Advanced Query Research Articles

Construction and Application of Place Name and Address Management System Based on Elasticsearch

Abstract. Place names and addresses serve as essential references for locating information about people's living, production, and various activity sites. They are a crucial component of public geographic framework data and act as tools and bridges for social communication in daily production and life. This paper presents a comprehensive system for managing place names and addresses by integrating a multi-level address model, data synchronization between PostgreSQL and Elasticsearch, and advanced query capabilities to ensure efficient and accurate data management. The system implements full lifecycle management of place names and addresses, fully leveraging the advantages of relational databases and distributed search engines. It provides essential functions such as address standardization, real-time updates, maintenance management, conditional queries, and statistical analysis. It has been successfully implemented in the National platform for common geospatial information service, laying the foundation for the next steps in refining social governance based on place names and addresses, and further advancing the digital transformation of government operations.

Climate warming effects on temperature structure in lentic waters: A bibliometric analysis from the recent 20 years

Global climate warming and intensified summer heatwaves have exacerbated thermal stratification in inland lakes and reservoirs, leading to increased issues of deep-water hypoxia and harmful algae blooms. This study aims to systematically review the developments, barriers, and future directions of thermal phenology in freshwaters under warming conditions through a visualized meta-analysis. According to the Web of Science Core Collection database, we retrieved 3262 articles published between January 1, 2000, and December 31, 2023, using an advanced search query that included terms related to global warming, temperature stratification, and freshwater bodies. The data was then analyzed via bibliometric visualization tools to create comprehensive visual maps, highlighting research hotspots and development trends. Key findings include a significant upward trend on this topic, in the annual number of published articles post-2015, in which China and the USA are leading in the publication output. Keyword co-occurrence analysis identified climate change, global warming, and temperature as central themes, with specific environmental issues linking to lake eutrophication and runoff being prominent as well. The study also delves into the collaboration networks among researchers, institutions, and countries, revealing strong international partnerships primarily between China, the USA, and European nations. Based on the analysis, we recommend future research should focus on integrating machine learning and advanced modeling techniques to better predict and mitigate the impacts of climate warming on thermal dynamics of inland waters. By upscaling the research from traditionally local (or regional) to global perspective, our work is vital, not just for science, but also for management of the aquatic systems under rapidly changing climate conditions.

An empirical investigation of open access scholarly publishing trends in social sciences at high-ranked central universities of India

Purpose This study aims to analyze the adoption of open access (OA) publishing in social sciences within central universities in India, focusing on various aspects such as the growth of OA literature, the use of different OA routes and collaboration patterns in OA publications. Design/methodology/approach Ten central universities were selected based on their rankings in the National Institute Ranking Framework 2022. Data on OA publishing in social sciences were collected from the Social Science Citation Index of the Web of Science (WoS) database using the advanced search query “(CU=India OR AD=India) AND PY=(2003–2022) NOT PY=(2023).” Data analysis was conducted using MS Excel (v16.0), BibExcel (version 2017), Biblioshiny (version 4.1.2) and Google Open Refine (version 3.7). Findings The study found that 30.40% of total publications were OA, with BHU as leading institute in OA publishing. OA publishing in social sciences saw a consistent increase, peaking in 2022 with 209 publications. “Sustainability” and “Plos One” were among the top ten journals, with 103 and 34 OA papers, respectively. OA publications had a higher mean citation rate than closed access publications. Collaboration with seven and nine authors had higher mean citation rates, while six-author collaborations were lower. Indian researchers received the most citations collaborating with the USA, UK and Australia. The Netherlands and Saudi Arabia received the fewer citations, when collaborating with Indian authors. Research limitations/implications The study’s main limitation is its reliance on WoS data, excluding many OA publications from smaller or specialized journals. Additionally, the focus on high-ranked central universities may not represent the entire academic landscape, as OA publishing patterns vary across other institutions and disciplines. Practical implications The study’s findings suggest that advancing OA publishing in social sciences at Indian universities requires raising awareness of OA concepts, enhancing institutional support and policies and informing researchers about funding opportunities. Emphasizing Gold OA and funding publication fees can broaden access to research. Universities with low OA ratios should adopt similar policies, mandate public research deposits and develop technical infrastructure. Encouraging multi-author collaborations can boost research impact and citation rates. Insights from the study can help institutions and policymakers shape effective OA strategies, enhancing the visibility and impact of social science research. Originality/value This is the first study analyzing the adoption of OA in the field of social sciences in high-ranked central universities in India. It has implications for promoting OA and increasing accessibility to research outputs. Universities with higher OA ratios can lead in this regard and encourage others to adopt similar practices for overall OA growth.

Integration of chromosome locations and functional aspects of enhancers and topologically associating domains in knowledge graphs enables versatile queries about gene regulation.

Knowledge about transcription factor binding and regulation, target genes, cis-regulatory modules and topologically associating domains is not only defined by functional associations like biological processes or diseases but also has a determinative genome location aspect. Here, we exploit these location and functional aspects together to develop new strategies to enable advanced data querying. Many databases have been developed to provide information about enhancers, but a schema that allows the standardized representation of data, securing interoperability between resources, has been lacking. In this work, we use knowledge graphs for the standardized representation of enhancers and topologically associating domains, together with data about their target genes, transcription factors, location on the human genome, and functional data about diseases and gene ontology annotations. We used this schema to integrate twenty-five enhancer datasets and two domain datasets, creating the most powerful integrative resource in this field to date. The knowledge graphs have been implemented using the Resource Description Framework and integrated within the open-access BioGateway knowledge network, generating a resource that contains an interoperable set of knowledge graphs (enhancers, TADs, genes, proteins, diseases, GO terms, and interactions between domains). We show how advanced queries, which combine functional and location restrictions, can be used to develop new hypotheses about functional aspects of gene expression regulation.

ADVANCED QUERY OPTIMIZATION IN SQL DATABASES FOR REAL-TIME BIG DATA ANALYTICS

This study investigates the effectiveness of advanced query optimization techniques in SQL databases, focusing on multi-level indexing, query rewriting, and dynamic query execution plans. The research employs a qualitative approach, gathering data from a variety of SQL databases characterized by large datasets typical of big data environments. Through structured interviews, focus groups, and observational methods, insights from database administrators highlight the practical benefits and challenges associated with implementing these techniques. The findings reveal significant improvements in query performance, with multi-level indexing reducing data retrieval times by approximately 40%, query rewriting decreasing execution times by 35%, and dynamic query execution plans enhancing resource utilization efficiency by 25%. These techniques were also praised for their ease of use, adaptability to different data types and query complexities, and overall reliability. This study contributes to the existing body of knowledge by providing a comprehensive analysis of the practical applications and performance enhancements offered by advanced query optimization methods in SQL databases, underscoring their value in managing large-scale, dynamic data environments.

FiReS: A semantic model for advanced querying and prediction analysis for first responders in post-disaster response plans

Natural disasters have emerged as a recurring and severe menace to the sustainability of countries, resulting in compromised environmental and infrastructural integrity, human fatalities, and significant economic repercussions. Prompt and effective response by first responders is essential in Disaster Risk Management (DRM) to reduce individuals' vulnerability and minimize environmental and infrastructural damages. However, disaster-related information is often generated by heterogeneous sources, making the first responders’ decision-making process complex and time-consuming. To address these challenges, a common conceptual model is imperative to improve interoperability among diverse organizations and software systems, enabling effective collaboration. Semantic Web technologies offer a promising solution for integrating heterogeneous data and providing well-defined meaning in the representation and exchange of DRM-related knowledge. In this context, this study introduces FiReS (First Responders System), an ontological model designed to enhance data interoperability among first responders in post-disaster response plans for advanced data analysis and machine learning prediction. The validation of FiReS is conducted through a series of case studies exploring various aspects of disaster response, such as the response time of emergency services and the volume and classification of emergency calls. This approach facilitates streamlined access, thorough analysis, and seamless exchange of information, empowering stakeholders to strengthen their disaster response strategies and foster resilience within communities.

A Systematic Review of Using Deep Learning in Aphasia: Challenges and Future Directions

In this systematic literature review, the intersection of deep learning applications within the aphasia domain is meticulously explored, acknowledging the condition’s complex nature and the nuanced challenges it presents for language comprehension and expression. By harnessing data from primary databases and employing advanced query methodologies, this study synthesizes findings from 28 relevant documents, unveiling a landscape marked by significant advancements and persistent challenges. Through a methodological lens grounded in the PRISMA framework (Version 2020) and Machine Learning-driven tools like VosViewer (Version 1.6.20) and Litmaps (Free Version), the research delineates the high variability in speech patterns, the intricacies of speech recognition, and the hurdles posed by limited and diverse datasets as core obstacles. Innovative solutions such as specialized deep learning models, data augmentation strategies, and the pivotal role of interdisciplinary collaboration in dataset annotation emerge as vital contributions to this field. The analysis culminates in identifying theoretical and practical pathways for surmounting these barriers, highlighting the potential of deep learning technologies to revolutionize aphasia assessment and treatment. This review not only consolidates current knowledge but also charts a course for future research, emphasizing the need for comprehensive datasets, model optimization, and integration into clinical workflows to enhance patient care. Ultimately, this work underscores the transformative power of deep learning in advancing aphasia diagnosis, treatment, and support, heralding a new era of innovation and interdisciplinary collaboration in addressing this challenging disorder.

Text-to-OverpassQL: A Natural Language Interface for Complex Geodata Querying of OpenStreetMap

Abstract We present Text-to-OverpassQL, a task designed to facilitate a natural language interface for querying geodata from OpenStreetMap (OSM). The Overpass Query Language (OverpassQL) allows users to formulate complex database queries and is widely adopted in the OSM ecosystem. Generating Overpass queries from natural language input serves multiple use-cases. It enables novice users to utilize OverpassQL without prior knowledge, assists experienced users with crafting advanced queries, and enables tool-augmented large language models to access information stored in the OSM database. In order to assess the performance of current sequence generation models on this task, we propose OverpassNL,1 a dataset of 8,352 queries with corresponding natural language inputs. We further introduce task specific evaluation metrics and ground the evaluation of the Text-to-OverpassQL task by executing the queries against the OSM database. We establish strong baselines by finetuning sequence-to-sequence models and adapting large language models with in-context examples. The detailed evaluation reveals strengths and weaknesses of the considered learning strategies, laying the foundations for further research into the Text-to-OverpassQL task.

Advanced Query Functionalities in Natural Logic Knowledge Bases

Natural logics are formal logics characterized by expressions that bear a resemblance to simplified natural language sentences. The inherent readability of natural logics renders them suitable also for large knowledge bases. Within the realm of natural logics, the rules of logical proof are applicable directly to the sentences constructed using this framework. This direct application ensures explainability of query inferences. In the paper we describe a specific natural logic named NaturaLog, suited for ontology-structured knowledge bases. We outline how NaturaLog can be effectively implemented into a database system to facilitate deductive querying. The primary focus of this paper lies in exploring various query functionalities and elucidating methods to achieve these capabilities, also when dealing with large-scale knowledge bases.

BopSkyline: Boosting privacy-preserving skyline query service in the cloud

With the widespread adoption of cloud computing, there has been great popularity of storing and querying databases in the cloud. However, such service outsourcing also entails critical data privacy concerns, as the cloud providers are generally not in the same trust domain as the data owners/users and could even suffer from data breaches. In this paper, different from most existing works that propose security designs for keyword search, we focus on secure realizations of advanced skyline query processing, which plays an important role in multi-criteria decision support applications. We propose BopSkyline, a new system framework for privacy-preserving skyline query service in cloud computing. BopSkyline is designed to not only ensure the confidentiality of outsourced databases, skyline queries, and query results, but also conceal data patterns (like the dominance relationships among database tuples) and search access patterns that may indirectly lead to data leakages. Notably, through a delicate synergy of key ideas on secure database shuffling and differentially private database padding, BopSkyline achieves a significant performance boost over the state-of-the-art. Extensive experiments demonstrate that compared with the state-of-the-art prior work, BopSkyline is up to 4.7× better in query latency and achieves up to 99.38% cost savings in communication.

The impact of omega-3 on recurrent seizures in epileptic patients: A case study with evidence-based approaches

Background: Epileptogenesis is also associated with increased production of excessive pro-inflammatory cytokines” which shows connection between pro-inflammatory cytokines as triggering factor with omega-3 which has anti-inflammatory effect. Omega-3, known for its neuroprotective and anticonvulsant properties, exhibits promising effects on epileptic seizure attacks.
 Objective: This study aimed to evaluate the effect of omega-3 supplementation on the incidence of seizures in epilepsy patients.
 Methods: This study used a literature search using advanced queries in the databases PubMed, Scopus, ProQuest, and Cochrane Library, and combined MeSH terms with Title/Abstract. The collected literature is discarded if there are duplicates, then literature is filtered that meets the eligibility criteria. We used the Oxford Center for Evidence-Based Medicine as a means of critical appraisal and determining the level of evidence of the selected literature.
 Results: Four chosen literatures were critically assessed. Two articles indicating no significant difference between omega-3 and placebo and two articles showed had a significantly higher number of seizure-free days than placebo. The first article, involving 78 adults, reported a 50% reduction in seizure risk with a frequency RR of 0.57, 95% CI 0.19 to 1.75 (IS = 0%) indicating no significant difference between omega-3 and placebo groups after 12 weeks of treatment. The second study, comprising mostly non-significant findings, demonstrated no significant correlation between omega-3 and epilepsy attacks in epilepsy patients. The third literature revealed seizure occurrences per month in the EPA group (9.7±1.2), DHA group (11.7±1.5), and placebo group (16.6±1.5). Incidence rate ratio (IRR) adjusted for seizure type in EPA and DHA groups compared to the placebo group were 0.61 (CI = 0.42–0.88, p = 0.008, a 42% reduction) and 0.67 (CI = 0.46–1.0, p = 0.04, a 39% reduction), respectively. Both treatment groups had a significantly higher number of seizure-free days compared to the placebo group (p < 0.05). The fourth study reported intervention group frequency (4.72 ± 1.6, p= 0.014) and placebo frequency (11.64 ± 1.63, p=0.014) with intervention group seizure duration (6.64 ± 1.39, p=0.009) compared to placebo group (14.36 ± 2.18, p=0.009).
 Conclusion: Omega-3 supplementation may be considered for management in patients with recurrent epileptic seizures, although its effect on reducing seizure frequency remains inconsistent.

Ethical considerations for artificial intelligence indermatology: a scoping review.

The field of dermatology is experiencing the rapid deployment of artificial intelligence (AI), from mobile applications (apps) for skin cancer detection to large language models like ChatGPT that can answer generalist or specialist questions about skin diagnoses. With these new applications, ethical concerns have emerged. In this scoping review, we aimed to identify the applications of AI to the field of dermatology and to understand their ethical implications. We used a multifaceted search approach, searching PubMed, MEDLINE, Cochrane Library and Google Scholar for primary literature, following the PRISMA Extension for Scoping Reviews guidance. Our advanced query included terms related to dermatology, AI and ethical considerations. Our search yielded 202 papers. After initial screening, 68 studies were included. Thirty-two were related to clinical image analysis and raised ethical concerns for misdiagnosis, data security, privacy violations and replacement of dermatologist jobs. Seventeen discussed limited skin of colour representation in datasets leading to potential misdiagnosis in the general population. Nine articles about teledermatology raised ethical concerns, including the exacerbation of health disparities, lack of standardized regulations, informed consent for AI use and privacy challenges. Seven addressed inaccuracies in the responses of large language models. Seven examined attitudes toward and trust in AI, with most patients requesting supplemental assessment by a physician to ensure reliability and accountability. Benefits of AI integration into clinical practice include increased patient access, improved clinical decision-making, efficiency and many others. However, safeguards must be put in place to ensure the ethical application of AI.

Using Natural Language Processing to Enhance the Efficiency of Digital Services: A Systematic Literature Review

The study focuses on the use of Natural Language Processing (NLP) to enhance the efficiency of digital services. The primary goal is to evaluate and analyze research published over a five-year span regarding NLP and its impact on digital services. This includes the predominant publishing mediums, high-impact journals, recurring keywords, and the geographical contribution in this domain. The research was conducted following the guidelines set by Kitchenham and Charters. The search was centered on well-established academic databases, using advanced search queries with pertinent keywords. Selected articles were assessed based on specific criteria, and key information from each article was extracted using the Mendeley Desktop tool. Research outcomes highlighted an upward trend in publishing NLP studies in high-impact journals, with a notable contribution from the United States, China, India, and the United Kingdom. The recurring keywords underscored the link between NLP, Machine Learning, and Deep Learning. Additionally, the rising significance of NLP in improving digital services was observed, indicating a bright future for its role in digital transformation.

S_IDS: An efficient skyline query algorithm over incomplete data streams

The efficient processing of mass stream data has attracted wide attention in the database field. The skyline query on the sensor data stream can monitor multiple targets in real time, to avoid abnormal events such as fire and explosion, which is very useful in the practical application of sensor data monitoring. However, real-world stream data may often contain incomplete data attributes due to faulty sensing devices or imperfect data collection techniques. Skyline queries over incomplete data streams may lead to a lack of transitivity and loop domination issues. To solve the problem of the skyline query over incomplete data streams, firstly, this paper uses differential dependency rule (DD) to fill the missing attribute values of data in the incomplete data stream. Then, the hierarchical grid index (HGrid) is introduced into the field of skyline query to improve pruning efficiency. In the process of skyline calculation, this paper only keeps as few calculation results as possible for the data that may affect the result to avoid a large number of repeated calculations. Thus, S_IDS (Skyline query algorithm over Incomplete Data Stream) is proposed to query skyline results with high confidence from the incomplete data stream. Finally, by comparing with the most advanced skyline query algorithms over incomplete data streams, the correctness and efficiency of the proposed S_IDS algorithm are proved.

AlphaFold Protein Structure Database in 2024: providing structure coverage for over 214 million protein sequences.

The AlphaFold Database Protein Structure Database (AlphaFold DB, https://alphafold.ebi.ac.uk) has significantly impacted structural biology by amassing over 214 million predicted protein structures, expanding from the initial 300k structures released in 2021. Enabled by the groundbreaking AlphaFold2 artificial intelligence (AI) system, the predictions archived in AlphaFold DB have been integrated into primary data resources such as PDB, UniProt, Ensembl, InterProand MobiDB. Our manuscript details subsequent enhancements in data archiving, covering successive releases encompassing model organisms, global health proteomes, Swiss-Prot integration, and a host of curated protein datasets. We detail the data access mechanisms of AlphaFold DB, from direct file access via FTP to advanced queries using Google Cloud Public Datasets and the programmatic access endpoints of the database. We also discuss the improvements and services added since its initial release, including enhancements to the Predicted Aligned Error viewer, customisation options for the 3D viewer, and improvements in the search engine of AlphaFold DB.

Creating a Scholarly API Cookbook: Supporting Library Users with Programmatic Access to Information

Scholarly web-based application programming interfaces (APIs) allow users to interact with information and data programmatically. Interacting with information programmatically allows users to create advanced information query workflows and quickly access machine-readable data for downstream computations. With the growing availability of scholarly APIs from open and commercial library databases, supporting access to information via an API has become a key support area for research data services in libraries. This article describes our efforts with supporting API access through the development of an online Scholarly API Cookbook. The Cookbook contains code recipes (i.e., tutorials) for getting started with 10 different scholarly APIs, including for example, Scopus, World Bank, and PubMed. API tutorials are available in Python, Bash, Matlab, and Mathematica. A tutorial for interacting with library catalog data programmatically via Z39.50 is also included, as traditional library catalog metadata is rarely available via an API. In addition to describing the Scholarly API Cookbook content, we discuss our experiences building a student research data services programming team, challenges we encountered, and ideas to improve the Cookbook. The University of Alabama Libraries Scholarly API Cookbook is freely available and hosted on GitHub. All code within the API Cookbook is licensed with the permissive MIT license, and as a result, users are free to reuse and adapt the code in their teaching and research.

SYSTEMATIC DATA PROCUREMENT IN AN OWL-EMBEDDED INFORMATION AND ANALYTICAL FRAMEWORK FOR THE MONITORING OF WATER RESOURCES IN THE ILE-BALKHASH BASIN

The world is facing an escalating water shortage crisis, with dire consequences for ecosystems, human health, and socio-economic development. This article explores the multifaceted nature of the water shortage problem of Ile-Balkhash basin that falls under the jurisdiction of the Balkhash-Alakol Republic of Kazakhstan, its underlying causes, and the complex web of challenges it presents. The predicament in the Ile-Balkhash basin is a complex interplay of various factors. Climate change has led to erratic precipitation patterns, exacerbating the problem. The simultaneous rise in population places additional stress on the already limited water resources. Moreover, inefficient water management practices have perpetuated the issue, hindering the equitable distribution of water. The challenge of conducting a comprehensive basin analysis is a formidable task due to the numerous variables and indicators involved. It demands an enormous amount of time and effort. To address this issue, a web application framework integrated with the Web Ontology Language database, allowing for the execution of advanced queries to extract valuable insights from various objects and indicators, has been developed. The database underpinning this system is meticulously compiled, drawing upon data from the Hydrological monitoring of water bodies and the National Hydrometeorological Service of the Republic of Kazakhstan. These sources provide critical data that forms the bedrock of the analysis. Recognizing the importance of data storage and management in this endeavor, integrated components have been established. These components play a pivotal role in structuring the diverse data sources and maintaining their currency. The water shortage issue in the Ile-Balkhash basin serves as a stark reminder of the urgency with which such crises must be addressed. The tools and methods offer hope and underscore global water management sustainability.

Multimodal video retrieval with CLIP: a user study

Recent machine learning advances demonstrate the effectiveness of zero-shot models trained on large amounts of data collected from the internet. Among these, CLIP (Contrastive Language-Image Pre-training) has been introduced as a multimodal model with high accuracy on a number of different tasks and domains. However, the unconstrained nature of the model begs the question whether it can be deployed in open-domain real-word applications effectively in front of non-technical users. In this paper, we evaluate whether CLIP can be used for multimodal video retrieval in a real-world environment. For this purpose, we implemented impa, an efficient shot-based retrieval system powered by CLIP. We additionally implemented advanced query functionality in a unified graphical user interface to facilitate an intuitive and efficient usage of CLIP for video retrieval tasks. Finally, we empirically evaluated our retrieval system by performing a user study with video editing professionals and journalists working in the TV news media industry. After having the participants solve open-domain video retrieval tasks, we collected data via questionnaires, interviews, and UI interaction logs. Our evaluation focused on the perceived intuitiveness of retrieval using natural language, retrieval accuracy, and how users interacted with the system’s UI. We found that our advanced features yield higher task accuracy, user ratings, and more efficient queries. Overall, our results show the importance of designing intuitive and efficient user interfaces to be able to deploy large models such as CLIP effectively in real-world scenarios.

An Ontology-Based Framework for Geospatial Integration and Querying of Raster Data Cube Using Virtual Knowledge Graphs

The integration of the raster data cube alongside another form of geospatial data (e.g., vector data) raises considerable challenges when it comes to managing and representing it using knowledge graphs. Such integration can play an invaluable role in handling the heterogeneity of geospatial data and linking the raster data cube to semantic technology standards. Many recent approaches have been attempted to address this issue, but they often lack robust formal elaboration or solely concentrate on integrating raster data cubes without considering the inclusion of semantic spatial entities along with their spatial relationships. This may constitute a major shortcoming when it comes to performing advanced geospatial queries and semantically enriching geospatial models. In this paper, we propose a framework that can enable such semantic integration and advanced querying of raster data cubes based on the virtual knowledge graph (VKG) paradigm. This framework defines a semantic representation model for raster data cubes that extends the GeoSPARQL ontology. With such a model, we can combine the semantics of raster data cubes with features-based models that involve geometries as well as spatial and topological relationships. This could allow us to formulate spatiotemporal queries using SPARQL in a natural way by using ontological concepts at an appropriate level of abstraction. We propose an implementation of the proposed framework based on a VKG system architecture. In addition, we perform an experimental evaluation to compare our framework with other existing systems in terms of performance and scalability. Finally, we show the potential and the limitations of our implementation and we discuss several possible future works.

Social Determinants of Health Documentation in Structured and Unstructured Clinical Data of Patients With Diabetes: Comparative Analysis.

Electronic health records (EHRs) have yet to fully capture social determinants of health (SDOH) due to challenges such as nonexistent or inconsistent data capture tools across clinics, lack of time, and the burden of extra steps for the clinician. However, patient clinical notes (unstructured data) may be a better source of patient-related SDOH information. It is unclear how accurately EHR data reflect patients' lived experience of SDOH. The manual process of retrieving SDOH information from clinical notes is time-consuming and not feasible. We leveraged two high-throughput tools to identify SDOH mappings to structured and unstructured patient data: PatientExploreR and Electronic Medical Record Search Engine (EMERSE). We included adult patients (≥18 years of age) receiving primary care for their diabetes at the University of California, San Francisco (UCSF), from January 1, 2018, to December 31, 2019. We used expert raters to develop a corpus using SDOH in the compendium as a knowledge base as targets for the natural language processing (NLP) text string mapping to find string stems, roots, and syntactic similarities in the clinical notes of patients with diabetes. We applied advanced built-in EMERSE NLP query parsers implemented with JavaCC. We included 4283 adult patients receiving primary care for diabetes at UCSF. Our study revealed that SDOH may be more significant in the lives of patients with diabetes than is evident from structured data recorded on EHRs. With the application of EMERSE NLP rules, we uncovered additional information from patient clinical notes on problems related to social connectionsisolation, employment, financial insecurity, housing insecurity, food insecurity, education, and stress. We discovered more patient information related to SDOH in unstructured data than in structured data. The application of this technique and further investment in similar user-friendly tools and infrastructure to extract SDOH information from unstructured data may help to identify the range of social conditions that influence patients' disease experiences and inform clinical decision-making.