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The article introduces focuses on advancements in XLORE 3, a multilingual knowledge graph built to integrate and manage knowledge from diverse resources. Topics include the creation of an extensive XLORE ontology for managing entities across languages, the merging of equivalent entities for enhanced knowledge sharing, and a multi-strategy framework for knowledge completion using pre-trained language models.

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. [ABSTRACT FROM AUTHOR]

The principal goal of this study is to analyze the evolution of topics and trends in emerging research fields by a combination of entity linking, Mann–Kendall test, and burst detection techniques. Multiple methods are applied here in the emerging field of cloud computing by focusing on the frequency of critical topics from 2004 to 2021. Statistical analysis reveals that the Internet of Things exhibits a significant scientific growth compared to other subject areas within the research field of cloud computing. Other emerging topics with rapid growth are computer networks, encryption, big data, distributed computing, and interaction of cloud computing with virtual machine research. The combination of different techniques can better show the complex dynamics and complementary aspects of scientific topics and trends underlying evolutionary pathways in emerging fields, such as the science and technology advances of architecture, hardware, and software components in the field of cloud computing. In scientometrics, the analysis with multiple techniques provides comprehensive scientific and technological information driving new directions in the evolution of research fields to guide R&D investments towards growing topics and technologies having the potential of supporting fruitful scientific and technological change. [ABSTRACT FROM AUTHOR]

Abstract Aviation emergency management is playing a more and more important role in the aviation field. How to make effective use of massive heterogeneous and multi-source aviation accident knowledge has become a great challenge for aviation emergency management. Aiming at the problems such as too long physical length, mixed and composite entities, similar character of domain entity names, information difference between entities, separation of codes between entities, coding errors during transmission, etc., the construction method of knowledge map of civil aviation emergencies is studied. In previous research methods, entity link is always divided into two parts, that is, first detection and then disambiguation, which makes the mentioned entity and the candidate entity are encoded separately, and there is error transmission between the two parts, modules cannot communicate with each other, and the close association between entities cannot be well learned. In this paper, we proposed an end-to-end entity linking method based on two-layer BiLSTM model joint coding vectorize each word of civil aviation text information, and then concatenate feature vectors into two-layer BiLSTM model to obtain high-level context representation. Because the joint encoding of boundary information can reduce the error transmission, information is exchanged between candidate entities during the initial encoding to enhance the closeness between candidate entities and candidate entities. The experimental results show that compared with other sota models, the F1 value of the proposed model reaches 88.97%.

The accurate diagnosis and effective treatment of mental health disorders such as depression remain challenging owing to the complex underlying causes and varied symptomatology. Traditional information extraction methods struggle to adapt to evolving diagnostic criteria such as the Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5) and to contextualize rich patient data effectively. This study proposes a novel approach for enhancing information extraction from mental health data by integrating medical knowledge graphs and large language models (LLMs). Our method leverages the structured organization of knowledge graphs specifically designed for the rich domain of mental health, combined with the powerful predictive capabilities and zero-shot learning abilities of LLMs. This research enhances the quality of knowledge graphs through entity linking and demonstrates superiority over traditional information extraction techniques, making a significant contribution to the field of mental health. It enables a more fine-grained analysis of the data and the development of new applications. Our approach redefines the manner in which mental health data are extracted and utilized. By integrating these insights with existing healthcare applications, the groundwork is laid for the development of real-time patient monitoring systems. The performance evaluation of this knowledge graph highlights its effectiveness and reliability, indicating significant advancements in automating medical data processing and depression management. [ABSTRACT FROM AUTHOR]

Extracting named entities text forms the basis for many crucial tasks such as information retrieval and extraction, machine translation, opinion mining, sentiment analysis and question answering. This paper presents a survey of the research literature on named entity linking, including named entity recognition and disambiguation. We present 200 works by focusing on 43 papers (5 surveys and 38 research works). We also describe and classify 56 resources, including 25 tools and 31 corpora. We focus on the most recent papers, where more than 95% of the described research works are after 2015. To show the efficiency of our construction methodology and the importance of this state of the art, we compare it to other surveys presented in the research literature, which were based on different criteria (such as the domain, novelty and presented models and resources). We also present a set of open issues (including the dominance of the English language in the proposed studies and the frequent use of NER rather than the end-to-end systems proposing NED and EL) related to entity linking based on the research questions that this survey aims to answer. [ABSTRACT FROM AUTHOR]

This paper describes NEREL—a Russian news dataset suited for three tasks: nested named entity recognition, relation extraction, and entity linking. Compared to flat entities, nested named entities provide a richer and more complete annotation while also increasing the coverage of relations annotation and entity linking. Relations between nested named entities may cross entity boundaries to connect to shorter entities nested within longer ones, which makes it harder to detect such relations. NEREL is currently the largest Russian dataset annotated with entities and relations: it comprises 29 named entity types and 49 relation types. At the time of writing, the dataset contains 56 K named entities and 39 K relations annotated in 933 person-oriented news articles. NEREL is annotated with relations at three levels: (1) within nested named entities, (2) within sentences, and (3) with relations crossing sentence boundaries. We provide benchmark evaluation of current state-of-the-art methods in all three tasks. The dataset is freely available at https://github.com/nerel-ds/NEREL. [ABSTRACT FROM AUTHOR]

Knowledge graph question answering (KGQA) systems have an important role in retrieving data from a knowledge graph (KG). With the system, regular users can access data from a KG without the need to construct a formal SPARQL query. KGQA systems receive a natural language question (NLQ) and translate it into a SPARQL query through three main tasks, namely, entity and relation detection, entity and relation linking, and query construction. However, the translation is not trivial due to lexical gaps and entity ambiguity that may occur during entity or relation linking. This research proposed an approach based on multiclass classification of NLQ whose entity occurrences are detected into categories based on KG relations to address the lexical gap challenge. Next, to solve the entity ambiguity challenge, this research proposed a three-stage searching procedure to determine appropriate KG entities associated with the NLQ entities, given the correspondence between the NLQ and a particular KG relation. This three-stage searching consisted of text-based searching, vector-based searching, and entity and relation pairing. The proposed approach was evaluated on the SimpleQuestions and LC-QuAD 2.0 datasets. The experiments demonstrated that the proposed approach outperformed the state-of-the-art baseline. For the relation linking task, the proposed approach reached 89.87% and 74.83% recall for the SimpleQuestions and LC-QuAD 2.0, respectively. This approach also achieved 91.74% and 61.96% recall on the entity linking tasks for the SimpleQuestions and LC-QuAD 2.0, respectively.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learningmethod in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine learning. With a profound amalgamation of cutting-edge research in machine learning, this article undertakes a systematical exploration of KG construction methods in three distinct phases: entity learning, ontology learning, and knowledge reasoning. Especially, a meticulous dissection of machine learningdriven algorithms is conducted, spotlighting their contributions to critical facets such as entity extraction, relation extraction, entity linking, and link prediction. Moreover, this article also provides an analysis of the unresolved challenges and emerging trajectories that beckon within the expansive application of machine learning-fueled, large-scale KG construction. [ABSTRACT FROM AUTHOR]

Privacy is a fundamental human right according to the Universal Declaration of Human Rights of the United Nations. Adoption of the General Data Protection Regulation (GDPR) in European Union in 2018 was turning point in management of personal data, specifically personal identifiable information (PII). Although there were many previous privacy laws in existence before, GDPR has brought privacy topic in the regulatory spotlight. Two most important novelties are seven basic principles related to processing of personal data and huge fines defined for violation of the regulation. Many other countries have followed the EU with the adoption of similar legislation. Personal data management processes in companies, especially in analytical systems and Data Lakes, must comply with the regulatory requirements. In Data Lakes, there are no standard architectures or solutions for the need to discover personal identifiable information, match data about the same person from different sources, or remove expired personal data. It is necessary to upgrade the existing Data Lake architectures and metadata models to support these functionalities. The goal is to study the current Data Lake architecture and metadata models and to propose enhancements to improve the collection, discovery, storage, processing, and removal of personal identifiable information. In this paper, a new metadata model that supports the handling of personal identifiable information in a Data Lake is proposed.

Knowledge-Based Question Answering (KBQA) is a technique that utilizes the rich semantic information present in knowledge bases to comprehensively understand questions and obtain answers. The mainstream approaches consist of two methods: Semantic Parsing-Based (SP-based) and Information Retrieval-Based (IR-based). The former converts the question into a logical form that can be understood and executed by machines through semantic analysis, and then queries the knowledge base for answers. The latter first identifies the topic entity in the question and retrieves candidate answers, and then extracts features from both the question and candidate answers. Finally, a ranking model is used to model and predict the question and candidate answers. Compared to the impressive results achieved by English KBQA systems, Chinese KBQA systems face challenges due to the sparse semantic expression and limited features of the Chinese knowledge base, as well as the large number of similar entities that are difficult to differentiate. This makes it difficult for general models to properly understand the text's characteristics, resulting in a challenge to improve the accuracy of Entity Linking and to maximize the performance of the KBQA system. To address this, this paper proposes two steps to improve Entity Linking in the KBQA system: Candidate Generation (CG) and Entity Disambiguation (ED), with a focus on realizing Entity Disambiguation. In this paper, Entity Disambiguation is treated as a classification task, and a Dual-Channel Network Model based on Bi-LSTM and CNN is constructed. By combining different featuresextracted from Bi-LSTM and CNN, this paper also introduces an attention mechanism to fully explore the weak semantic relationship between the question answering system and candidate entity, effectively reducing the reliance of the question answering system on additional feature rules. Experimental results show that the Entity Linking model proposed in this paper can effectively improve the performance of the question and answer system, has strong generalization, weakens dependence on additional information, and ensures the quality of Q &A while reducing manual intervention. Our method has achieved the current best average F1 value in the Chinese open domain datasets NLPCC-2016KBQA and CCKS2019KBQA. [ABSTRACT FROM AUTHOR]

Neural entity-linking models excel at bridging the lexical gap of multiple facets of facts, such as entity-related claims or evidence documents. Despite advancements in self-supervised learning and pretrained language models, challenges persist in entity linking, particularly in interpretability and transferability. Moreover, these models need many aligned documents to adapt to emerging entities, which may not be available due to data scarcity. In this work, we propose a novel Demonstrative Self-TrAining fRamework (D-STAR) that leverages multi-perspective thought navigation. D-STAR iteratively optimizes a question generator and an entity retriever by navigating thoughts on a dynamic graph reasoning across multiple perspectives for question generation. The generated question–answer pairs, along with hard negatives shared in the graph, enable adaptation with minimal computational overhead. Additionally, we introduce a new task, source-free entity linking, focusing on unsupervised transfer learning without direct access to original domain data. To demonstrate the feasibility of this task, we provide a generated question–answering dataset, FandomWiki , for novel entities. Our experiments show that D-STAR significantly improves baselines on SciFact, Zeshel, and FandomWiki. • Adapting a pretrained entity linking model for entity queries of emerging domains. • Dynamically prompting a large language model with multiperspective thought navigation algorithm. • Harnessing topological data to mine hard negatives in contrastive learning. • Conceptualizing source-free entity linking. [ABSTRACT FROM AUTHOR]

Entity linking is an important task for information retrieval and knowledge graph construction. Most existing methods use a bi-encoder structure to encode mentions and entities in the same space, and learn contextual features for entity linking. However, this type of system still faces some problems: (1) the entity embedding part of the model only learns from the local context of the target entity, which is too unique for entity linking model to learn the context commonality of information; (2) the entity disambiguation part only uses similarity calculation once to determine the target entity, resulting in insufficient interaction between the mentions and candidate entities, and ineffective recall of real entities. We propose a new entity linking model based on graph neural network. Different from other bi-encoder retrieval systems, this paper introduces a fine-grained semantic enhancement information into the entity embedding part of the bi-encoder to reduce the specificity of the model. Then, the cross-attention encoder is used to re-rank the target mention and each candidate entity after the entity retrieval model. Experimental results show that although the model is not optimal in inference speed, it outperforms all baseline methods on the AIDA-CoNLL dataset, and has good generalization effects on four datasets in different fields such as MSNBC and ACE2004. [ABSTRACT FROM AUTHOR]

Entity linking and relation linking are two crucial components in many question answering systems over knowledge graphs, which aim to identify the relevant entity or relation mentions in a question and link them to the target entity or relation in the knowledge graph. Previous studies mostly solve these two tasks independently or as sequential tasks, which usually leads to poor performance since the short texts in most questions lack the context information needed for disambiguation. In this paper, we propose an approach to jointly perform entity linking and relation linking. The idea is to exploit both the independent and joint features of the candidates for disambiguation, which captures different characteristics when the knowledge graph information and the semantics of the question are both considered. We evaluated our approach on the QALD-7 and LC-QuAD datasets and the experimental results shows that our approach significantly outperforms the existing entity and relation linking approaches. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) is a field of study that focuses on data analysis on texts with certain methods. NLP includes tasks such as sentiment analysis, spam detection, entity linking, and question answering, to name a few. Entity linking is an NLP task that is used to map mentions specified in the text to the entities of a Knowledge Base. In this study, we analysed the efficacy of bi‐encoder entity linking models for multilingual biomedical texts. Using surface‐based, approximate nearest neighbour search and embedding approaches during the candidate generation phase, accuracy, and recall values were measured on language representation models such as BERT, SapBERT, BioBERT, and RoBERTa according to language and domain. The proposed entity linking framework was analysed on the BC5CDR and Cantemist datasets for English and Spanish, respectively. The framework achieved 76.75% accuracy for the BC5CDR and 60.19% for the Cantemist. In addition, the proposed framework was compared with previous studies. The results highlight the challenges that come with domain‐specific multilingual datasets. [ABSTRACT FROM AUTHOR]

Entity linking is the task of resolving ambiguous mentions in documents to their referent entities in a knowledge graph (KG). Existing solutions mainly rely on three kinds of information: local contextual similarity, global coherence, and prior probability. But the information of the mentions' types is rarely utilized, which is helpful for precise entity linking. That is to say, if the type information of a mention is obtained from a mention classifier, we can exclude candidate entities with different types. However, the key challenge of realizing it lies in obtaining the type labels with appropriate granularity and performing entity linking with the error propagated from the mention classifier. To solve the challenges, we propose a model named type-oriented multi-task entity linking (TMTEL). First, we select types with appropriate granularity from the taxonomy of a KG, which is modeled as a nonlinear integer programming problem. Second, we use a multi-task learning framework to incorporate the selected types into entity linking. The type information is used to enhance the representation of the mentions' context, which is more robust to the errors of the mention classifier. Experimental results show that our model outperforms multiple existing solutions. [ABSTRACT FROM AUTHOR]

Over the past decade, knowledge bases (KB) have been increasingly utilized to complete and enrich the representation of queries and documents in order to improve the document retrieval task. Although many approaches have used KB for such purposes, the problem of how to effectively leverage entity-based representation still needs to be resolved. This paper proposes a Purely Entity-based Semantic Search Approach for Information Retrieval (PESS4IR) as a novel solution. The approach includes (i) its own entity linking method and (ii) an inverted indexing method, and for document retrieval and ranking, (iii) an appropriate ranking method is designed to take advantage of all the strengths of the approach. We report the findings on the performance of our approach, which is tested by queries annotated by two known entity linking tools, REL and DBpedia-Spotlight. The experiments are performed on the standard TREC 2004 Robust and MSMARCO collections. By using the REL method on the Robust collection, for the queries whose terms are all annotated and whose average annotation scores are greater than or equal to 0.75, our approach achieves the maximum nDCG@5 score (1.00). Also, it is shown that using PESS4IR alongside another document retrieval method would improve performance, unless that method alone achieves the maximum nDCG@5 score for those highly annotated queries. [ABSTRACT FROM AUTHOR]

A vast amount of human communication occurs online. These digital traces of natural human communication along with recent advances in natural language processing technology provide for computational analysis of these discussions. In the study of social networks, the typical perspective is to view users as nodes and concepts as flowing through and among the user nodes within the social network. In the present work, we take the opposite perspective: we extract and organize massive amounts of group discussion into a concept space we call an entity graph where concepts and entities are static and human communicators move about the concept space via their conversations. Framed by this perspective, we performed several experiments and comparative analysis on large volumes of online discourse from Reddit. In quantitative experiments, we found that discourse was difficult to predict, especially as the conversation carried on. We also developed an interactive tool to visually inspect conversation trails over the entity graph; although they were difficult to predict, we found that conversations, in general, tended to diverge to a vast swath of topics initially, but then tended to converge to simple and popular concepts as the conversation progressed. An application of the spreading activation function from the field of cognitive psychology also provided compelling visual narratives from the data. [ABSTRACT FROM AUTHOR]

Copyright of Procesamiento del Lenguaje Natural is the property of Sociedad Espanola para el Procesamiento del Lenguaje Natural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A table is a convenient way to store, structure, and present data. Tables are an attractive knowledge source in various applications, including knowledge graph engineering. However, a lack of understanding of the semantic structure and meaning of their content may reduce the effectiveness of this process. Hence, the restoration of tabular semantics and the development of knowledge graphs based on semantically annotated tabular data are highly relevant tasks that have attracted a lot of attention in recent years. We propose a hybrid approach using heuristics and machine learning methods for the semantic annotation of relational tabular data and knowledge graph populations with specific entities extracted from the annotated tables. This paper discusses the main stages of the approach, its implementation, and performance testing. We also consider three case studies for the development of domain-specific knowledge graphs in the fields of industrial safety inspection, labor market analysis, and university activities. The evaluation results revealed that the application of our approach can be considered the initial stage for the rapid filling of domain-specific knowledge graphs based on tabular data. [ABSTRACT FROM AUTHOR]

This paper examines different methods of predicting bibliographical details (e.g. author, title, and publisher) of books under review in a corpus of approximately 1,100 historical book reviews. The dataset is comprised of book reviews from ProQuest’s American Periodicals Series (APS). This kind of bibliographical profiling is often characterized as a Natural Language Processing (NLP) or Named Entity Recognition (NER) task, but it can be more specifically described as a two-part Named Entity Linking (NEL) task, beginning with a feature extraction stage followed by one of several available matching or classification methods. An attempt has been made to formalize constraints for modular bibliographical profiling (MBP) and shed light on some important choices that are often glossed over or obscured by digital humanities practitioners. Applying these constraints, the paper evaluates combinations of feature selection (naive bag-of-words [BOW], rule-based feature extraction, and NER using a pre-trained model) with a standardized similarity-based matching strategy (cosine similarity). All tasks are performed on derived text data (term frequency tables), so that data can be shared and all methods can be used on materials available only in non-consumptive formats. These comparisons suggest that naive BOW can perform quite robustly, and that using even a basic pre-trained NER model in conjunction with a BOW approach may reduce false positives.

Entity linking, a crucial task in the realm of natural language processing, aims to link entity mentions in a text to their corresponding entities in the knowledge base. While long documents provide abundant contextual information, facilitating feature extraction for entity identification and disambiguation, entity linking in Chinese short texts presents significant challenges. This study introduces an innovative approach to entity linking within Chinese short texts, combining multiple embedding representations. It integrates embedding representations from both entities and relations in the knowledge graph triples, as well as embedding representations from the descriptive text of entities and relations, to enhance the performance of entity linking. The method also incorporates external semantic supplements to strengthen the model's feature learning capabilities. The Multi-Embedding Representation–Bidirectional Encoder Representation from Transformers–Bidirectional Gated Recurrent Unit (MER-BERT-BiGRU) neural network model is employed for embedding learning. The precision, recall, and F1 scores reached 89.73%, 92.18%, and 90.94% respectively, demonstrating the effectiveness of our approach. [ABSTRACT FROM AUTHOR]