Abstract 19161: EchoMap Automatically Maps Echocardiogram Report Text to Ontology
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Background: Big data has the potential to revolutionize echocardiography by enabling novel research and rigorous, scalable quality improvement. Text reports are a key part of such analyses. Currently, echocardiogram reports include both structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both non-deep learning and deep-learning (e.g., large language model, or LLM) based techniques. Challenges to date in using echo text with LLMs include small size, domain-specific language, and high need for accuracy and clinical meaning in model results. Hypotheses: We tested whether we could map echocardiography text to a structured ontology using NLP. Methods: We developed a three-tier ontology for the echocardiographic anatomic structures, functional elements, and descriptive characteristics in an adult transthoracic echocardiogram using 919 sentences from UCSF’s structured echocardiogram report text. We tested LLM fine-tuning as well as non-LLM techniques to map echocardiography sentences to this ontology. Two-hundred twenty-eight UCSF sentences served as an internal test set. Additional test datasets included free text from UCSF reports; structured text sentences from two other hospitals; and sentences from reports representing 17 additional hospitals. Results: Despite all adhering to clinical guidelines for reporting, there were notable differences by institution in what structural and functional information was included in structured reporting. A non-LLM hierarchical model performed best in mapping sentences to the ontology, with internal test accuracy of 96% for the first level of the ontology, 91% for the second level, and 77% for the third level. Echomap retained good performance across diverse datasets and displayed the ability to extrapolate to ontological terms not initially included in training. Conclusions: We show that non-LLM NLP methods can achieve good performance and may be especially useful for small, specialized text datasets where clinical meaning is important. These results highlight the utility of a high-resolution, standardized cardiac ontology to harmonize reports across institutions.To support context-aware mobile recommendation, an ontology-based context modeling approach was proposed. We analyzed the framework of the mobile recommender system based on contextual model and suggested designing the model with two-layer structure including an upper ontology layer and a domain ontology layer. The ontologies provides formalizations representing the main entities, including users, objects, contexts, and their interactive relationships in mobile recommendation environments. A specific context ontology model for catering recommendation was developed and a use case of the instantiated context ontology was demonstrated.
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To support the personalized mobile recommendation service, a context modeling approach based on ontology was proposed. The role of context ontologies in mobile recommenders was discussed, and under the application background of petrol station recommendation, a two-level context ontology model was designed. The model contains an upper ontology to describe general concepts, and a domain ontology extended from the upper ontology. The context ontology provides formalizations representing the mainly entities including users, objects and contexts, and their interactive relationships in mobile recommendation environments. Finally, a use case of the instantiated context ontology was demonstrated.
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Based on the study of the technology of grid and ontology,we imported the new Ontology technology into manufacturing domain,brought out the essential for the importing of Ontology into manufacturing data gird and classified the Ontology into domain Ontology and application ontology.The research will help to the further development of domestic manufacturing industry.
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There are, meta-theoretically speaking, two main ways to investigate socio-economic phenomena in general and the current economic crisis in particular, each one rooted in its own ontology. The first, which I refer to as `scientistic-oriented economics ´ is rooted in an ontology of atomistic, observable events and event regularities. The second, which I refer to as `political-economics´, can be rooted in an ontology which I will abbreviate to structures and mechanisms that are reproduced and transformed by human agents. This ontology has been advocated by critical realists (CRs). The version of political economy I advocate, therefore, is a critical realist-oriented political economy (CR).
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3 Abstract An ontology is a computer-processable collection of knowledge about the world. This thesis explains how an ontology can be constructed and expanded automatically. The proposed approach consists of three contributions:An ontology is a computer-processable collection of knowledge about the world. This thesis explains how an ontology can be constructed and expanded automatically. The proposed approach consists of three contributions: 1. A core ontology, YAGO. YAGO is an ontology that has been constructed automatically. It combines high accuracy with large coverage and serves as a core that can be expanded. 2. A tool for information extraction, LEILA. LEILA is a system that can extract knowledge from natural language texts. LEILA will be used to find new facts for YAGO. 3. An integration mechanism, SOFIE. SOFIE is a system that can reason on the plausibility of new knowledge. SOFIE will assess the facts found by LEILA and integrate them into YAGO. Each of these components comes with a fully implemented system. Together, they form an integrative architecture, which does not only gather new facts, but also reconcile them with the existing facts. The result is an ever-growing, yet highly accurate ontological knowledge base. A survey of applications of the ontology completes the thesis.
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This paper establishes a smart car ontology using the hierarchical context ontology modeling method.To address the context and the context reasoning ontology model of information sharing between the issues,the paper proposes a concept-based ontology mapping method.This paper implements a smart car space drivers identify the state of the prototype system,experimental results show that this ontology model and reasoning methods can more effectively identify the driver of the different states.
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Ontology construction aims to build conceptual knowledge in such a way that the relations among major concepts can be explicitly identified and presented in a machine operable way so as to assist in intelligent processing of computer applications. An upper-level ontology includes general concepts that are used broadly across different domains whereas ontologies acquired by computing through algorithms automatically are more likely to be domain specific. This paper first introduces domain specific core ontology ( mid-level ontology) and application domain ontology ( lower-level ontology) . Then,it presents a top-down approach to build a core ontology for Chinese in the IT domain based on the English upper level ontology SUMO and other English-Chinese resources available. The paper also introduces a bottom-up approach to build domain specific ontology using corpus based approach.
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Ontology population is an instantiation of the ontology classes and subclasses. Ontology population is the main step in ontology construction. However, the manual population is a time-consuming task. Accordingly, automatic or semi-automatic methods to populate an ontology are required. This paper suggests an approach for the creation of an ontology and its population. The studied ontology is related to named entities in the holy Quran. The major contribution of this approach is to harness the benefits of learning methods, conjoined with statistical models to extract contexts (words surrounding a named entity) from Quran and Hadith and retain the weighty contexts for the recognition of supplementary named entities to populate the ontology.
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Open Biomedical Ontologies
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Web Ontology Language
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