Cosine Similarity Metric Research Articles

Pre-trained models for linking process in data washing machine

Entity Resolution (ER) has been investigated for decades in various domains as a fundamental task in data integration and data quality. The emerging volume of heterogeneously structured data and even unstructured data challenges traditional ER methods. This research mainly focuses on the Data Washing Machine (DWM). The DWM was developed in the NSF DART Data Life Cycle and Curation research theme, which helps to detect and correct certain types of data quality errors automatically. It also performs unsupervised entity resolution to identify duplicate records. However, it uses traditional methods that are driven by algorithmic pattern rules such as Levenshtein Edit Distances and Matrix comparators. The goal of this research is to assess the replacement of rule-based methods with machine learning and deep learning methods to improve the effectiveness of the processes using 18 sample datasets. The DWM has different processes to improve data quality, and we are currently focusing on working with the scoring and linking processes. To integrate the machine model into the DWM, different pre-trained models were tested to find the one that helps to produce accurate vectors that can be used to calculate the similarity between the records. After trying different pre-trained models, distilroberta was chosen to get the embeddings, and cosine similarity metrics were later used to get the similarity scores, which helped us assess the machine learning model into DWM and gave us closer results to what the scoring matrix is giving. The model performed well and gave closer results overall, and the reason can be that it helped to pick up the important features and helped at the entity matching process.

Forming Dataset of The Undergraduate Thesis using Simple Clustering Methods

Each university collects many undergraduate theses data but has yet to process it to make it easier forstudents to find references as desired. This study aims to classify and compare the grouping ofdocuments using expert and simple clustering methods. Experts have done ground truth using ORBoolean Retrieval and keyword generation. The best cluster was discovered by the experiments usingthe K-Means, K-Medoids, and DBSCAN clustering methods and using Euclidean, Manhattan, CityBlock, and Cosine Similarity metrics. The cluster with the best Silhouette Score compared to theaccuracy of the categorization of each document. The K-Means clustering method and the CosineSimilarity metric gave the best results with a Silhouette Score value of 0.105534. The comparisonbetween ground truth and the best cluster results shows an accuracy of 33.42%. The result shows thatthe simple clustering method cannot handle data with Negative Skewness and Leptokurtic Kurtosis.

Quantum Approach for Contextual Search, Retrieval, and Ranking of Classical Information.

Quantum-inspired algorithms represent an important direction in modern software information technologies that use heuristic methods and approaches of quantum science. This work presents a quantum approach for document search, retrieval, and ranking based on the Bell-like test, which is well-known in quantum physics. We propose quantum probability theory in the hyperspace analog to language (HAL) framework exploiting a Hilbert space for word and document vector specification. The quantum approach allows for accounting for specific user preferences in different contexts. To verify the algorithm proposed, we use a dataset of synthetic advertising text documents from travel agencies generated by the OpenAI GPT-4 model. We show that the "entanglement" in two-word document search and retrieval can be recognized as the frequent occurrence of two words in incompatible query contexts. We have found that the user preferences and word ordering in the query play a significant role in relatively small sizes of the HAL window. The comparison with the cosine similarity metrics demonstrates the key advantages of our approach based on the user-enforced contextual and semantic relationships between words and not just their superficial occurrence in texts. Our approach to retrieving and ranking documents allows for the creation of new information search engines that require no resource-intensive deep machine learning algorithms.

Evaluation-Focused Multidimensional Score for Turkish Abstractive Text Summarization

Despite the inherent complexity of Abstractive Text Summarization, which is widely acknowledged as one of the most challenging tasks in the field of natural language processing, transformer-based models have emerged as an effective solution capable of delivering highly accurate and coherent summaries. In this study, the effectiveness of transformer-based text summarization models for Turkish language is investigated. For this purpose, we utilize BERTurk, mT5 and mBART as transformer-based encoder-decoder models. Each of the models was trained separately with MLSUM, TR-News, WikiLingua and Fırat_DS datasets. While obtaining experimental results, various optimizations were made in the summary functions of the models. Our study makes an important contribution to the limited Turkish text summarization literature by comparing the performance of different language models on existing Turkish datasets. We first evaluate ROUGE, BERTScore, FastText-based Cosine Similarity and Novelty Rate metrics separately for each model and dataset, then normalize and combine the scores we obtain to obtain a multidimensional score. We validate our innovative approach by comparing the summaries produced with the human evaluation results.

Development of a Children’s Educational Dictionary for a Low-Resource Language Using AI Tools

Today, various interactive tools or partially available artificial intelligence applications are actively used in educational processes to solve multiple problems for resource-rich languages, such as English, Spanish, French, etc. Unfortunately, the situation is different and more complex for low-resource languages, like Kazakh, Uzbek, Mongolian, and others, due to the lack of qualitative and accessible resources, morphological complexity, and the semantics of agglutinative languages. This article presents research on early childhood learning resources for the low-resource Kazakh language. Generally, a dictionary for children differs from classical educational dictionaries. The difference between dictionaries for children and adults lies in their purpose and methods of presenting information. A themed dictionary will make learning and remembering new words easier for children because they will be presented in a specific context. This article discusses developing an approach to creating a thematic children’s dictionary of the low-resource Kazakh language using artificial intelligence. The proposed approach is based on several important stages: the initial formation of a list of English words with the use of ChatGPT; identification of their semantic weights; generation of phrases and sentences with the use of the list of semantically related words; translation of obtained phrases and sentences from English to Kazakh, dividing them into bigrams and trigrams; and processing with Kazakh language POS pattern tag templates to adapt them for children. When the dictionary was formed, the semantic proximity of words and phrases to the given theme and age restrictions for children were taken into account. The formed dictionary phrases were evaluated using the cosine similarity, Euclidean similarity, and Manhattan distance metrics. Moreover, the dictionary was extended with video and audio data by implementing models like DALL-E 3, Midjourney, and Stable Diffusion to illustrate the dictionary data and TTS (Text to Speech) technology for the Kazakh language for voice synthesis. The developed thematic dictionary approach was tested, and a SUS (System Usability Scale) assessment of the application was conducted. The experimental results demonstrate the proposed approach’s high efficiency and its potential for wide use in educational purposes.

Atlas-Based Labeling of Resting-State fMRI.

Background: Functional magnetic resonance imaging (fMRI) has the potential to provide noninvasive functional mapping of the brain with high spatial and temporal resolution. However, fMRI independent components (ICs) must be manually inspected, selected, and interpreted, requiring time and expertise. We propose a novel approach for automated labeling of fMRI ICs by establishing their characteristic spatio-functional relationship. Methods: The approach identifies 9 resting-state networks and 45 ICs and generates a functional activation feature map that quantifies the spatial distribution, relative to an anatomical labeled atlas, of the z-scores of each IC across a cohort of 176 subjects. The cosine-similarity metric was used to classify unlabeled ICs based on the similarity to the spatial distribution of activation with the pregenerated feature map. The approach was tested on three fMRI datasets from the 1000 functional connectome projects, consisting of 280 subjects, that were not included in feature map generation. Results: The results demonstrate the effectiveness of the approach in classifying ICs based on their spatial features with an accuracy of better than 95%. Conclusions: The approach significantly reduces expert time and computation time required for labeling ICs, while improving reliability and accuracy. The spatio-functional relationship also provides an explainable relationship between the functional activation and the anatomically defined regions.

Evaluating the performance of multilingual models in answer extraction and question generation

Multiple-choice test generation is one of the most complex NLP problems, especially in languages other than English, where there is a lack of prior research. After a review of the literature, it has been verified that some methods like the usage of rule-based systems or primitive neural networks have led to the application of a recent architecture, the Transformer architecture, in the tasks of Answer Extraction (AE) and Question Generation (QG). Thereby, this study is centred in searching and developing better models for the AE and QG tasks in Spanish, using an answer-aware methodology. For this purpose, three multilingual models (mT5-base, mT0-base and BLOOMZ-560 M) have been fine-tuned using three different datasets: a translation to Spanish of the SQuAD dataset; SQAC, which is a dataset in Spanish; and their union (SQuAD + SQAC), which shows slightly better results. Regarding the models, the performance of mT5-base has been compared with that found in two newer models, mT0-base and BLOOMZ-560 M. These models were fine-tuned for multiple tasks in literature, including AE and QG, but, in general, the best results are obtained from the mT5 models trained in our study with the SQuAD + SQAC dataset. Nonetheless, some other good results are obtained from mT5 models trained only with the SQAC dataset. For their evaluation, the widely used BLEU1-4, METEOR and ROUGE-L metrics have been obtained, where mT5 outperforms some similar research works. Besides, CIDEr, SARI, GLEU, WER and the cosine similarity metrics have been calculated to present a benchmark within the AE and QG problems for future work.

Enhanced content-based fashion recommendation system through deep ensemble classifier with transfer learning

With the rise of online shopping due to the COVID-19 pandemic, Recommender Systems have become increasingly important in providing personalized product recommendations. Recommender Systems face the challenge of efficiently extracting relevant items from vast data. Numerous methods using deep learning approaches have been developed to classify fashion images. However, those models are based on a single model that may or may not be reliable. We proposed a deep ensemble classifier that takes the probabilities obtained from five pre-trained models such as MobileNet, DenseNet, Xception, and the two varieties of VGG. The probabilities obtained from the five pre-trained models are then passed as inputs to a deep ensemble classifier for the prediction of the given item. Several similarity measures have been studied in this work and the cosine similarity metric is used to recommend the products for a classified product given by a deep ensemble classifier. The proposed method is trained and validated using benchmark datasets such as Fashion product images dataset and Shoe dataset, demonstrating superior accuracy compared to existing models. The results highlight the potential of leveraging transfer learning and deep ensemble techniques to enhance fashion recommendation systems. The proposed model achieves 96% accuracy compared to the existing models.

Unsupervised Content Mining in CBIR: Harnessing Latent Diffusion for Complex Text-Based Query Interpretation.

The paper demonstrates a novel methodology for Content-Based Image Retrieval (CBIR), which shifts the focus from conventional domain-specific image queries to more complex text-based query processing. Latent diffusion models are employed to interpret complex textual prompts and address the requirements of effectively interpreting the complex textual query. Latent Diffusion models successfully transform complex textual queries into visually engaging representations, establishing a seamless connection between textual descriptions and visual content. Custom triplet network design is at the heart of our retrieval method. When trained well, a triplet network will represent the generated query image and the different images in the database. The cosine similarity metric is used to assess the similarity between the feature representations in order to find and retrieve the relevant images. Our experiments results show that latent diffusion models can successfully bridge the gap between complex textual prompts for image retrieval without relying on labels or metadata that are attached to database images. This advancement sets the stage for future explorations in image retrieval, leveraging the generative AI capabilities to cater to the ever-evolving demands of big data and complex query interpretations.

Using Probe Counts to Provide High-Resolution Detector Data for a Microscopic Traffic Simulation

Microscopic traffic simulations have become increasingly important for research targeting connected vehicles. They are especially appreciated for enabling investigations targeting large areas, which would be practically impossible or too expensive in the real world. However, such large-scale simulation scenarios often lack validation with real-world measurements since these data are often not available. To overcome this issue, this work integrates probe counts from floating car data as reference counts to model a large-scale microscopic traffic scenario with high-resolution detector data. To integrate the frequent probe counts, a road network matching is required. Thus, a novel road network matching method based on a decision tree classifier is proposed. The classifier automatically adjusts its cosine similarity and Hausdorff distance-based similarity metrics to match the network’s requirements. The approach performs well with an F1-score of 95.6%. However, post-processing steps are required to produce a sufficiently consistent detector dataset for the subsequent traffic simulation. The finally modeled traffic shows a good agreement of 95.1%. with upscaled probe counts and no unrealistic traffic jams, teleports, or collisions in the simulation. We conclude that probe counts can lead to consistent traffic simulations and, especially with increasing and consistent penetration rates in the future, help to accurately model large-scale microscopic traffic simulations.

A Natural Language Processing-Based Classification and Mode-Based Ranking of Musculoskeletal Disorder Risk Factors

This research explores the intricate landscape of Musculoskeletal Disorder (MSD) risk factors, employing a novel fusion of Natural Language Processing (NLP) techniques and mode-based ranking methodologies. Enhancing knowledge of MSD risk factors, their classification, and their relative severity is the main goal of enabling more focused preventative and treatment efforts. The study benchmarks eight NLP models, integrating pre-trained transformers, cosine similarity, and various distance metrics to categorize risk factors into personal, biomechanical, workplace, psychological, and organizational classes. Key findings reveal that the Bidirectional Encoder Representations from Transformers (BERT) model with cosine similarity attains an overall accuracy of 28%, while the sentence transformer, coupled with Euclidean, Bray–Curtis, and Minkowski distances, achieves a flawless accuracy score of 100%. Using a 10-fold cross-validation strategy and performing rigorous statistical paired t-tests and Cohen’s d tests (with a 5% significance level assumed), the study provides the results with greater validity. To determine the severity hierarchy of MSD risk variables, the research uses survey data and a mode-based ranking technique parallel to the classification efforts. Intriguingly, the rankings align precisely with the previous literature, reaffirming the consistency and reliability of the approach. “Working posture” emerges as the most severe risk factor, emphasizing the critical role of proper posture in preventing MSD. The collective perceptions of survey participants underscore the significance of factors like “Job insecurity”, “Effort reward imbalance”, and “Poor employee facility” in contributing to MSD risks. The convergence of rankings provides actionable insights for organizations aiming to reduce the prevalence of MSD. The study concludes with implications for targeted interventions, recommendations for improving workplace conditions, and avenues for future research. This holistic approach, integrating NLP and mode-based ranking, contributes to a more sophisticated comprehension of MSD risk factors and opens the door for more effective strategies in occupational health.

Optimization of traditional methods for determining the similarity of project names and purchases using large language models

The subject of the study is the analysis and improvement of methods for determining the relevance of project names to the information content of purchases using large language models. The object of the study is a database containing the names of projects and purchases in the field of electric power industry, collected from open sources. The author examines in detail such aspects of the topic as the use of TF-IDF and cosine similarity metrics for primary data filtering, and also describes in detail the integration and evaluation of the effectiveness of large language models such as GigaChat, GPT-3.5, and GPT-4 in text data matching tasks. Special attention is paid to the methods of clarifying the similarity of names based on reflection introduced into the prompta of large language models, which makes it possible to increase the accuracy of data comparison. The study uses TF-IDF and cosine similarity methods for primary data analysis, as well as large GigaChat, GPT-3.5 and GPT-4 language models for detailed verification of the relevance of project names and purchases, including reflection in model prompta to improve the accuracy of results. The novelty of the research lies in the development of a combined approach to determining the relevance of project names and purchases, combining traditional methods of processing text information (TF-IDF, cosine similarity) with the capabilities of large language models. A special contribution of the author to the research of the topic is the proposed methodology for improving the accuracy of data comparison by clarifying the results of primary selection using GPT-3.5 and GPT-4 models with optimized prompta, including reflection. The main conclusions of the study are confirmation of the prospects of using the developed approach in the tasks of information support for procurement processes and project implementation, as well as the possibility of using the results obtained for the development of text data mining systems in various sectors of the economy. The study showed that the use of language models makes it possible to improve the value of the F2 measure to 0.65, which indicates a significant improvement in the quality of data comparison compared with basic methods.

Automatic generation of conclusions from neuroradiology MRI reports through natural language processing.

The conclusion section of a radiology report is crucial for summarizing the primary radiological findings in natural language and essential for communicating results to clinicians. However, creating these summaries is time-consuming, repetitive, and prone to variability and errors among different radiologists. To address these issues, we evaluated a fine-tuned Text-To-Text Transfer Transformer (T5) model for abstractive summarization to automatically generate conclusions for neuroradiology MRI reports in a low-resource language. We retrospectively applied our method to a dataset of 232,425 neuroradiology MRI reports in Spanish. We compared various pre-trained T5 models, including multilingual T5 and those newly adapted for Spanish. For precise evaluation, we employed BLEU, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics alongside expert radiologist assessments. The findings are promising, with the models specifically fine-tuned for neuroradiology MRI achieving scores of 0.46, 0.28, 0.52, 2.45, and 0.87 in the BLEU-1, METEOR, ROUGE-L, CIDEr, and cosine similarity metrics, respectively. In the radiological experts' evaluation, they found that in 75% of the cases evaluated, the conclusions generated by the system were as good as or even better than the manually generated conclusions. The methods demonstrate the potential and effectiveness of customizing state-of-the-art pre-trained models for neuroradiology, yielding automatic MRI report conclusions that nearly match expert quality. Furthermore, these results underscore the importance of designing and pre-training a dedicated language model for radiology report summarization.

SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis.

Data-driven methods have shown tremendous progress in medical image analysis. In this context, deep learning-based supervised methods are widely popular. However, they require a large amount of training data and face issues in generalisability to unseen datasets that hinder clinical translation. Endoscopic imaging data is characterised by large inter- and intra-patient variability that makes these models more challenging to learn representative features for downstream tasks. Thus, despite the publicly available datasets and datasets that can be generated within hospitals, most supervised models still underperform. While self-supervised learning has addressed this problem to some extent in natural scene data, there is a considerable performance gap in the medical image domain. In this paper, we propose to explore patch-level instance-group discrimination and penalisation of inter-class variation using additive angular margin within the cosine similarity metrics. Our novel approach enables models to learn to cluster similar representations, thereby improving their ability to provide better separation between different classes. Our results demonstrate significant improvement on all metrics over the state-of-the-art (SOTA) methods on the test set from the same and diverse datasets. We evaluated our approach for classification, detection, and segmentation. SSL-CPCD attains notable Top 1 accuracy of 79.77% in ulcerative colitis classification, an 88.62% mean average precision (mAP) for detection, and an 82.32% dice similarity coefficient for segmentation tasks. These represent improvements of over 4%, 2%, and 3%, respectively, compared to the baseline architectures. We demonstrate that our method generalises better than all SOTA methods to unseen datasets, reporting over 7% improvement.

Bearing Digital Twin Based on Response Model and Reinforcement Learning

In recent years, research on bearing fault modeling has witnessed significant advancements. However, the modeling of bearing faults using digital twins (DTs) remains an emerging area of exploration. This paper introduces a bearing digital twin developed by integrating a signal-based response model with reinforcement learning techniques. Initially, a signal-based model is constructed, comprising a unit fault impulse function and a decay oscillation function. This model illustrates the bearing’s acceleration response under fault conditions and acts as the environmental component within the bearing digital twin. Subsequently, a parameter estimation process identifies two critical parameters from the signal-based model: the load proportional factor and the decaying constant. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed as the agent for online learning of these parameters. The cosine similarity metric is employed to define the state and reward by comparing the real acceleration measurements with the simulation data generated by the digital twin. To validate the effectiveness of the digital twin, experimental data sourced from the three datasets are utilized. The results underscore the digital twin’s capacity to faithfully replicate the bearing’s acceleration response under diverse conditions, demonstrating a high degree of similarity in both the time and frequency domains.

Cognitive decline assessment using semantic linguistic content and transformer deep learning architecture.

Dementia is a cognitive decline that leads to the progressive deterioration of an individual's ability to perform daily activities independently. As a result, a considerable amount of time and resources are spent on caretaking. Early detection of dementia can significantly reduce the effort and resources needed for caretaking. This research proposes an approach for assessing cognitive decline by analysing speech data, specifically focusing on speech relevance as a crucial indicator for memory recall. This is a cross-sectional, online, self-administered. The proposed method used deep learning architecture based on transformers, with BERT (Bidirectional Encoder Representations from Transformers) and Sentence-Transformer to derive encoded representations of speech transcripts. These representations provide contextually descriptive information that is used to analyse the relevance of sentences in their respective contexts. The encoded information is then compared using cosine similarity metrics to measure the relevance of uttered sequences of sentences. The study uses the Pitt Corpus Dementia dataset for experimentation, which consists of speech data from individuals with and without dementia. The accuracy of the proposed multi-QA-MPNet (Multi-Query Maximum Inner Product Search Pretraining) model is compared with other pretrained transformer models of Sentence-Transformer. The results show that the proposed approach outperforms the other models in capturing context level information, particularly semantic memory. Additionally, the study explores the suitability of different similarity measures to evaluate the relevance of uttered sequences of sentences. The experimentation reveals that cosine similarity is the most appropriate measure for this task. This finding has significant implications for the early warning signs of dementia, as it suggests that cosine similarity metrics can effectively capture the semantic relevance of spoken language. The persistent cognitive decline over time acts as one of the indicators for prevalence of dementia. Additionally early dementia could be recognised by analysis on other modalities like speech and brain images. What is already known on this subject It is already known that speech- and language-based detection methods can be useful for dementia diagnosis, as language difficulties are often early signs of the disease. Additionally, deep learning algorithms have shown promise in detecting and diagnosing dementia through analysing large datasets, particularly in speech- and language-based detection methods. However, further research is needed to validate the performance of these algorithms on larger and more diverse datasets and to address potential biases and limitations. What this paper adds to existing knowledge This study presents a unique and effective approach for cognitive decline assessment through analysing speech data. The study provides valuable insights into the importance of context and semantic memory in accurately detecting the potential in dementia and demonstrates the applicability of deep learning models for this purpose. The findings of this study have important clinical implications and can inform future research and development in the field of dementia detection and care. What are the potential or actual clinical implications of this work? The proposed approach for cognitive decline assessment using speech data and deep learning models has significant clinical implications. It has the potential to improve the accuracy and efficiency of dementia diagnosis, leading to earlier detection and more effective treatments, which can improve patient outcomes and quality of life.

Robust multi-sensor image matching based on normalized self-similarity region descriptor

Multi-modal image matching is crucial in aerospace applications because it can fully exploit the complementary and valuable information contained in the amount and diversity of remote sensing images. However, it remains a challenging task due to significant non-linear radiometric, geometric differences, and noise across different sensors. To improve the performance of heterologous image matching, this paper proposes a normalized self-similarity region descriptor to extract consistent structural information. We first construct the pointwise self-similarity region descriptor based on the Euclidean distance between adjacent image blocks to reflect the structural properties of multi-modal images. Then, a linear normalization approach is used to form Modality Independent Region Descriptor (MIRD), which can effectively distinguish structural features such as points, lines, corners, and flat between multi-modal images. To further improve the matching accuracy, the included angle cosine similarity metric is adopted to exploit the directional vector information of multi-dimensional feature descriptors. The experimental results show that the proposed MIRD has better matching accuracy and robustness for various multi-modal image matching than the state-of-the-art methods. MIRD can effectively extract consistent geometric structure features and suppress the influence of SAR speckle noise using non-local neighboring image blocks operation, effectively applied to various multi-modal image matching.

Tracing the Proliferation of Socialist Realism Doctrine in Latvian Periodicals: Case Study of "Literature and Art" and "The Flag"


 
 
 The paper presents the results of a study on the dissemination of socio-political and aesthetic ideas of Socialist Realism doctrine in the Latvian periodicals Literature and Art (LitArt) and The Flag. Several programmatic, ideologically saturated articles that were published in The Flag in the 1940s were compared to the rest of the corpus to explore the proliferation and persistence of similar ideas in the course of following decades. Authors have employed methodologies commonly used for plagiarism detection: fingerprinting and the comparison of document similarity based on word embeddings and document similarity measures. In particular, three perspectives were used to examine the similarity and reuse of texts: comparison of matching 5-grams processed by the winnowing algorithm and comparison of documents based on the TF-IDF and Doc2Vec embeddings and cosine similarity metrics. To facilitate the analysis, the results were loaded in the open-source version of Neo4j graph database. The findings were further explored and evaluated qualitatively to identify the distribution of direct citations, frequently reused phrases and most similar documents.
 
 

Infrastructure tools to support an effective Radiation Oncology Learning Health System.

Radiation Oncology Learning Health System (RO-LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real-time. This paper describes a novel set of tools to support the development of a RO-LHS and the current challenges they can address. We present a knowledge graph-based approach to map radiotherapy data from clinical databases to an ontology-based data repository using FAIR concepts. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology-based keyword searching, synonym-based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics. The data pipeline and tool were tested with 1660 patient clinical and dosimetry records resulting in 504180 RDF (Resource Description Framework) tuples and visualized data relationships using graph-based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances. The framework and tools described support the development of a RO-LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning programs in radiation oncology.

A Query Expansion Benchmark on Social Media Information Retrieval: Which Methodology Performs Best and Aligns with Semantics?

This paper presents a benchmarking survey on query expansion techniques for social media information retrieval, with a focus on comparing the performance of methods using semantic web technologies. The study evaluated query expansion techniques such as generative AI models and semantic matching algorithms and how they are integrated in a semantic framework. The evaluation was based on cosine similarity metrics, including the Discounted Cumulative Gain (DCG), Ideal Discounted Cumulative Gain (IDCG), and normalized Discounted Cumulative Gain (nDCG), as well as the Mean Average Precision (MAP). Additionally, the paper discusses the use of semantic web technologies as a component in a pipeline for building thematic knowledge graphs from retrieved social media data with extended ontologies integrated for the refugee crisis. The paper begins by introducing the importance of query expansion in information retrieval and the potential benefits of incorporating semantic web technologies. The study then presents the methodologies and outlines the specific procedures for each query expansion technique. The results of the evaluation are presented, as well as the rest semantic framework, and the best-performing technique was identified, which was the curie-001 generative AI model. Finally, the paper summarizes the main findings and suggests future research directions.