Domain-specific knowledge graphs: A survey

Abu-Salih, Bilal

doi:10.1016/j.jnca.2021.103076

Cited by 219 publications

(82 citation statements)

References 175 publications

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“…Moreover, a recent survey on domain-specific knowledge graphs showed a wide adoption of knowledge graphs in the healthcare domain. [1] While knowledge graphs can be used for various purposes in the medical domain, such as the exploration of scientific literature, the focus of our work is on medical diagnosis. Similar approaches are discussed in [5,7,24,29], where knowledge graphs of symptoms and diseases are constructed from electronic medical records (EMR), medical literature, and/or other sources by means of relation extraction.…”

Section: Related Workmentioning

confidence: 99%

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Heilig¹,

Kirchhoff²,

Stumpe³

et al. 2022

Preprint

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Medical diagnosis is the process of making a prediction of the disease a patient is likely to have, given a set of symptoms and observations. This requires extensive expert knowledge, in particular when covering a large variety of diseases. Such knowledge can be coded in a knowledge graph -encompassing diseases, symptoms, and diagnosis paths. Since both the knowledge itself and its encoding can be incomplete, refining the knowledge graph with additional information helps physicians making better predictions. At the same time, for deployment in a hospital, the diagnosis must be explainable and transparent. In this paper, we present an approach using diagnosis paths in a medical knowledge graph. We show that those graphs can be refined using latent representations with RDF2vec, while the final diagnosis is still made in an explainable way. Using both an intrinsic as well as an expert-based evaluation, we show that the embedding-based prediction approach is beneficial for refining the graph with additional valid conditions.

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Section: Related Workmentioning

confidence: 99%

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Heilig¹,

Kirchhoff²,

Stumpe³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Most of the works focus on providing applications related to Knowledge Graphs. Abu-Salih et al [52] surveyed domain-specific KG to give readers a thorough review of the state-of-the-art approaches drawn from academic works relevant to different knowledge domains. Zou et al [53] conducted a bird's eye view on applications stemming from different domains like Question answering and recommendation systems.…”

Section: Related Work and Contributionsmentioning

confidence: 99%

A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

Garg¹,

Roy²

2022

Preprint

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In recent years, Knowledge Graph (KG) development has attracted significant researches considering the applications in web search, relation prediction, natural language processing, information retrieval, question answering to name a few. However, often KGs are incomplete due to which Knowledge Graph Completion (KGC) has emerged as a sub-domain of research to automatically track down the missing connections in a KG. Numerous strategies have been suggested to work out the KGC dependent on different representation procedures intended to embed triples into a low-dimensional vector space. Given the difficulties related to KGC, researchers around the world are attempting to comprehend the attributes of the problem statement. This study intends to provide an overview of knowledge bases combined with different challenges and their impacts. We discuss existing KGC approaches, including the state-of-the-art Knowledge Graph Embeddings (KGE), not only on static graphs but also for the latest trends such as multimodal, temporal, and uncertain knowledge graphs. In addition, reinforcement learning techniques are reviewed to model complex queries as a link prediction problem. Subsequently, we explored popular software packages for model training and examine open research challenges that can guide future research.

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“…They are often used by search engines and question answering services to deliver direct responses to users queries for facts about the world. From 2016-2020, interest in domain-specific knowledge graphs has increased dramatically with many applications in Health, Education, Telecommunications, Science and Business [1]. One potential reason for this increase is advances in NLP [20] that enable automatically constructing high quality knowledge graphs.…”

Section: Knowledge Graphs and Association Datamentioning

confidence: 99%

“…For PopNet we combine structured knowledge with associations to generate a network then can be easily explored for creative tasks. Based on a survey of research on creating knowledge graphs, there is a generally accepted way of using NLP to construct knowledge graphs from plain text [1]. First parse the text into sentences.…”

Section: Popnet Systemmentioning

confidence: 99%

PopBlends: Strategies for Conceptual Blending with Large Language Models

Wang¹,

Petridis²,

Kwon³

et al. 2021

Preprint

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Fig. 1. Star Wars Day creative visuals collected on Twitter from (a) McDonald's, (b) Volkswagen, and (c) Girl ScoutsPop culture is a pervasive and important aspect of communication and self-expression. When people wish to communicate using pop culture references, they need to find connections between their message and the things, people, location and actions of a movie, tv series, or other pop culture domain. However, finding an appropriate match from memory is challenging and search engines are not specific enough to the task. Often domain-specific knowledge graphs provide the structure, specificity and search capabilities that people need. We introduce PopNet -a Pop Culture Knowledge Association Network automatically created from plain text using state-of-the art NLP methods to extract entities and actions from text summaries of movies and tv shows. The interface allows people to browse and search the entries to find connections. We conduct a study showing that this system is accurate and helpful for finding multiple connections between a message and a pop culture domain.CCS Concepts: • Human-centered computing → Interactive systems and tools.

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Domain-specific knowledge graphs: A survey

Cited by 219 publications

References 175 publications

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

Refining Diagnosis Paths for Medical Diagnosis based on an Augmented Knowledge Graph

A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges

PopBlends: Strategies for Conceptual Blending with Large Language Models

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