Types Of Entities Research Articles

Dual Contrastive Learning for Cross-Domain Named Entity Recognition

Benefiting many information retrieval applications, named entity recognition (NER) has shown impressive progress. Recently, there has been a growing trend to decompose complex NER tasks into two subtasks (e.g., entity span detection (ESD) and entity type classification (ETC), to achieve better performance. Despite the remarkable success, from the perspective of representation, existing methods do not explicitly distinguish non-entities and entities, which may lead to ESD errors. Meanwhile, they do not explicitly distinguish entities with different entity types, which may lead to entity type misclassification. As such, the limited representation abilities may challenge some competitive NER methods, leading to unsatisfactory performance, especially in the low-resource setting (e.g., cross-domain NER). In light of these challenges, we propose to utilize contrastive learning to refine the original chaotic representations and learn the generalized representations for cross-domain NER. In particular, this article proposes a dual contrastive learning model (Dual-CL), which respectively utilizes a token-level contrastive learning module and a sentence-level contrastive learning module to enhance ESD, ETC for cross-domain NER. Empirical results on 10 domain pairs under two different settings show that Dual-CL achieves better performances than compared baselines in terms of several standard metrics. Moreover, we conduct detailed analyses to are presented to better understand each component’s effectiveness.

Types of legal entities under public law in German doctrine and legislation

This article examines the concepts and types of legal entities under public law in German doctrine and legislation. The legal status of public legal entities is regulated by the provisions of the German Constitution, the German Civil Code, the Law on Administrative Procedures, the Law «On the Procedure for Consideration of Cases Under the Management of Administrative Bodies», the Law «On Administrative Courts». The author examines the issue of determining the criteria for the classification of legal entities, so legal entities under public law are created in an executive order, act to satisfy the public or public interest, have authority, operate in organizational and legal forms of an open type, are responsible for the activities of their officials, legal the regime of property has certain features, in particular, such property can be transferred for use by legal entities under public law. As a rule, legal entities under public law have a special scope of legal capacity, unlike legal entities under private law, in this regard, the article analyzes the doctrine of ultra vires. The author examines the procedure for creating legal entities under public law in Germany, the types of organizational and legal forms of legal entities under public law that carry out their activities in the form of a public corporation, public institution, public fund. The article considers the sources of property formation of legal entities under public law, in particular, it can be income from entrepreneurial activity, budget funds, contributions of members of public corporations, donations (for example, the German State Bank, the German Chamber of Commerce and Industry, the Prussian Cultural Heritage Fund). The article considers four types of public corporations: territorial, united, personal, real. A feature of the activity of public corporations in Germany is the presence of membership, unlike public institutions.

M&A contracts in Ukraine: legal regulation and the mechanism of their conclusion

The article is devoted to the study of legal regulation of M&A contracts and mechanisms of their conclusion in Ukraine as a generally accepted way of business scaling. The author establishes that, according to the current regulatory provisions, M&A contracts may be embodied in the following ways: reorganisation procedures of mergers and acquisitions; establishment of control over an enterprise by acquiring a share in the authorised capital or shares of a company; asset deal, as well as a loan agreement with an alternative obligation within the Diia City legal regime. The author stated the diversity of legislation on reorganisation in the form of mergers and acquisitions - the detailed, step-by-step, European-compliant regulation of mergers and acquisitions of joint stock companies contrasts with fragmented and scattered legislation on reorganisation of other types of business entities. It has been emphasised that despite the complexity and resource­consuming nature of reorganisation in the form of mergers and acquisitions, business entities use these procedures due to their inherent universal succession, which in turn leads to tax-neutral consequences of the changes. The author additionally argues that negotiability relates to the share in the authorised capital of a company, and not to corporate rights. Based on the analysis of the legislative framework and law enforcement practice, the author establishes that an alienation agreement of a share in the authorised capital may be: concluded with both company members and third parties; with or without consideration; concluded only in respect of a paid share (at the same time, the legislative prohibition on alienation of unpaid shares in an LLC is aimed at protecting the legitimate interests of other persons, not company members who have not paid for the share); concluded both orally and in writing. The author outlines the regulatory peculiarities of acquiring a significant and controlling shareholding in joint stock companies. The author systematises the types of contracts which are asset deals by their very nature. It has been substantiated the expediency of transferring debts as a part of a single property complex to its acquirer. The article examines the legal regulation of loan agreements with alternative obligations, which are in essence analogous to the convertible loan mechanism common abroad. The author emphasises that despite the principle of freedom of contract, a loan agreement with an alternative obligation cannot be used outside the legal regime of the Diia City. It was concluded that it is necessary to regulate in more detail the conclusion of such an agreement; the variables used in calculating the creditor’s share; the rights and obligations of the creditor and the debtor.

Features of the regulation of the legal status of public law legal entities in France

In the article, the author considers issues related to the legal status of legal entities under public law in France. The legal status of legal entities under public law is regulated by the Constitution of France, special legislation in the field of administrative law. The article analyzes the criteria for distinguishing legal entities into legal entities of private and public law, developed by the judicial practice of French courts. In the article, the author examines the types of legal entities of French public law, in particular, the latter include: the state, territorial entities, public institutions. The article examines the legal nature and types of public service, which is carried out in the field of defense, transport, and industry management. The article defines a public service, as a rule, the specified services do not have the status of a legal entity, the specified status belongs to the state that creates these services. The author researches public institutions of France that belong to legal entities under public law. The article defines the concept of a public institution, classifies public institutions according to various criteria, and defines the organizational structure of these institutions. A variety of public institutions is industrial and commercial in nature. The author examines the historical aspects of the emergence and development of this type of institution. The article examines the characteristics of a public institution, in particular, the following characteristics are distinguished: 1) it is a legal entity under public law; 2) has a socially beneficial purpose; 3) is under the supervision (guardianship) of the state can be fully applied to the legal regulation of public benefit institutions, which also have the status of a legal entity, pursue a socially beneficial goal, and are under the supervision (guardianship) of the state. The article examines the legal nature of associations of persons of liberal professions (lawyers, attorneys, notaries, doctors, pharmacists, veterinarians, and others). The article examines the judicial practice of French courts on issues of compensation for damage caused by the activities of a state institution. The author examines and investigates issues of the legal regime of property of legal entities under public law.

Use of SNOMED CT in Large Language Models: Scoping Review.

Large language models (LLMs) have substantially advanced natural language processing (NLP) capabilities but often struggle with knowledge-driven tasks in specialized domains such as biomedicine. Integrating biomedical knowledge sources such as SNOMED CT into LLMs may enhance their performance on biomedical tasks. However, the methodologies and effectiveness of incorporating SNOMED CT into LLMs have not been systematically reviewed. This scoping review aims to examine how SNOMED CT is integrated into LLMs, focusing on (1) the types and components of LLMs being integrated with SNOMED CT, (2) which contents of SNOMED CT are being integrated, and (3) whether this integration improves LLM performance on NLP tasks. Following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines, we searched ACM Digital Library, ACL Anthology, IEEE Xplore, PubMed, and Embase for relevant studies published from 2018 to 2023. Studies were included if they incorporated SNOMED CT into LLM pipelines for natural language understanding or generation tasks. Data on LLM types, SNOMED CT integration methods, end tasks, and performance metrics were extracted and synthesized. The review included 37 studies. Bidirectional Encoder Representations from Transformers and its biomedical variants were the most commonly used LLMs. Three main approaches for integrating SNOMED CT were identified: (1) incorporating SNOMED CT into LLM inputs (28/37, 76%), primarily using concept descriptions to expand training corpora; (2) integrating SNOMED CT into additional fusion modules (5/37, 14%); and (3) using SNOMED CT as an external knowledge retriever during inference (5/37, 14%). The most frequent end task was medical concept normalization (15/37, 41%), followed by entity extraction or typing and classification. While most studies (17/19, 89%) reported performance improvements after SNOMED CT integration, only a small fraction (19/37, 51%) provided direct comparisons. The reported gains varied widely across different metrics and tasks, ranging from 0.87% to 131.66%. However, some studies showed either no improvement or a decline in certain performance metrics. This review demonstrates diverse approaches for integrating SNOMED CT into LLMs, with a focus on using concept descriptions to enhance biomedical language understanding and generation. While the results suggest potential benefits of SNOMED CT integration, the lack of standardized evaluation methods and comprehensive performance reporting hinders definitive conclusions about its effectiveness. Future research should prioritize consistent reporting of performance comparisons and explore more sophisticated methods for incorporating SNOMED CT's relational structure into LLMs. In addition, the biomedical NLP community should develop standardized evaluation frameworks to better assess the impact of ontology integration on LLM performance.

Named Entity Recognition for Equipment Fault Diagnosis Based on RoBERTa-wwm-ext and Deep Learning Integration

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text.

Augmenting biomedical named entity recognition with general-domain resources

ObjectiveTraining a neural network-based biomedical named entity recognition (BioNER) model usually requires extensive and costly human annotations. While several studies have employed multi-task learning with multiple BioNER datasets to reduce human effort, this approach does not consistently yield performance improvements and may introduce label ambiguity in different biomedical corpora. We aim to tackle those challenges through transfer learning from easily accessible resources with fewer concept overlaps with biomedical datasets. MethodsWe proposed GERBERA, a simple-yet-effective method that utilized general-domain NER datasets for training. We performed multi-task learning to train a pre-trained biomedical language model with both the target BioNER dataset and the general-domain dataset. Subsequently, we fine-tuned the models specifically for the BioNER dataset. ResultsWe systematically evaluated GERBERA on five datasets of eight entity types, collectively consisting of 81,410 instances. Despite using fewer biomedical resources, our models demonstrated superior performance compared to baseline models trained with additional BioNER datasets. Specifically, our models consistently outperformed the baseline models in six out of eight entity types, achieving an average improvement of 0.9% over the best baseline performance across eight entities. Our method was especially effective in amplifying performance on BioNER datasets characterized by limited data, with a 4.7% improvement in F1 scores on the JNLPBA-RNA dataset. ConclusionThis study introduces a new training method that leverages cost-effective general-domain NER datasets to augment BioNER models. This approach significantly improves BioNER model performance, making it a valuable asset for scenarios with scarce or costly biomedical datasets. We make data, codes, and models publicly available via https://github.com/qingyu-qc/bioner_gerbera.

Uncovering condition information loss in medical text extraction: The challenge of non-contiguous spans

We investigated the limitations of conventional named entity recognition (NER) and entity linking (EL) methods in accurately extracting patient condition information from medical texts, focusing on the challenges posed by non-contiguous spans and the potential information loss. We utilized a corpus with entity-relation annotations, analyzing the frequency and nature of non-contiguous spans that include irrelevant entities within gaps. The corpus was further analyzed to pinpoint the types of entity representations predominantly linked with peripheral spans—those not encompassing central symptom-describing terms—with a focus on items, body parts, and clinical tests. Our analysis revealed that 18.6 % of patient condition expressions were non-contiguous spans containing irrelevant entities, suggesting an accuracy ceiling of 81.4 % for conventional NER and EL approaches in the worst-case scenario. The study highlights the importance of entity types such as items, body parts, and clinical tests in these expressions, indicating that conventional extraction methods incur considerable information loss. The findings underscore the need for more sophisticated information extraction techniques capable of handling the complexities of medical texts, including non-contiguous spans. Adapting methods that allow gaps within entities or employing graph-based term assignments can enhance the accuracy and comprehensiveness of medical text annotation.

TarKG: a comprehensive biomedical knowledge graph for target discovery.

Target discovery is a crucial step in drug development, as it directly affects the success rate of clinical trials. Knowledge graphs (KGs) offer unique advantages in processing complex biological data and inferring new relationships. Existing biomedical KGs primarily focus on tasks such as drug repositioning and drug-target interactions, leaving a gap in the construction of KGs tailored for target discovery. We established a comprehensive biomedical KG focusing on target discovery, termed TarKG, by integrating seven existing biomedical KGs, nine public databases, and traditional Chinese medicine knowledge databases. TarKG consists of 1143313 entities and 32806467 relations across 15 entity categories and 171 relation types, all centered around 3 core entity types: Disease, Gene, and Compound. TarKG provides specialized knowledges for the core entities including chemical structures, protein sequences, or text descriptions. By using different KG embedding algorithms, we assessed the knowledge completion capabilities of TarKG, particularly for disease-target link prediction. In case studies, we further examined TarKG's ability to predict potential protein targets for Alzheimer's disease (AD) and to identify diseases potentially associated with the metallo-deubiquitinase CSN5, using literature analysis for validation. Furthermore, we provided a user-friendly web server (https://tarkg.ddtmlab.org) that enables users to perform knowledge retrieval and relation inference using TarKG. TarKG is accessible at https://tarkg.ddtmlab.org.

Entity Linking Model Based on Cascading Attention and Dynamic Graph

The purpose of entity linking is to connect entity mentions in text to real entities in the knowledge base. Existing methods focus on using the text topic, entity type, linking order, and association between entities to obtain the target entities. Although these methods have achieved good results, they ignore the exploration of candidate entities, leading to insufficient semantic information among entities. In addition, the implicit relationship and discrimination within the candidate entities also affect the accuracy of entity linking. To address these problems, we introduce information about candidate entities from Wikipedia and construct a graph model to capture implicit dependencies between different entity decisions. Specifically, we propose a cascade attention mechanism and develop a novel local entity linkage model termed CAM-LEL. This model leverages the interaction between entity mentions and candidate entities to enhance the semantic representation of entities. Furthermore, a global entity linkage model termed DG-GEL based on a dynamic graph is established to construct an entity association graph, and a random walking algorithm and entity entropy are used to extract the implicit relationships within entities to increase the differentiation between entities. Experimental results and in-depth analyses of multiple datasets show that our model outperforms other state-of-the-art models.

Кримінально-правова характеристика суб’єкта корупційних та пов’язаних з корупцією правопорушень у сфері охорони здоров’я

The article is devoted to the analysis of the legislative regulation of subjects of corruption and corruption-related criminal offenses, finding out their specifics and types in the field of health care. A comparative description of the definition of subjects of corruption offenses in accordance with the Criminal Code of Ukraine and the Law of Ukraine "On Prevention of Corruption" has been made, a number of shortcomings have been identified and certain considerations have been expressed regarding the directions for improving the legislation. It is stated that the peculiarity of the subject of a corruption offense is the presence of special features that indicate, first of all, a certain legal status of a person, due to the sphere of activity (public or private), position, functions performed, powers, etc. The Criminal Code of Ukraine provides for a wide range of types of special subject of corruption criminal offenses, among which the key is an official. It was found that the anti-corruption legislation applies to all categories of health workers. At the same time, the Criminal Code of Ukraine provides for differentiation of responsibility for accepting a proposal, promising or receiving undue benefit by a special entity, given the presence or absence of the status of an official and a type of legal entity (public or private law) or the provision of public services. The most controversial for the doctrine of criminal law and law enforcement practice is the problem of a criminal legal assessment of a health worker receiving undue benefits in connection with legally significant actions. In particular, such acts can be qualified both under Art. 368 or 368-3 and under Art. 354 or 368-4 of the Criminal Code of Ukraine, which indicates primarily the shortcomings of criminal legislation and the lack of unified theoretical developments in this area.

HunFlair2 in a cross-corpus evaluation of biomedical named entity recognition and normalization tools.

With the exponential growth of the life sciences literature, biomedical text mining (BTM) has become an essential technology for accelerating the extraction of insights from publications. The identification of entities in texts, such as diseases or genes, and their normalization, i.e. grounding them in knowledge base, are crucial steps in any BTM pipeline to enable information aggregation from multiple documents. However, tools for these two steps are rarely applied in the same context in which they were developed. Instead, they are applied "in the wild," i.e. on application-dependent text collections from moderately to extremely different from those used for training, varying, e.g. in focus, genre or text type. This raises the question whether the reported performance, usually obtained by training and evaluating on different partitions of the same corpus, can be trusted for downstream applications. Here, we report on the results of a carefully designed cross-corpus benchmark for entity recognition and normalization, where tools were applied systematically to corpora not used during their training. Based on a survey of 28 published systems, we selected five, based on predefined criteria like feature richness and availability, for an in-depth analysis on three publicly available corpora covering four entity types. Our results present a mixed picture and show that cross-corpus performance is significantly lower than the in-corpus performance. HunFlair2, the redesigned and extended successor of the HunFlair tool, showed the best performance on average, being closely followed by PubTator Central. Our results indicate that users of BTM tools should expect a lower performance than the original published one when applying tools in "the wild" and show that further research is necessary for more robust BTM tools. All our models are integrated into the Natural Language Processing (NLP) framework flair: https://github.com/flairNLP/flair. Code to reproduce our results is available at: https://github.com/hu-ner/hunflair2-experiments.

Актуальные вопросы противодействия коррупции при осуществлении публичных и корпоративных закупок

The article analyzes the main anti-corruption mechanisms provided for by federal Laws No. 44-FZ “On the contract system in the field of procurement of goods, works, and services for State and municipal needs” and No. 223-FZ“On Procurement of goods, works, and services by certain types of legal entities”. At the same time, consideration of issues of administrative and criminal liability for corruption is not included in the subject of the study within the framework of this article.

MAKG: A maritime accident knowledge graph for intelligent accident analysis and management

With the increasing frequency of human activities at sea, maritime accidents are occurring more often. Analyzing and mining maritime accident cases can help uncover the causal mechanisms behind these incidents, thereby enhancing maritime safety. As an emerging technology for knowledge management and mining, knowledge graphs offer significant support for the storage, reasoning, and decision-making processes related to maritime accidents.In this study, we established a knowledge graph construction and application framework for maritime accidents to facilitates the extraction and management of maritime knowledge from unstructured texts. First, 581 accident reports released by the China Maritime Safety Administration over the past decade (2014–2023) were used as the data basis for analysis and construction of the maritime accident ontology structure using the seven-step method, which comprises 8 entity types, 8 relationship types, and 18 attribute entity types. Second, We proposed MBERT-BiLSTM-CRF-SF, a named entity recognition model based on domain pretraining and self-training, to reduce graph construction costs. This model achieved state-of-the-art performance in the maritime domain, with an F1 score of 0.910 ± 0.006, which is about 5% higher than the mainstream model. In addition, we proposed an entity alignment method based on font and semantics to refine knowledge further. On the basis of the proposed method, we constructed a large, high-quality maritime accident knowledge graph (MAKG) system that contains 16,099 entities and 20,809 relationship instances. Finally, we reduced the complexity of applying knowledge graphs by integrating the CRISPE prompt learning framework of the large language model, and experiments on graph traversal, pattern recognition, and aggregation analysis were conducted to assess the quality of MAKG. Results demonstrate that MAKG can effectively enhance the efficiency of querying and reasoning about maritime accident information, thus providing significant support for the prevention and management of maritime accidents.

SWENER-1800

Named entity recognition (NER) is the process of automatically identifying persons, places, organisations and other name-like entities in text, in order to perform natural language processing tasks such as automatic extraction of metadata from text, anonymisation/pseudonymisation of sensitive personal data, or as a preprocessing step for linking different terms describing the same entity to a single reference. While NER is a mature language technology, it is generally lacking for historical language varieties. We describe our work on compiling SWENER-1800, a large (half a million words) reference corpus of historical Swedish texts, covering the time period from the first half of the 18th century until about 1900, and manually annotating it with named entity types identified as significant for this time period, as well as with sentence boundaries, notoriously difficult to recognise automatically in historical text. This corpus can then be used to train and evaluate NER systems and sentence segmenters for historical Swedish text. An additional concrete contribution from this work is a manual for annotation of named entities in historical Swedish.

Joint multimodal entity-relation extraction based on temporal enhancement and similarity-gated attention

Joint Multimodal Entity and Relation Extraction (JMERE), which needs to combine complex image information to extract entity-relation quintuples from text sequences, posts higher requirements of the model’s multimodal feature fusion and selection capabilities. With the advancement of large pre-trained language models, existing studies focus on improving the feature alignments between textual and visual modalities. However, there remains a noticeable gap in capturing the temporal information present in textual sequences. In addition, these methods exhibit a certain deficiency in distinguishing irrelevant images when integrating image and text features, making them susceptible to interference from image information unrelated to the text. To address these challenges, we propose a temporally enhanced and similarity-gated attention network (TESGA) for joint multimodal entity relation extraction. Specifically, we first incorporate an LSTM-based Text Temporal Enhancement module to enhance the model’s ability to capture temporal information from the text. Next, we introduce a Text-Image Similarity-Gated Attention mechanism, which controls the degree of incorporating image information based on the consistency between image and text features. Subsequently, We design the entity and relation prediction module using a form-filling approach based on entity and relation types, and conduct prediction of entity-relation quintuples. Notably, apart from the JMERE task, our approach can also be applied to other tasks involving text-visual enhancement, such as Multimodal Named Entity Recognition (MNER) and Multimodal Relation Extraction (MRE). To demonstrate the effectiveness of our approach, our model is extensively experimented on three benchmark datasets and achieves state-of-the-art performance. Our code will be available upon paper acceptance.11https://github.com/vacuum-cup/TESGA.

Research on the Management Model of University Teaching Materials based on Knowledge Graph

Colleges and universities will produce a large number of teaching materials in their teaching work. It is of great significance to collect and manage teaching materials. In this study, knowledge graph technology is applied to the management of teaching materials in colleges and universities, and an ontology model of teaching materials in colleges and universities based on knowledge graph is proposed. Based on the relevant norms of teaching resources ontology in China, the ontology model of teaching materials in colleges and universities is constructed by seven-step method, and it is transformed into the attribute graph model of teaching materials in colleges and universities, which is stored in the Neo4 j graph database. The model can define the entity type and the relationship between entities, and construct the subject knowledge framework.

Computer-Assisted Processing of Current Step Signals in Single Blocking Impact Electrochemistry.

Current step signals related to single-entity collisions in blocking impact electrochemistry were analyzed by computer-assisted processing for estimating the size distributions of various particles. In this work, three different types of entities were studied by single blocking impact electrochemistry: polystyrene nanospheres (350 nm diameter) and microspheres (1 μm diameter), phospholipid liposomes (300 nm diameter) and two different strains of Gram-negative bacillus bacteria (Escherichia coli and Shewanella oneidensis). The size estimations of these different entities from the current step signal analysis were compared and discussed according to the shape and size of each entity. From the magnitude of the current step transient, the size distribution of each entity was calculated by a new computer program assisting in the detection and analysis of single impact events in chronoamperometry measurements. The data processing showed that the size distributions obtained from the electrochemical data agreed with the dynamic light scattering and atomic force microscopy data for nanospheres and liposomes. In contrast, the size estimation calculated from the electrochemical data was underestimated for microspheres and bacteria. We demonstrated that our computer program was efficient for detecting and analyzing the collision events in single blocking impact electrochemistry for various entities from spherical hard nanoparticles to micrometer-sized rod-shaped living bacteria.

Improving dictionary-based named entity recognition with deep learning.

Dictionary-based named entity recognition (NER) allows terms to be detected in a corpus and normalized to biomedical databases and ontologies. However, adaptation to different entity types requires new high-quality dictionaries and associated lists of blocked names for each type. The latter are so far created by identifying cases that cause many false positives through manual inspection of individual names, a process that scales poorly. In this work, we aim to improve block list s by automatically identifying names to block, based on the context in which they appear. By comparing results of three well-established biomedical NER methods, we generated a dataset of over 12.5 million text spans where the methods agree on the boundaries and type of entity tagged. These were used to generate positive and negative examples of contexts for four entity types (genes, diseases, species, and chemicals), which were used to train a Transformer-based model (BioBERT) to perform entity type classification. Application of the best model (F1-score = 96.7%) allowed us to generate a list of problematic names that should be blocked. Introducing this into our system doubled the size of the previous list of corpus-wide blocked names. In addition, we generated a document-specific list that allows ambiguous names to be blocked in specific documents. These changes boosted text mining precision by ∼5.5% on average, and over 8.5% for chemical and 7.5% for gene names, positively affecting several biological databases utilizing this NER system, like the STRING database, with only a minor drop in recall (0.6%). All resources are available through Zenodo https://doi.org/10.5281/zenodo.11243139 and GitHub https://doi.org/10.5281/zenodo.10289360.

MARML: Motif-Aware Deep Representation Learning in Multilayer Networks.

The rapid increase in high-throughput, complex, and heterogeneous data has led to the adoption of network-structured models and analyses for interpretation. However, these data are inherently complex and challenging to understand, prompting researchers to turn to graph embedding methods to facilitate analysis. While general network embedding techniques have shown promise in improving downstream prediction and classification tasks, real-world data are complicated due to cross-domain interactions between different types of entities. Multilayered networks have been successful in integrating biological data to represent biological systems' hierarchy, but embedding nodes based on different types of interactions remains an unsolved problem. To address this challenge, we propose the Motif-aware deep representation learning in multilayer (MARML) networks for learning network representations. Our method considers recurring motif patterns, topological information, and attributive information from other sources as node features. We validated the MARML method using various multilayer network datasets. In addition, by incorporating motif information, MARML considers higher order connections across different hierarchies. The learned features exhibited excellent accuracy in tasks related to link prediction and link differentiation, enabling us to distinguish between existing and disconnected triplets. Through the integration of both intrinsic node attributes and topological network structures, we enhance our understanding of complex biological systems.