multiple IE pipelines for the biomedical domain. These pipelines primarily concentrate on identifying genotype and phenotype entities, as well as health-related entities such as diseases, treatments, drugs, and others. To allow their development, several collaborative workshops, as part of conference events like BioNLP14, BioCreative15, i2b216, and DDIExtraction17, have been arranged to provide semantic resources (e.g., annotated corpora, ontologies) that will further allow the developing of biomedical IE pipelines. The efforts done in the biomedical domain are focused entirely on biomedical concepts and not investigating relations with food concepts. On the other side, most of the efforts done in IE in the food domain are focused on relations that do not involve health/biomedical concepts, and even more, are developed using static data that is already presented in some other resources (e.g., datasets, controlled vocabularies, ontologies), so they need to be updated when new data is available in these resources. In addition, only a few studies have concentrated on traditional text mining techniques that employ sentiment analysis through manual feature extraction18,19,20. Despite this, the food and nutrition domain is low-resourced in semantic data resources compared to the biomedical domain. There is a lack of annotated food-disease relation corpora that serve as a benchmark and help develop IE pipelines. Even more, food semantic resources such as FoodOn21, FoodEx222, are still under development (i.e., frequently updating them with new data) to support IE activities.
To bridge the gap between the food and biomedical domains, we introduce an approach that uses language models to extract the relations that exist between food, chemical, and disease entities and further normalize them to allow the creation of a KG. In our case, we evaluate the approach to trace the new knowledge about CVDs and milk products. The benefit of our approach is that we are not using the information that already exists in some static resources (e.g., databases), but try to catch all relations from textual data related to CVDs and milk products (milk was selected as a case study since it is rich in nutrients, a resource of proteins, vitamins, minerals, and fatty acids, which have an important impact on human metabolism and health) available in scientific abstracts, where new findings are presented. This makes the methodology easy to apply on new corpora of scientific abstracts, where the results of the pipelines can point out areas where the KG should be updated with new entities or relations.
Related work
A recent survey on knowledge-based biomedical data science23 highlights the application of KGs in the biomedical and clinical domain in improving the retrieval of information from large sources of clinical data or literature24,25,26, providing evidence to support phenomena observed in data27,28, using link prediction to complete missing information and hypothesize previously unknown relationships29, and improving patient data representation30,31,32. In the biomedical domain, IE pipelines have been developed for the extraction of drug-disease relations33,34 and disease-symptom relations35 from biomedical literature. A Coronavirus KG has been constructed by merging the Analytical Graph, with a collection of published scientific articles36. A PubMed KG has been constructed by extracting biomedical entities from PubMed abstracts and enriching it with funding, author, and affiliation data37. A recent work12 proposes the construction of domain-specific KGs with minimal supervision, which is able to derive open-ended relations from unstructured biomedical articles without the need of extensive labeling. While this study is largely focused on data integration, and only uses NER to extract the biomedical entities from the literature, our study goes a step further in the RE task, to extract the relations between the entities based on the text in the scientific abstracts, so that new relations can be added between entities in existing resources. Apart from using biomedical scientific papers as a source of information, EHRs have also been used for extracting disease-symptom relations38 and constructing a medical KG with nine biomedical entity types39.
In the food domain, FoodKG has been recently developed for representing food recipe data including their ingredients and nutritional content40 by enriching a large amount of recipe data from Recipe1M dataset with the nutritional information available from USDA’s National Nutrient Database for Standard Reference represented with FoodOn21 semantic meta-data. Additionally, FoodKG41 was developed by using the existing text and graph embedding techniques applied to a controlled vocabulary called AGROVOC, to model the relations that exist in a plethora of datasets related to food, energy and water.
Results
To trace the knowledge about food, chemical, and disease interactions, we have shown the creation of a KG centered around the impact of different foods and chemicals on CVDs, and the other targeting the composition of the selected food item “milk”, as well as its beneficial and detrimental effects on different NCDs. For this purpose, three NLP pipelines, called FooDis, FoodChem, and ChemDis, were combined to extract “food-disease”, “food-chemical”, and “chemical-disease” relations from textual data. Semantically, we distinguish two relations between food-disease and chemical-disease entity pairs, which are “treat” and “cause”. In the case of food-chemical entity pairs, we extracted only one relation which is “contains”. All three pipelines were executed twice, on two different corpora, one that was collected for CVDs and one collected for milk products. In both use cases, the searched keywords were selected by domain experts. In the CVDs case, a more general keyword was selected “heart disease food”, since we would like to retrieve broader aspects between different cardiovascular events and food products. This ends up with 9984 abstracts. In the milk use case, three keywords were selected by the domain experts i.e., “milk composition”, “milk disease”, and “milk health benefits”.
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References
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| Author | volume | Date Value | title | type | journal | titleUrl | doi | note | year | |
|---|---|---|---|---|---|---|---|---|---|---|
| 2023 FromLanguageModelstoLargeScaleF | Gjorgjina Cenikj Lidija Strojnik Risto Angelski Nives Ogrinc Tome Eftimov Barbara Koroušić Seljak | From Language Models to Large-scale Food and Biomedical Knowledge Graphs | 2023 |