Corpus processing service: A Knowledge Graph platform to perform deep data exploration on corpora

Staar, Peter; Dolfi, Michele; Auer, Christoph

doi:10.1002/ail2.20

Cited by 8 publications

(3 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We developed a framework for automating the construction of a multimodal knowledge graph by extracting and fusing data from a variety of sources, including text delimited files, JSON, and proprietary data sources (Staar, Dolfi, and Auer 2020). The framework takes as input a schema file (specified in JSON), which declaratively describes how to build the desired graph from a set of data sources.…”

Section: Multimodal Knowledge Graph Constructionmentioning

confidence: 99%

Knowledge Enhanced Representation Learning for Drug Discovery

Hoang,

Sbodio,

Martinez Galindo

et al. 2024

AAAI

View full text Add to dashboard Cite

Recent research on predicting the binding affinity between drug molecules and proteins use representations learned, through unsupervised learning techniques, from large databases of molecule SMILES and protein sequences. While these representations have significantly enhanced the predictions, they are usually based on a limited set of modalities, and they do not exploit available knowledge about existing relations among molecules and proteins. Our study reveals that enhanced representations, derived from multimodal knowledge graphs describing relations among molecules and proteins, lead to state-of-the-art results in well-established benchmarks (first place in the leaderboard for Therapeutics Data Commons benchmark ``Drug-Target Interaction Domain Generalization Benchmark", with an improvement of 8 points with respect to previous best result). Moreover, our results significantly surpass those achieved in standard benchmarks by using conventional pre-trained representations that rely only on sequence or SMILES data. We release our multimodal knowledge graphs, integrating data from seven public data sources, and which contain over 30 million triples. Pretrained models from our proposed graphs and benchmark task source code are also released.

show abstract

Section: Multimodal Knowledge Graph Constructionmentioning

confidence: 99%

Knowledge Enhanced Representation Learning for Drug Discovery

Hoang,

Sbodio,

Martinez Galindo

et al. 2024

AAAI

View full text Add to dashboard Cite

show abstract

“…Machine learning demonstrates vast potential in constructing and analyzing knowledge graphs, spanning various aspects from automated data mining [168] to deep knowledge exploration [169] and intelligent decision support [170]. Technological advancements have enabled machine learning not only to enhance the efficiency of constructing knowledge graphs but also to strengthen their analytical capabilities, bringing innovative breakthroughs to different fields.…”

Section: Prospects For Machine Learning In Knowledge Graphs Construct...mentioning

confidence: 99%

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Zhong,

Yang,

Shi

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

show abstract

“…IBM deep search 12 leverages ML models to extract content from ingested documents using the Corpus conversion service with high accuracy. They leverage the Corpus processing service 13 to apply natural language processing techniques (e.g., named‐entity recognition, fact extraction) to build knowledge graphs for data consumers in performing contextual information retrieval and knowledge discovery. COVIDScholar 14 handles the needs of data consumers by developing a literature analytic platform that collects, processes, and manages over 150,000 documents for actionable insights.…”

Section: Related Workmentioning

confidence: 99%

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

Oruche

Milman

Alarcon

et al. 2022

Concurrency and Computation

View full text Add to dashboard Cite

There is a growing need for next-generation science gateways to increase the accessibility of emerging large-scale datasets for data consumers (e.g., clinicians, researchers) who aim to combat COVID-19-related challenges. Such science gateways that enable access to distributed computing resources for large-scale data management need to be made more programmable, extensible, and scalable. In this article, we propose a novel socio-technical approach for developing a next-generation healthcare science gateway, namely, OnTimeEvidence that addresses data consumer challenges surrounding the COVID-19 pandemic related data analytics. OnTimeEvidence implements an intelligent agent, namely, Vidura Advisor that integrates an evidence-based filtering method to transform manual practices and improve scalability of data analytics. It also features a plug-in management middleware that improves the programmability and extensibility of the science gateway capabilities using microservices. Lastly, we present a usability study that shows the important factors from data consumers' perspective to adopt OnTimeEvidence with chatbot-assisted middleware support to increase their productivity and collaborations to access vast publication archives for rapid knowledge discovery tasks. K E Y W O R D Sdiffusion of innovations, intelligent agents, intelligent middleware, microservices, science gateways INTRODUCTIONManaging the emerging collections of large-scale medical datasets such as scientific publications and electronic health records (EHRs) can be a challenging task for medical data consumers (e.g., clinicians, medical researchers) who need to make timely decisions for combating COVID-19-related issues. Data consumers are constantly faced with complex tasks that are labor-intensive and require domain-specific knowledge discovery over medical information for critical decision making. When synthesizing scientific literature for knowledge discovery, data consumers often rely clinical methodologies such as levels of evidence 1,2 to improve information reliability and reduce the quantity of literature by prioritizing scientific rigor (e.g., expert opinions to systematic reviews and meta-analyses). The challenge of manually filtering high-volume of literature based on evidence-based methods presents the need from data consumers to adopt next-generation science gateways to gain access to emerging large-scale datasets and resources for developing timely pandemic-related solutions.Any opinions, findings, and conclusions or recommendations expressed in this publication are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

show abstract

Corpus processing service: A Knowledge Graph platform to perform deep data exploration on corpora

Cited by 8 publications

References 9 publications

Knowledge Enhanced Representation Learning for Drug Discovery

Knowledge Enhanced Representation Learning for Drug Discovery

From data to insights: the application and challenges of knowledge graphs in intelligent audit

Science gateway adoption using plug‐in middleware for evidence‐based healthcare data management

Contact Info

Product

Resources

About