Description Of Entities Research Articles

Knowledge Representation Learning Method Based on Semantic Enhancement of External Information

Background: Knowledge representation learning aims at mapping entity and relational data in knowledge graphs to a low-dimensional space in the form of vectors. The existing work has mainly focused on structured information representation of triples or introducing only one additional kind of information, which has large limitations and reduces the representation efficiency. Objective: This study aims to combine entity description information and textual relationship description information with triadic structure information, and then use the linear mapping method to linearly transform the structure vector and text vector to obtain the joint representation vector. Methods: A knowledge representation learning (DRKRL) model that fuses external information for semantic enhancement is proposed, which combines entity descriptions and textual relations with a triadic structure. For entity descriptions, a vector representation is performed using a bi-directional long- and short-term memory network (Bi-LSTM) model and an attention mechanism. For the textual relations, a convolutional neural network is used to vectorially encode the relations between entities, and then an attention mechanism is used to obtain valuable information as complementary information to the triad. Results: Link prediction and triadic group classification experiments were conducted on the FB15K, FB15K-237, WN18, WN18RR, and NELL-995 datasets. Theoretical analysis and experimental results show that the DRKRL model proposed in this paper has higher accuracy and efficiency compared with existing models. Conclusion: Combining entity description information and textual relationship description information with triadic structure information can make the model have better performance and effectively improve the knowledge representation learning ability.

TeKo: Text-Rich Graph Neural Networks With External Knowledge.

Graph neural networks (GNNs) have gained great prevalence in tackling various analytical tasks on graph-structured data (i.e., networks). Typical GNNs and their variants adopt a message-passing principle that obtains network representations by the attribute propagates along network topology, which however ignores the rich textual semantics (e.g., local word-sequence) that exist in numerous real-world networks. Existing methods for text-rich networks integrate textual semantics by mainly using internal information such as topics or phrases/words, which often suffer from an inability to comprehensively mine the textual semantics, limiting the reciprocal guidance between network structure and textual semantics. To address these problems, we present a novel text-rich GNN with external knowledge (TeKo), in order to make full use of both structural and textual information within text-rich networks. Specifically, we first present a flexible heterogeneous semantic network that integrates high-quality entities as well as interactions among documents and entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity descriptions, to gain a deeper insight into textual semantics. Furthermore, we devise a reciprocal convolutional mechanism for the constructed heterogeneous semantic network, enabling network structure and textual semantics to collaboratively enhance each other and learn high-level network representations. Extensive experiments illustrate that TeKo achieves state-of-the-art performance on a variety of text-rich networks as well as a large-scale e-commerce searching dataset.

Empowering Graph Neural Network-Based Computational Drug Repositioning with Large Language Model-Inferred Knowledge Representation.

Computational drug repositioning, through predicting drug-disease associations (DDA), offers significant potential for discovering new drug indications. Current methods incorporate graph neural networks (GNN) on drug-disease heterogeneous networks to predict DDAs, achieving notable performances compared to traditional machine learning and matrix factorization approaches. However, these methods depend heavily on network topology, hampered by incomplete and noisy network data, and overlook the wealth of biomedical knowledge available. Correspondingly, large language models (LLMs) excel in graph search and relational reasoning, which can possibly enhance the integration of comprehensive biomedical knowledge into drug and disease profiles. In this study, we first investigate the contribution of LLM-inferred knowledge representation in drug repositioning and DDA prediction. A zero-shot prompting template was designed for LLM to extract high-quality knowledge descriptions for drug and disease entities, followed by embedding generation from language models to transform the discrete text to continual numerical representation. Then, we proposed LLM-DDA with three different model architectures (LLM-DDANode Feat, LLM-DDADual GNN, LLM-DDAGNN-AE) to investigate the best fusion mode for LLM-based embeddings. Extensive experiments on four DDA benchmarks show that, LLM-DDAGNN-AE achieved the optimal performance compared to 11 baselines with the overall relative improvement in AUPR of 23.22%, F1-Score of 17.20%, and precision of 25.35%. Meanwhile, selected case studies of involving Prednisone and Allergic Rhinitis highlighted the model's capability to identify reliable DDAs and knowledge descriptions, supported by existing literature. This study showcases the utility of LLMs in drug repositioning with its generality and applicability in other biomedical relation prediction tasks.

Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement

The design and development of text-based knowledge graph completion (KGC) methods leveraging textual entity descriptions are at the forefront of research. These methods involve advanced optimization techniques such as soft prompts and contrastive learning to enhance KGC models. The effectiveness of text-based methods largely hinges on the quality and richness of the training data. Large language models (LLMs) can utilize straightforward prompts to alter text data, thereby enabling data augmentation for KGC. Nevertheless, LLMs typically demand substantial computational resources. To address these issues, we introduce a framework termed constrained prompts for KGC (CP-KGC). This CP-KGC framework designs prompts that adapt to different datasets to enhance semantic richness. Additionally, CP-KGC employs a context constraint strategy to effectively identify polysemous entities within KGC datasets. Through extensive experimentation, we have verified the effectiveness of this framework. Even after quantization, the LLM (Qwen-7B-Chat-int4) still enhances the performance of text-based KGC methods.11Code and datasets are available at https://github.com/sjlmg/CP-KGC. This study extends the performance limits of existing models and promotes further integration of KGC with LLMs.

Biotextgraph: graphical summarization of functional similarities from textual information.

Functional interpretation of biological entities such as differentially expressed genes is one of the fundamental analyses in bioinformatics. The task can be addressed by using biological pathway databases with enrichment analysis (EA). However, textual description of biological entities in public databases is less explored and integrated in existing tools and it has a potential to reveal new mechanisms. Here, we present a new R package biotextgraph for graphical summarization of omics' textual description data which enables assessment of functional similarities of the lists of biological entities. We illustrate application examples of annotating gene identifiers in addition to EA. The results suggest that the visualization based on words and inspection of biological entities with text can reveal a set of biologically meaningful terms that could not be obtained by using biological pathway databases alone. The results suggest the usefulness of the package in the routine analysis of omics-related data. The package also offers a web-based application for convenient querying. The package, documentation, and web server are available at: https://github.com/noriakis/biotextgraph.

Detecting and Preventing Hallucinations in Large Vision Language Models

Instruction tuned Large Vision Language Models (LVLMs) have significantly advanced in generalizing across a diverse set of multi-modal tasks, especially for Visual Question Answering (VQA). However, generating detailed responses that are visually grounded is still a challenging task for these models. We find that even the current state-of-the-art LVLMs (InstructBLIP) still contain a staggering 30 percent of the hallucinatory text in the form of non-existent objects, unfaithful descriptions, and inaccurate relationships. To address this, we introduce M-HalDetect, a Multimodal Hallucination Detection Dataset that can be used to train and benchmark models for hallucination detection and prevention. M-HalDetect consists of 16k fine-grained annotations on VQA examples, making it the first comprehensive multi-modal hallucination detection dataset for detailed image descriptions. Unlike previous work that only consider object hallucination, we additionally annotate both entity descriptions and relationships that are unfaithful. To demonstrate the potential of this dataset for hallucination prevention, we optimize InstructBLIP through our novel Fine-grained Direct Preference Optimization (FDPO). We also train fine-grained multi-modal reward models from InstructBLIP and evaluate their effectiveness with best-of-n rejection sampling (RS). We perform human evaluation on both FDPO and rejection sampling, and find that they reduce hallucination rates in InstructBLIP by 41% and 55% respectively. We also find that our reward model generalizes to other multi-modal models, reducing hallucinations in LLaVA and mPLUG-OWL by 15% and 57% respectively, and has strong correlation with human evaluated accuracy scores. The dataset is available at https://github.com/hendryx-scale/mhal-detect.

Anchoring Path for Inductive Relation Prediction in Knowledge Graphs

Aiming to accurately predict missing edges representing relations between entities, which are pervasive in real-world Knowledge Graphs (KGs), relation prediction plays a critical role in enhancing the comprehensiveness and utility of KGs. Recent research focuses on path-based methods due to their inductive and explainable properties. However, these methods face a great challenge when lots of reasoning paths do not form Closed Paths (CPs) in the KG. To address this challenge, we propose Anchoring Path Sentence Transformer (APST) by introducing Anchoring Paths (APs) to alleviate the reliance of CPs. Specifically, we develop a search-based description retrieval method to enrich entity descriptions and an assessment mechanism to evaluate the rationality of APs. APST takes both APs and CPs as the inputs of a unified Sentence Transformer architecture, enabling comprehensive predictions and high-quality explanations. We evaluate APST on three public datasets and achieve state-of-the-art (SOTA) performance in 30 of 36 transductive, inductive, and few-shot experimental settings.

Medical knowledge graph completion via fusion of entity description and type information

Medical Knowledge Graphs (MKGs) are vital in propelling big data technologies in healthcare and facilitating the realization of medical intelligence. However, large-scale MKGs often exhibit characteristics of data sparsity and missing facts. Following the latest advances, knowledge embedding addresses these problems by performing knowledge graph completion. Most knowledge embedding algorithms rely solely on triplet structural information, overlooking the rich information hidden within entity property sets, leading to bottlenecks in performance enhancement when dealing with the intricate relations of MKGs. Inspired by the semantic sensitivity and explicit type constraints unique to the medical domain, we propose BioBERT-based graph embedding model. This model represents an evolvable framework that integrates graph embedding, language embedding, and type information, thereby optimizing the utility of MKGs. Our study utilizes not only WordNet as a benchmark dataset but also incorporates MedicalKG to compare and corroborate the specificity of medical knowledge. Experimental results on these datasets indicate that the proposed fusion framework achieves state-of-art (SOTA) performance compared to other baselines. We believe that this incremental improvement provides promising insights for future medical knowledge graph completion endeavors.

A hierarchical shape description approach and its application in similarity measurement of polygon entities

Spatial similarity provides an important basis for geographic information processing and is widely applied in multi-source data fusion and update, data retrieval and query, and cartographic generalization. To address the shape description and similarity measurement of polygon entities, this study presents a new hierarchical shape description approach and examines its application in similarity measurement of polygon entities. Using the rotation and segmentation methods, we first constructed a hierarchical shape description model for target polygon entities, followed by measurement of global and hierarchical shape description of polygon entities, respectively, using the farthest-point-distance and geometric feature description methods. Finally, we constructed a comprehensive similarity measurement model through a weighted integration of position, size, direction, and shape. The hierarchical shape description approach proposed in this paper can be applied to the shape similarity measurement of polygon elements, similarity measurement after spatial object simplification, and multi-scale polygon entity matching. The experimental results showed that the hierarchical shape description approach and similarity measurement model are able to effectively measure spatial similarity between different polygon entities, and have obtained good application results.

The role of metaphor in creating polysemy complexes in Jordanian Arabic and American English

Most papers written on polysemy focus on sense overlaps and lexical ambiguity, yet studies that explore the possibility of establishing a polysemic complex and explaining how the new interpretations arise through metaphor are almost non-existent in Arabic. This paper aims to explore how metaphor serves to create new concepts as part of polysemic complexes through adopting Dynamic Conceptual Semantics. The target words are bidʒannin [make mad][1] in Jordanian Arabic (JA) and mad in American English (AE). An online questionnaire containing 15 items was sent to forty participants (20 JA speakers and 20 AE speakers) where they were asked to provide the interpretations of the words bidʒannin and mad in contextualized sentences. The AE contextualized instances of mad were collected from Corpus of Contemporary American English (COCA) which generated 5,168 tokens of mad (in the years 2015/2019). The questionnaire results were discussed in a semi-structured focus-group discussion involving 10 participants. We have demonstrated that when an expression is deemed suitable for all situations categorized under both the primary perspective of madness and a related perspective involving exaggerated descriptions of entities, a concept (P) emerges that bears similarity or relevance to the polysemic complex ( bidʒannin\mad ) to which the expression belongs. In such cases, we can consider the related perspective (P') as a member of the polysemic complex ( bidʒannin\mad ). Thus, this study explains how the same metaphor can lead to a complex of multiple meanings in two different languages that are not necessarily related to each other.

2021 WHO Classification of Lung Cancer: Molecular Biology Research and Radiologic-Pathologic Correlation.

The 2021 World Health Organization (WHO) classification system for thoracic tumors (including lung cancer) contains several updates to the 2015 edition. Revisions for lung cancer include a new grading system for invasive nonmucinous adenocarcinoma that better reflects prognosis, reorganization of squamous cell carcinomas and neuroendocrine neoplasms, and description of some new entities. Moreover, remarkable advancements in our knowledge of genetic mutations and targeted therapies have led to a much greater emphasis on genetic testing than that in 2015. In 2015, guidelines recommended evaluation of only two driver mutations, ie, epidermal growth factor receptor (EGFR) mutations and anaplastic lymphoma kinase (ALK) fusions, in patients with nonsquamous non-small cell lung cancer. The 2021 guidelines recommend testing for numerous additional gene mutations for which targeted therapies are now available including ROS1, RET, NTRK1-3, KRAS, BRAF, and MET. The correlation of imaging features and genetic mutations is being studied. Testing for the immune biomarker programmed death ligand 1 is now recommended before starting first-line therapy in patients with metastatic non-small cell lung cancer. Because 70% of lung cancers are unresectable at patient presentation, diagnosis of lung cancer is usually based on small diagnostic samples (ie, biopsy specimens) rather than surgical resection specimens. The 2021 version emphasizes differences in the histopathologic interpretation of small diagnostic samples and resection specimens. Radiologists play a key role not only in evaluation of tumor and metastatic disease but also in identification of optimal biopsy targets. ©RSNA, 2024 Test Your Knowledge questions in the supplemental material and the slide presentation from the RSNA Annual Meeting are available for this article.

Security scenarios: 3D Printed Firearms

The aim of this study was to determine possible future scenarios if 3D printed firearms were to be manufactured in society. These scenarios provide a closer examination of the factors influencing the use of this relatively new method of firearms production. We reviewed the factors in 3D printed firearms that are relevant to various security authorities. Scenario analysis enables the examination of problem-solving for this multi-dimensional, multi-layered and multi-scale phenomenon. The topic was approached from a systemic perspective based on complexity thinking. The scenario-creation method employed five forecasting techniques applicable to the Playbook for Strategic Foresight and Innovation (Carleton, 2013). A context map created the basis for factor analysis, generational arcs, white spots and expert panel. The scenarios were developed into a structural format, allowing for the description of coherent entities. Upon analysing the scenarios, it was found that the ease of manufacturing 3D printed firearms could lead to a rapid increase in their numbers, potentially resulting in decreased internal and external security. The networking of manufacturers could also enable the production of weapons for individuals who previously did not have access to firearms. On the other hand, 3D printed firearms could open up new legal uses and possibilities. The networking of manufacturers could also enable the production of weapons for individuals who previously did not have access to firearms. On the other hand, 3D-printed firearms could open up new legal uses and possibilities. <b></b>

MADLINK: Attentive multihop and entity descriptions for link prediction in knowledge graphs

Knowledge Graphs (KGs) comprise of interlinked information in the form of entities and relations between them in a particular domain and provide the backbone for many applications. However, the KGs are often incomplete as the links between the entities are missing. Link Prediction is the task of predicting these missing links in a KG based on the existing links. Recent years have witnessed many studies on link prediction using KG embeddings which is one of the mainstream tasks in KG completion. To do so, most of the existing methods learn the latent representation of the entities and relations whereas only a few of them consider contextual information as well as the textual descriptions of the entities. This paper introduces an attentive encoder-decoder based link prediction approach considering both structural information of the KG and the textual entity descriptions. Random walk based path selection method is used to encapsulate the contextual information of an entity in a KG. The model explores a bidirectional Gated Recurrent Unit (GRU) based encoder-decoder to learn the representation of the paths whereas SBERT is used to generate the representation of the entity descriptions. The proposed approach outperforms most of the state-of-the-art models and achieves comparable results with the rest when evaluated with FB15K, FB15K-237, WN18, WN18RR, and YAGO3-10 datasets.

Product promotion copywriting from multimodal data: New benchmark and model

In our latest project, we devise a comprehensive corpus for product promotion text generation, named Video-Enabled Product Promotion Corpus (VPPC), which integrates multimodal and multi-structural information of products such as visual spatial details and fine structural specifics. It is crucial to highlight that this is one of the largest datasets available in the field of video captioning. Notably, conventional multimodal text generation often focuses on regular descriptions of entities and events, which doesn not suffice the real-world requirements of product promotion copywriting, as it necessitates a more lively language style and a high degree of authenticity. Regrettably, there is an evident lack of reusable evaluation frameworks and sufficient datasets at the current stage. To address these challenges, we have proposed a unique baseline approach and authenticity evaluation metric, both tailored to meet the realistic demands of our dataset. The results are promising, as our method surpasses previous approaches across all evaluation metrics.

A knowledge-graph based text summarization scheme for mobile edge computing

As the demand for edge services intensifies, text, being the most common type of data, has seen a significant expansion in data volume and an escalation in processing complexity. Furthermore, mobile edge computing (MEC) service systems often faces challenges such as limited computational capabilities and difficulties in data integration, requiring the development and implementation of more efficient and lightweight methodologies for text data processing. To swiftly extract and analysis vital information from MEC text data, an automatic generation scheme of multi-document text summarization based on knowledge graph is proposed in this paper, named KGCPN. For the text data from MEC devices and applications, the natural language processing technology is used to execute the data pre-processing steps, which transforms the MEC text data into a computationally tractable and semantically comprehensible format. Then, the knowledge graph of multi-document text is constructed by integrating the relationship paths and entity descriptions. The nodes and edges of the knowledge graph serve to symbolize the semantic relationships within the text, and the Graph Convolution Neural network (GCN) is used to understand the text and learn the semantic representation. Finally, a pointer-generator network model accepts the encoding information from GCN and automatically generate a general text summarization. The experimental results indicate that our scheme can effectively facilitate the smart pre-processing and integration of MEC data.

Knowledge Graphs and Pretrained Language Models Enhanced Representation Learning for Conversational Recommender Systems.

Conversational recommender systems (CRSs) utilize natural language interactions and dialog history to infer user preferences and provide accurate recommendations. Due to the limited conversation context and background knowledge, existing CRSs rely on external sources such as knowledge graphs (KGs) to enrich the context and model entities based on their interrelations. However, these methods ignore the rich intrinsic information within entities. To address this, we introduce the knowledge-enhanced entity representation learning (KERL) framework, which leverages both the KG and a pretrained language model (PLM) to improve the semantic understanding of entities for CRS. In our KERL framework, entity textual descriptions are encoded via a PLM, while a KG helps reinforce the representation of these entities. We also employ positional encoding to effectively capture the temporal information of entities in a conversation. The enhanced entity representation is then used to develop a recommender component that fuses both entity and contextual representations for more informed recommendations, as well as a dialog component that generates informative entity-related information in the response text. A high-quality KG with aligned entity descriptions is constructed to facilitate this study, namely, the Wiki Movie Knowledge Graph (WikiMKG). The experimental results show that KERL achieves state-of-the-art results in both recommendation and response generation tasks. Our code is publicly available at the link: https://github.com/icedpanda/KERL.

Biomedical Information Integration via Adaptive Large Language Model Construction.

Integrating diverse biomedical knowledge information is essential to enhance the accuracy and efficiency of medical diagnoses, facilitate personalized treatment plans, and ultimately improve patient outcomes. However, Biomedical Information Integration (BII) faces significant challenges due to variations in terminology and the complex structure of entity descriptions across different datasets. A critical step in BII is biomedical entity alignment, which involves accurately identifying and matching equivalent entities across diverse datasets to ensure seamless data integration. In recent years, Large Language Model (LLMs), such as Bidirectional Encoder Representations from Transformers (BERTs), have emerged as valuable tools for discerning heterogeneous biomedical data due to their deep contextual embeddings and bidirectionality. However, different LLMs capture various nuances and complexity levels within the biomedical data, and none of them can ensure their effectiveness in all heterogeneous entity matching tasks. To address this issue, we propose a novel Two-Stage LLM construction (TSLLM) framework to adaptively select and combine LLMs for Biomedical Information Integration (BII). First, a Multi-Objective Genetic Programming (MOGP) algorithm is proposed for generating versatile high-level LLMs, and then, a Single-Objective Genetic Algorithm (SOGA) employs a confidence-based strategy is presented to combine the built LLMs, which can further improve the discriminative power of distinguishing heterogeneous entities. The experiment utilizes OAEI's entity matching datasets, i.e., Benchmark and Conference, along with LargeBio, Disease and Phenotype datasets to test the performance of TSLLM. The experimental findings validate the efficiency of TSLLM in adaptively differentiating heterogeneous biomedical entities, which significantly outperforms the leading entity matching techniques.

Kids Lung Registry and Child-EU Project - Progress in Rare and Interstitial Lung Diseases in Childhood Through Collaboration

Progress in rare and interstitial lung disease in childhood can most usefully be achieved through systematic, registry-based collection. What are the practicalities and benefits of participating in the pediatric lung registry/chILD-EU project? We report our clinical experiences. Pediatricians and pediatric pulmonologists identify children with rare lung diseases. These are reported to the Kid's Lung Register after parental consent. Clinical data, imaging, and blood are sent to the registry. Genetic analysis can be arranged if desired. With completeness of the data, a peer-review process by pediatric radiology, possibly lung pathology, clinical and possibly genetic experts takes place in an interdisciplinary conference. A working diagnosis is established and communicated to the responsible physician via the registry and, if necessary, further discussed in case-related discussions. Assistance in entering the data is provided by the registry. Follow-ups are performed annually, and all registered physicians are invited to regular, web-based case discussions. Significant questions are answered in scientific projects and jointly published (>110 publications to date). Due to voluntary additional work of all participants beyond clinical routine, more than 1000 children with rare lung diseases have been included in the registry with biobank to date. A deeper understanding of the clinical courses of large cohorts of rare diseases and the initial description of new entities contributes to better care for these children.

An effective link prediction method for industrial knowledge graphs by incorporating entity description and neighborhood structure information

An effective link prediction method for industrial knowledge graphs by incorporating entity description and neighborhood structure information

Entity Summarization via Exploiting Description Complementarity and Salience.

Entity summarization is a novel and efficient way to understand real-world facts and solve the increasing information overload problem in large-scale knowledge graphs (KG). Existing studies mainly rely on ranking independent entity descriptions as a list under a certain scoring standard such as importance. However, they often ignore the relatedness and even semantic overlap between individual descriptions. This may seriously interfere with the contribution judgment of descriptions for entity summarization. Actually, the entity summary is a whole to comprehensively integrate the main aspects of entity descriptions, which could be naturally treated as a set. Unfortunately, the exploration of these set characteristics for entity summarization is still an open issue with great challenges. To that end, we draw inspiration from a set completion perspective and propose an entity summarization method with complementarity and salience (ESCS) to deeply exploit description complementarity and salience in order to form a summary set for the target entity. Specifically, we first generate entity description representations with textual features in the description embedding module. For the purpose of learning complementary relationships within the entire summary set, we devise a bi-directional long short-term memory structure to capture global complementarity for each summary in the summary complementarity learning module. Meanwhile, in order to estimate the salience of individual descriptions, we calculate similarities between semantic embeddings of the target entity and its property-value pairs in the description salience learning module. Next, with a joint learning stage, we can optimize ESCS from a set completion perspective. Finally, a summary generation strategy is designed to infer the entire summary set step-by-step for the target entity. Extensive experiments on a public benchmark have clearly demonstrated the effectiveness of ESCS and revealed the potential of set completion in entity summarization task.