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DOE OSTI2019

Integration of Local Knowledge is a Key for the Remediation Success of Legacy Uranium Mine Sites - 19564

A key to success, during all aspects of the remediation process on legacy uranium sites, and easily transferable between countries and projects, is the integration of local knowledge. However, before this knowledge can be integrated, local trust and project understanding must be developed. Local trust and project understanding can only be developed through a principle-centred approach to a shared project vision, two-way communication of knowledge, alignment of project goals, and understanding the local needs early in the remediation process. Once public confidence is obtained, local trust and project understanding must be maintained throughout the entirety of the remediation process. Project understanding is an ongoing endeavor that needs continuous communication and varied methods to succeed. During the management of assessment, remediation, and post remediation monitoring on the Saskatchewan Research Council's CLEANS project (remediation of 37 legacy uranium mine sites in Northern Saskatchewan, Canada), integration of local knowledge has been an instrumental tool attributing to success. During site assessment, integration of local knowledge has included the development and implementation of land use surveys to determine realistic travel and occupancy on and adjacent to these legacy sites, and site specific traditional knowledge and land use studies that have contributed to the development of a detailed knowledge of local land use, resource utilization, country food intake, and human history of the sites. The local knowledge gained during the assessment process is invaluable as it provides information required to support remediation planning, such as: valued ecosystem components, human trophic utilization, length of site occupancy, cultural value assessment, and future land use considerations. During remediation planning, local and traditional knowledge can also be incorporated through determination of plant species suitability for revegetation, development and selection of remediation options, and setting realistic sustainable development targets for local project inclusion. During site remediation operations, local involvement can be utilized through direct labour and equipment, cultural resource monitoring (i.e. archaeological and heritage protection), local logistics and historic information transfer, and stakeholder communications and engagement support. Post remediation, local involvement is an efficient mechanism for long-term monitoring, and care and maintenance activities. Although there may be differences between countries, regions and sites, the remediation process on legacy uranium mines can universally be more successful through the integration of local knowledge; while developing rights/stakeholder engagement and capacity building. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2015

Knowledge brokering on emissions modelling in Strategic Environmental Assessment of Estonian energy policy with special reference to the LEAP model

Strategic Environmental Assessment (SEA) serves as a platform for bringing together researchers, policy developers and other stakeholders to evaluate and communicate significant environmental and socio-economic effects of policies, plans and programmes. Quantitative computer models can facilitate knowledge exchange between various parties that strive to use scientific findings to guide policy-making decisions. The process of facilitating knowledge generation and exchange, i.e. knowledge brokerage, has been increasingly explored, but there is not much evidence in the literature on how knowledge brokerage activities are used in full cycles of SEAs which employ quantitative models. We report on the SEA process of the national energy plan with reflections on where and how the Long-range Energy Alternatives Planning (LEAP) model was used for knowledge brokerage on emissions modelling between researchers and policy developers. Our main suggestion is that applying a quantitative model not only in ex ante, but also ex post scenario modelling and associated impact assessment can facilitate systematic and inspiring knowledge exchange process on a policy problem and capacity building of participating actors. - Highlights: • We examine the knowledge brokering on emissions modelling between researchers and policy developers in a full cycle of SEA. • Knowledge exchange process can evolve at any modelling stage within SEA. • Ex post scenario modelling enables systematic knowledge exchange and learning on a policy problem.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Knowledge-guided learning with curated prior genetic biomarkers for robust model interpretation

Abstract Motivation Knowledge-guided learning offers effective and robust model training strategies in data-scarce settings by incorporating established domain knowledge, thereby enhancing generalization, robustness, and interpretability. By contrast, conventional deep learning approaches rely purely on data-driven learning, which can limit robust model interpretability, particularly in high-dimensional settings with limited size samples. In computational biology, knowledge-guided learning has primarily leveraged network- and structural-based knowledge, leading to biologically interpretable representations and enhanced predictive performance compared to conventional approaches. However, curated biomarkers, one of the most accessible forms of biological knowledge, remain largely unexplored within knowledge-guided paradigms. Results In this study, we propose a model-agnostic training paradigm, Biomarker-driven Explainable Prior-guided Learning (BioExPL), that can be applied to any neural networks that incorporates curated prior knowledge. BioExPL enforces neural networks to reflect curated biomarker priors in their latent representations through a novel knowledge-alignment loss. BioExPL consistently demonstrated significantly improved predictive performance and enhanced model interpretability with minimized computational overhead in simulation studies and intensive experiments on multiple cancer datasets. BioExPL not only integrates prior curated knowledge into the model but also accurately identifies unknown associated signals additionally. BioExPL is model-agnostic and domain-independent, enabling its integration into diverse neural network architectures. Availability and implementation The open-source is publicly available at: https://github.com/datax-lab/BioExPL.

Baek, Beomsu [Department of Computer Science, Univ↗

DOE OSTI2015

MO-DE-BRA-05: Developing Effective Medical Physics Knowledge Structures: Models and Methods

Purpose: Develop a method and supporting online resources to be used by medical physics educators for teaching medical imaging professionals and trainees so they develop highly-effective physics knowledge structures that can contribute to improved diagnostic image quality on a global basis. Methods: The different types of mental knowledge structures were analyzed and modeled with respect to both the learning and teaching process for their development and the functions or tasks that can be performed with the knowledge. While symbolic verbal and mathematical knowledge structures are very important in medical physics for many purposes, the tasks of applying physics in clinical imaging--especially to optimize image quality and diagnostic accuracy--requires a sensory conceptual knowledge structure, specifically, an interconnected network of visually based concepts. This type of knowledge supports tasks such as analysis, evaluation, problem solving, interacting, and creating solutions. Traditional educational methods including lectures, online modules, and many texts are serial procedures and limited with respect to developing interconnected conceptual networks. A method consisting of the synergistic combination of on-site medical physics teachers and the online resource, CONET (Concept network developer), has been developed and made available for the topic Radiographic Image Quality. This was selected as the inaugural topic, others to follow, because it can be used by medical physicists teaching the large population of medical imaging professionals, such as radiology residents, who can apply the knowledge. Results: Tutorials for medical physics educators on developing effective knowledge structures are being presented and published and CONET is available with open access for all to use. Conclusion: An adjunct to traditional medical physics educational methods with the added focus on sensory concept development provides opportunities for medical physics teachers to share their knowledge and experience at a higher cognitive level and produce medical professionals with the enhanced ability to apply physics to clinical procedures.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Knowledge Oriented Graph Unified Transformer (KOGUT) v0.1

KOGUT — Knowledge Oriented Graph Unified Transformer KOGUT implements the Relational Graph Transformer (RelGT) architecture for knowledge graph link prediction in biological domains, with a primary focus on microbial growth media prediction. While the original RelGT (arXiv:2505.10960) targets relational tables, time series, and multi-table databases, KOGUT adapts this architecture for heterogeneous biological knowledge graphs, providing first-in-class AI predictive models for microbial cultivation. Key Adaptations Beyond Original RelGT: - Knowledge Graph Focus: Applied to biological KGs with semantic node types (taxa, chemicals, media, phenotypes, environments) versus generic relational database tables, trained on the KG-Microbe knowledge graph (1.3M entities, 2.9M edges, 24 relation types). - Multimodal Node Encoding: Integrates node labels, categories, descriptions, and synonyms from KG metadata through learned embedding layers—adapting relational column features to graph node attributes with textual semantics. - Extended K-Hop Subgraph Strategy: Optimized neighborhood sampling (3-hop default, configurable up to 200 nodes) tuned for sparse biological networks, building on the original local-global attention framework with biological relation preservation. - Biolink Predicate Preservation: Type-specific transformations for 24 biological edge semantics (occurs_in, consumes, produces, has_phenotype, subclass_of) beyond standard relational foreign keys, enabling multi-relation link prediction. - Inductive Learning Support: Enables zero-shot predictions for novel taxa through feature-based embeddings (temperature, oxygen requirements, gram stain, cell shape), extending the original transductive relational benchmark scope to uncultured microorganisms. CheapSOTA Performance Optimizations (This Distribution): - VQ-EMA Centroid Attention: Vector quantization with exponential moving average for improved global context modeling (+5-10% MRR improvement). - HDF5 Precomputed Data Loading: One-time preprocessing of k-hop subgraphs to eliminate redundant graph traversals (2-5× training speedup). - Distributed Data Parallel Training: Multi-GPU support for scaling to larger knowledge graphs (tested on 4× NVIDIA A100 GPUs at NERSC Perlmutter). - Mixed Precision Training: Automatic mixed precision (AMP) for memory efficiency and faster training. Advantages Over Standard Knowledge Graph Embedding Models: Combines RelGT's proven multi-element tokenization (features, type, hop, structure) with graph-native biological representations, enabling interpretable link prediction across heterogeneous entities that standard embedding models (TransE, RotatE, ComplEx) and table-based transformers cannot directly model. Achieves near-perfect performance on microbial growth media prediction (MRR: 0.9966, Precision@1: 0.9932, Hit@10: 1.0000) while maintaining explainability through attention-based reasoning over biological pathways. Training Data: - KG-Microbe merged knowledge graph: 1,379,337 nodes, 2,960,472 edges - 24 biological relation types including taxonomic hierarchies, metabolic interactions, phenotype associations, and environmental relationships - Primary prediction task: Growth media suitability for microbial taxa (biolink:occurs_in, 50K edges) - Multi-relation capability: Predicts links for any of the 24 relation types, including chemical consumption/production, phenotype associations, and taxonomic classification Citation: Original RelGT Architecture: Dwivedi et al., "Relational Graph Transformer", arXiv:2505.10960, 2025 KOGUT Implementation: Knowledge Oriented Graph Unified Transformer for Microbial Growth Media Prediction Developed at Lawrence Berkeley National Laboratory (LBNL) Trained on NERSC Perlmutter supercomputer

Joachimiak, Marcin [Lawrence Berkeley National Lab↗

DOE OSTI2019

Soft costs and knowledge pathways in the US solar photovoltaic ecosystem

An enduring puzzle in the US solar industry is that despite substantial reductions in the cost of photovoltaic hardware, soft costs (defined here as all costs not directly driven by hardware) have not done the same (US DOE, 2012). In fact, soft costs now account for the majority of the cost of total cost of installed solar systems, despite decades of efforts by firms and policy-makers to reduce these costs. In this study, we address two questions: a) what drives soft costs within the US solar industry, and b) how do firms gain the knowledge required to address these costs? We address this question through an explorative inductive multiple-case study (e.g., Eisenhardt, 1989; Appleyard, 1996). Our sample consists of 15 actors from across various roles within the industry, including residential and commercial installers (8), distribution intermediaries (3), and business software vendors (4). We conducted extensive interviews with executives at each firm (76 interviews in total), and supplemented this information with archival resources such as press releases, news coverage, analyst reports, and internet articles. This data was synthesized into a comprehensive case for each actor, detailing the activities conducted within each firm, the drivers of soft costs within that firm, and knowledge flows (e.g., new technologies, best practices, etc) within and across firms. We then compared across these cases to identify common patterns in cost and knowledge dynamics. Our study contributes a nuanced perspective on the drivers of soft costs within the solar industry as well as the patterns and mechanisms of knowledge flows within the industry. We find for example that the majority of the soft costs occur at the interface between firms (e.g., utilities, suppliers). We also observe substantive variation in costs across different types of actors and even within firm types (e.g., small vs. large installers). We also provide insight into how firms acquire knowledge and when they are able to apply it in order to reduce these costs. Our data reveal heterogeneity in a) the types of knowledge, and b) the pathways of knowledge flow between the actors in the industry. For example, we observe that distributors and suppliers play a key role in the dissemination of new technologies as well as general management knowledge for smaller firms. In contrast, informal personal networks and internal experimentation appear to be more important for larger firms. We also reveal critical distinctions across markets – e.g., differences in organizational culture that facilitate learning and experimentation more readily in residential vs. commercial markets. Overall, these results contribute preliminary insights into the knowledge dynamics of the solar industry, the persistence of soft costs over time, heterogeneity across firm types and regions, and the effective design of policy to support the solar industry.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2019

A Knowledge Graph Approach for the Secondary Use of Cancer Registry Data

Population-based central cancer registries collect valuable structured and unstructured data primarily for cancer surveillance and research, enhancing insights into clinical features associated with cancer occurrence, cancer treatment, and cancer outcomes to guide interventions which reduce the cancer burden. Cancer registries primarily collect data on (1) cancer type (case or tumor); (2) patient demographics such as age, gender, and residential address at time of diagnosis; (3) planned first course of treatment; and (4) date of last contact, vital status, and cause of death. Cancer registry data is dynamic, structured data, which is extracted from many unstructured sources such as electronic healthcare records, and consolidated for reporting and other purposes. While available advanced analytic tools such as SEER*Stat have the ability to build SAS queries, we, however, explore an innovative knowledge graph approach to organizing cancer registry data for advanced analytics and visualization, which has unique advantages over approaches of existing tools. This innovative knowledge graph approach semantically enriches the data and easily enables linkage with third-party data, which can better explain variation in outcomes. We have developed a prototype knowledge graph based on data from the Louisiana Tumor Registry and other publicly available datasets including Behavioral Risk Factor Surveillance System, Clinical Trials, DBpedia, GeoNames, Rural-Urban Continuum Codes, and Semantic MEDLINE. The resource description framework (RDF) data model was selected to represent our knowledge graph, which contains more than 25 billion triples and is ~4TB in storage size. To exhibit the benefits of the knowledge graph approach, we used scenario specific queries, which find the relationships between cancer treatment sequences and outcomes. To illustrate its ease of use in iterative analysis, the knowledge graph was linked to external datasets for performing complex queries across multiple datasets. In addition, we used knowledge graphs to identify data discrepancies and to handle schema changes. Finally, we visualized the knowledge graph to discover data patterns. Our results demonstrate this graph-based solution enables cancer researchers to execute complex queries and more easily perform iterative analyses to improve understanding of cancer registry data. In the future, we would like to use high-performance computing (HPC) resources for faster-generating hypotheses with clinical potential from our knowledge graph.

Hasan, S M Shamimul↗

DOE OSTI2013

Knowledge Framework Implementation with Multiple Architectures - 13090

Multiple kinds of knowledge management systems are operational in public and private enterprises, large and small organizations with a variety of business models that make the design, implementation and operation of integrated knowledge systems very difficult. In recent days, there has been a sweeping advancement in the information technology area, leading to the development of sophisticated frameworks and architectures. These platforms need to be used for the development of integrated knowledge management systems which provides a common platform for sharing knowledge across the enterprise, thereby reducing the operational inefficiencies and delivering cost savings. This paper discusses the knowledge framework and architecture that can be used for the system development and its application to real life need of nuclear industry. A case study of deactivation and decommissioning (D and D) is discussed with the Knowledge Management Information Tool platform and framework. D and D work is a high priority activity across the Department of Energy (DOE) complex. Subject matter specialists (SMS) associated with DOE sites, the Energy Facility Contractors Group (EFCOG) and the D and D community have gained extensive knowledge and experience over the years in the cleanup of the legacy waste from the Manhattan Project. To prevent the D and D knowledge and expertise from being lost over time from the evolving and aging workforce, DOE and the Applied Research Center (ARC) at Florida International University (FIU) proposed to capture and maintain this valuable information in a universally available and easily usable system. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Managing Knowledge Over Time for Supporting Radioactive Waste Geological Disposal Project Development - 19028

Projects designed to define, build, operate and monitor radioactive waste disposal solutions require the transmission of knowledge over several decades and the preservation of memory over several centuries. This is particularly acute for the geological disposal project 'Cigeo' led by the French national radioactive waste management agency (Andra) as far as the requirements in terms of reversibility lead to the choice of an incremental design that allows to integrate learning resulting from continuous improvement of knowledge. For instance, for robustness purposes, Cigeo is designed on the basis of currently proven technologies, but its step-by-step construction in successive stages must make it possible to integrate improvements derived from the scientific and technical progress as well as from experience feedback from its exploitation. In the field of industrial activity, a significant proportion of the knowledge and skills implemented by employees of a company are not known or even identified by the human resources organization or the mid-management staff. This is mainly due to the fact that (i) the organized knowledge-sharing is often based on documentary tools but little on social modalities and (ii) the organization of knowledge is mostly cross-cutting to the managerial lines. To overcome this bias, a global and integrated approach to knowledge management is currently the focus of Andra's specific efforts. Cross-disciplinary groups and close intergenerational links positioned throughout the life of the projects aim specifically at continuous and participative knowledge sharing, capitalization and transmission. Then, devices aiming at systematic dissemination of and access to knowledge support experience feedback and provide unified collaboration, traceability and decision-support at all scales. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2019

Unsupervised word embeddings capture latent knowledge from materials science literature

The overwhelming majority of scientific knowledge is published as text, which is difficult to analyse by either traditional statistical analysis or modern machine learning methods. By contrast, the main source of machine-interpretable data for the materials research community has come from structured property databases, which encompass only a small fraction of the knowledge present in the research literature. Beyond property values, publications contain valuable knowledge regarding the connections and relationships between data items as interpreted by the authors. To improve the identification and use of this knowledge, several studies have focused on the retrieval of information from scientific literature using supervised natural language processing, which requires large hand-labelled datasets for training. Here we show that materials science knowledge present in the published literature can be efficiently encoded as information-dense word embeddings (vector representations of words) without human labelling or supervision. Without any explicit insertion of chemical knowledge, these embeddings capture complex materials science concepts such as the underlying structure of the periodic table and structure-property relationships in materials. Furthermore, we demonstrate that an unsupervised method can recommend materials for functional applications several years before their discovery. This suggests that latent knowledge regarding future discoveries is to a large extent embedded in past publications. Lastly, our findings highlight the possibility of extracting knowledge and relationships from the massive body of scientific literature in a collective manner, and point towards a generalized approach to the mining of scientific literature.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Knowledge brokerage - potential for increased capacities and shared power in impact assessment

Constructive and collaborative planning theory has exposed the perceived limitations of public participation in impact assessment. At strategic levels of assessment the established norm can be misleading and practice is illusive. For example, debates on SEA effectiveness recognize insufficiencies, but are often based on questionable premises. The authors of this paper argue that public participation in strategic assessment requires new forms of information and engagement, consistent with the complexity of the issues at these levels and that strategic assessments can act as knowledge brokerage instruments with the potential to generate more participative environments and attitudes. The paper explores barriers and limitations, as well as the role of knowledge brokerage in stimulating the engagement of the public, through learning-oriented processes and responsibility sharing in more participative models of governance. The paper concludes with a discussion on building and inter-change of knowledge, towards creative solutions to identified problems, stimulating learning processes, largely beyond simple information transfer mechanisms through consultative processes. The paper argues fundamentally for the need to conceive strategic assessments as learning platforms and design knowledge brokerage opportunities explicitly as a means to enhance learning processes and power sharing in IA. - Highlights: Black-Right-Pointing-Pointer Debates on SEA recognize insufficiencies on public participation Black-Right-Pointing-Pointer We propose new forms of engagement consistent with complex situations at strategic levels of decision-making Black-Right-Pointing-Pointer Constructive and collaborative planning theories help explain how different actors acquire knowledge and the value of knowledge exchange Black-Right-Pointing-Pointer Strategic assessments can act as knowledge brokerage instruments Black-Right-Pointing-Pointer The paper argues for strategic assessments as learning platforms as a means to enhance learning processes and power sharing in IA.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Knowledge-Based Hazardous Waste Determinations for Solar Photovoltaic (PV) Modules: A Foundational Study

This report explores using "generator knowledge" to determine whether a solar photovoltaic (PV) module must be managed as hazardous waste prior to recycling or landfilling. Generator knowledge is a legal term and existing regulatory pathway for making a hazardous waste determination that has been used by other industries but is a relatively unknown option to the PV industry. In the United States, a hazardous waste determination often acts as a pre-requisite to recycle or landfill a PV module. The results of the hazardous waste determination dictate whether the PV module must be managed as hazardous waste or nonhazardous solid waste. Managing a PV module as hazardous waste requires compliance with stringent U.S. federal and state hazardous waste law. In addition to increased management costs, which can be ten times higher, legal liability for PV modules regulated as hazardous waste is also heightened with both civil and criminal penalties for noncompliance which includes making an inaccurate or faulty hazardous waste determination. The most common reason a PV module would be regulated as hazardous is if it contains a regulated metal in an amount that equals or exceeds the toxicity characteristic limits. To determine whether a PV module exhibits a hazardous characteristic, the regulated person/entity must "apply knowledge...in light of the materials and processes used." In the absence of adequate knowledge to determine whether the PV module is hazardous, it must be tested using Test Method 1311 Toxicity Characteristic Leaching Procedure (TCLP) or an equivalent EPA-approved method. Although TCLP is the predominant method used to today to make a hazardous waste determination for PV modules, evidence from this study concludes it is not practical to TCLP test every PV module even in a single utility-scale installation, and a scalable solution is needed. This study finds that knowledge-based hazardous waste determinations may allow a regulated person/entity to make a hazardous waste determination for more than one PV module at one time - making this regulatory pathway a potential scalable solution. Through legal analysis and interviews with 44 experts, the authors explore what it means to make an accurate knowledge-based hazardous determination for PV modules considering sources and forms of information as well as potential limitations. The work aims to provide a foundation for building consensus on whether knowledge-based hazardous determinations are a viable, scalable industry approach for solar.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

System Engineers and Decisions: It?s All about Knowledge

In order to guarantee that a system meets adequate levels of reliability and availability, system performances are continuously monitored and analyzed thanks to the technological advancements driving the Industry 4.0 revolution. An Industry 4.0 approach is typically based on advanced statistical, big data mining, machine learning, and internet-of-things methods designed to detect anomalies in the behavior of system, detect the most likely failure modes, and provide indications to system engineers on when maintenance activities should be performed before system performance are deemed unacceptable (which can be generated by diagnostic and prognostic methods). However, these analyses, which are designed to automatize and increase the efficacy of the system maintenance program, require large amount of data which can come in various forms: numeric, textual, images, sounds etc. Such data constitutes the historic knowledge benchmark to track system performances and support system engineer decisions. Here we claim that data is not sufficient to support this kind of analyses when applied to systems characterized by complex architectures and behaviors. Robust system engineer decisions require the ability to understand the system operational context that lies behind the observed data elements. In this respect, system models are in fact necessary to “put data in context” and capture relationships between data elements. Industry 4.0 methods require in fact contextual knowledge as a basis upon which hypotheses can be generated and assumptions tested. In our view, for complex systems, model-based system engineering (MBSE) models can afford this contextual knowledge, as they are typically used to describe systems architecture and dynamic behaviors. System knowledge is here intended as the blending of collected data and system architecture which takes the form of a “knowledge graph”. A knowledge graph is a database which consists of a large set of nodes (in our case an entity can be either a data or an MBSE element) which are linked to each other. The types of nodes and links follow a pre-defined topology, sometimes also refers as an ontology, that is designed to fit the actual decisions that needs to be performed. We show here how a knowledge graph can be defined to support system engineer maintenance decisions and how the same graph can be built based on system MBSE models and pre-processed data from numeric (through anomaly detections and diagnostic methods) and textual elements (through technical language processing TLP).

97 - MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Knowledge Transfer Program for International Nuclear Safeguards

Nuclear safeguards were first announced in 1945 by the U.S., Canadian and British governments as a means to exchange scientific information about peaceful uses of atomic energy and prevent the use of nuclear material for weapons. International nuclear safeguards, now under the provision of the International Atomic Energy Agency (IAEA), serves to hold accountable the 140 member countries (States) that have entered into treaties and agreements against the spread of nuclear weapons. Most notably, the IAEA acts as a nuclear materials inspectorate under the global Nuclear Non-Proliferation Treaty, brought about in 1968, that specifies commitments member States make to the world?s non-proliferation regime. The Office of International Nuclear Safeguards (OINS) is part of the National Nuclear Security Administration, a semi-autonomous agency within the U.S. Department of Energy. Within OINS, the Human Capital Development program recognizes the need to build workforce capacity and safeguards expertise. This includes supporting the education and training of younger generations working in international nuclear safeguards. One of the program?s main concerns is core competencies being lost to retirement or attrition and building knowledge retention pipelines from senior to new professionals. One identified core competency is knowledge gained by individuals who have completed IAEA assignments such as missions to member States to conduct safeguards inspections. Such individuals possess unique knowledge, and efforts are being put forth to effectively capture this knowledge within the national laboratories complex. To this end, we report findings from a one-year mentor-mentee knowledge retention program in which an international safeguards subject matter expert imparted knowledge and skills to a willing professional mentee. The knowledge and skills stem from the mentor?s multi-year assignment at the IAEA in Vienna, Austria.

98 - NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL↗

DOE OSTI2013

'Big Data' Collaboration: Exploring, Recording and Sharing Enterprise Knowledge

As data sources and data size proliferate, knowledge discovery from "Big Data" is starting to pose several challenges. In this paper, we address a specific challenge in the practice of enterprise knowledge management while extracting actionable nuggets from diverse data sources of seemingly-related information. In particular, we address the challenge of archiving knowledge gained through collaboration, dissemination and visualization as part of the data analysis, inference and decision-making lifecycle. We motivate the implementation of an enterprise data-discovery and knowledge recorder tool, called SEEKER based on real world case-study. We demonstrate SEEKER capturing schema and data-element relationships, tracking the data elements of value based on the queries and the analytical artifacts that are being created by analysts as they use the data. We show how the tool serves as digital record of institutional domain knowledge and a documentation for the evolution of data elements, queries and schemas over time. As a knowledge management service, a tool like SEEKER saves enterprise resources and time by avoiding analytic silos, expediting the process of multi-source data integration and intelligently documenting discoveries from fellow analysts.

Big Data↗

DOE OSTI2012

D and D Knowledge Management Information Tool - 2012 - 12106

Deactivation and decommissioning (D and D) work is a high priority activity across the Department of Energy (DOE) complex. Subject matter specialists (SMS) associated with the different ALARA (As-Low-As-Reasonably-Achievable) Centers, DOE sites, Energy Facility Contractors Group (EFCOG) and the D and D community have gained extensive knowledge and experience over the years in the cleanup of the legacy waste from the Manhattan Project. To prevent the D and D knowledge and expertise from being lost over time from the evolving and aging workforce, DOE and the Applied Research Center (ARC) at Florida International University (FIU) proposed to capture and maintain this valuable information in a universally available and easily usable system. D and D KM-IT provides single point access to all D and D related activities through its knowledge base. It is a community driven system. D and D KM-IT makes D and D knowledge available to the people who need it at the time they need it and in a readily usable format. It uses the World Wide Web as the primary source for content in addition to information collected from subject matter specialists and the D and D community. It brings information in real time through web based custom search processes and its dynamic knowledge repository. Future developments include developing a document library, providing D and D information access on mobile devices for the Technology module and Hotline, and coordinating multiple subject matter specialists to support the Hotline. The goal is to deploy a high-end sophisticated and secured system to serve as a single large knowledge base for all the D and D activities. The system consolidates a large amount of information available on the web and presents it to users in the simplest way possible. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2013

Surgeons' Knowledge and Practices Regarding the Role of Radiation Therapy in Breast Cancer Management

Purpose: Population-based studies suggest underuse of radiation therapy, especially after mastectomy. Because radiation oncology is a referral-based specialty, knowledge and attitudes of upstream providers, specifically surgeons, may influence patients' decisions regarding radiation, including whether it is even considered. Therefore, we sought to evaluate surgeons' knowledge of pertinent risk information, their patterns of referral, and the correlates of surgeon knowledge and referral in specific breast cancer scenarios. Methods and Materials: We surveyed a national sample of 750 surgeons, with a 67% response rate. We analyzed responses from those who had seen at least 1 breast cancer patient in the past year (n=403), using logistic regression models to identify correlates of knowledge and appropriate referral. Results: Overall, 87% of respondents were general surgeons, and 64% saw >10 breast cancer patients in the previous year. In a scenario involving a 45-year-old undergoing lumpectomy, only 45% correctly estimated the risk of locoregional recurrence without radiation therapy, but 97% would refer to radiation oncology. In a patient with 2 of 20 nodes involved after mastectomy, 30% would neither refer to radiation oncology nor provide accurate information to make radiation decisions. In a patient with 4 of 20 nodes involved after mastectomy, 9% would not refer to radiation oncology. Fewer than half knew that the Oxford meta-analysis revealed a survival benefit from radiation therapy after lumpectomy (45%) or mastectomy (32%). Only 16% passed a 7-item knowledge test; female and more-experienced surgeons were more likely to pass. Factors significantly associated with appropriate referral to radiation oncology included breast cancer volume, tumor board participation, and knowledge. Conclusions: Many surgeons have inadequate knowledge regarding the role of radiation in breast cancer management, especially after mastectomy. Targeted educational interventions may improve the quality of care.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2024

Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES): a method for populating knowledge bases using zero-shot learning

Abstract Motivation Creating knowledge bases and ontologies is a time consuming task that relies on manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrarily complex nested knowledge schemas. Results Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against an LLM to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for matched elements. We present examples of applying SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease relationships. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction methods, but greatly surpasses an LLM’s native capability of grounding entities with unique identifiers. SPIRES has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any new training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. Availability and implementation SPIRES is available as part of the open source OntoGPT package: https://github.com/monarch-initiative/ontogpt.

59 BASIC BIOLOGICAL SCIENCES↗