An end-to-end entity recognition and disambiguation framework for identifying Author Affiliation from literature publications (2024)

Abstract: Author affiliation information plays a key role in bibliometric analyses and is essential for evaluating studies. However, as author affiliation information has not been standardized, which leads to difficulties such as synonym ambiguity and incomplete data during automated processing. To address the challenge, this paper proposes an end-to-end entity recognition and disambiguation framework for identifying author affiliation from literature publications. For entity disambiguation, an algorithm combining word embedding and spatial embedding is presented considering that author affiliation texts often contain rich geographic information. The disambiguation algorithm utilizes the semantic information and geographic information, which effectively enhances entity recognition and disambiguation effect. In addition, the proposed framework facilitates the effective utilization of the extensive literature in the PubMed database for comprehensive bibliometric analysis. The experimental results verify the robustness and effectiveness of the algorithm.

## introduction bibliometrics is becoming increasingly important in academia, revealing disciplinary trends and assessing the impact of research by analyzing literature data, and has become an important tool for promoting academic communication and research decision-making @xcite . in particular, assessing the contribution of research elements such as journals, countries/regions, institutions, and authors to a given field plays a key role in bibliometric analyses, helping to reveal the contribution and impact of scholarly research and guiding academic decisionmaking and resource allocation @xcite . such assessments provide an important basis for trends in disciplinary development, opportunities for collaboration and science policy development, and promote progress and innovation in academic research @xcite . the pubmed database plays a crucial role in providing rich, reliable, timely and standardized literature data for bibliometric analysis in the field of medicine in order to support quantitative analysis of quantitative features and patterns of medical research @xcite . it not only helps to improve the quality and efficiency of bibliometric analysis, but also promotes knowledge innovation and dissemination in the information. specifically, the processing of textual affiliation data often encounter incomplete information (e.g., missing country) @xcite , inconsistent representation forms (e.g., university of cambridge and cambridge university) @xcite , and abbreviations (e.g., ucla) @xcite , which may affect the results of subsequent analysis that assessed the contribution of the research elements to the field of study. author affiliation information disambiguation is essential because it concerns the use of entities such as institution, country, etc. in bibliometric analysis. the accuracy of bibliometric indicators provided by databases themselves is limited. relying solely on these data for bibliometric analysis can lead to significant inaccuracies in the indicator values. as suggested by @xcite , additional data cleaning is necessary to disambiguate affiliation data. while there are numerous studies on entity disambiguation, relatively few have explored author affiliation information, especially for pubmed databases. moreover, existing research on author affiliation information is mainly confined to institutions mainly confined to institutional entities, ignoring the identification and disambiguation of location entities (l'hôte and jeangirard, 2021). furthermore, some disambiguation methods focus only on local features and ignore the semantic information embedded in the surrounding context, which may lead to inaccuracies and limitations in the results @xcite . building upon this foundation, the paper presents an end-to-end framework designed for entity recognition and disambiguation within author affiliation text. harnessing sophisticated entity recognition techniques, the framework meticulously extracts crucial details such as institutions, countries, and more from the textual data. to tackle entity ambiguity, an innovative disambiguation algorithm, amalgamating word embedding and spatial embedding, is introduced. this algorithm adeptly resolves ambiguities by capturing contextual semantic and geographic features. specifically, word embedding endeavors to grasp the semantic relationships among entities, while spatial embedding enriches disambiguation prowess through geographical insights. by enhancing identification accuracy and robustness, this framework not only mitigates information redundancy and misidentification but also furnishes a more dependable groundwork for subsequent bibliometric analyses. overall, the contributions of this work can be summarized as follows: ## related work entity disambiguation has been extensively studied in the past. its methodological studies can be broadly categorized into three types: rule-based approaches, knowledge-based approaches, and combinations of these two approaches @xcite . the rule-based approach relied mainly on manually crafted rules. these rules could be based on the name of the entity, contextual information, or other linguistic features, such as "ibm" commonly referring to the international business machines corporation. however, these rules were often limited by their lack of generalization and scalability. with the emergence of large-scale corpora and structured knowledge bases, researchers began exploring how to fully leverage these rich resources to address entity disambiguation problems. common knowledge bases include wikipedia, dbpedia, and yago @xcite . knowledge-based approaches utilize the structured information and entity relationships within these knowledge bases, such as entity descriptions, hypernym relationships, and linking relationships, to enhance the accuracy and efficiency of entity disambiguation. meanwhile, word embedding models such as word2vec, glove, and fasttext were introduced and widely applied. these models map words to high-dimensional vector spaces and capture semantic relationships between words by learning distributed representations of words @xcite . pre-trained word embedding models can generate vector representations for entities and their contexts, encoding the semantic information of entities into positional relationships in continuous vector space. this approach enables the semantic information of entities to be more effectively utilized for entity disambiguation tasks, thereby improving the performance and effectiveness of entity disambiguation @xcite . in addition, methods such as tf-idf (term frequency-inverse document frequency), lsi (latent semantic indexing), and lda (latent dirichlet allocation) have also been introduced into entity disambiguation tasks to extract semantic information and contextual associations of entities in text @xcite . the use of a single method no longer satisfies current research needs, prompting researchers to combine different methods to obtain richer semantic information and more accurate entity representations. babelfy leverages large-scale multilingual knowledge graphs and word embedding models to achieve entity linking and disambiguation. it adopts a context-based approach by analyzing the contextual information surrounding entities in the text to determine their semantics. specifically, babelfy treats each word in the text as a potential entity mention and uses contextual information and semantic relationships in the knowledge graph to infer the most likely entity corresponding to each mention @xcite . the welink model proposed by bellatreche et al. achieves entity disambiguation by generating a list of entity candidates from knowledge graph dbpedia, and utilizing context similarity, entity coherence, relation exploitation, entity name distance, and syntactic features to compute scores and rank candidate entities @xcite . some research focuses on social media datasets, such as tweets and news headlines, which often possess characteristics of brevity and noise. however, they also contain rich spatiotemporal metadata. consequently, some researchers have begun exploring the utilization of spatiotemporal signals in the entity disambiguation process @xcite @xcite . @xcite proposed a method for entity disambiguation of weibo tweets by integrating spatiotemporal signals. considering that the dataset used in this paper contains abundant spatial signals, we drew inspiration from their work and introduced spatial signals to enhance the results of semantic similarity calculation. in this process, we reference the method proposed by @xcite that utilizes containment relationships between locations. however, unlike them, they emphasize that location heavily depends on the locations mentioned in each document, while this paper directly considers all mentioned locations equally important. ## the proposed framework this paper proposes a new framework aimed at entity recognition and disambiguation of author affiliation data extracted from pubmed-exported datasets, which can provide a reliable basis for bibliometric analysis. as shown in figure 1 , the framework can be divided into two parts: text preprocessing and entity disambiguation. in general, text preprocessing can be summarized into two main steps. the first is to extract author affiliation information, and the second is to identify entities involved in the text, such as institution, country, and other bibliometric indicators. the purpose of text preprocessing is to prepare text data to ensure usability, providing efficient data processing for subsequent entity disambiguation tasks. disambiguation is the most critical part of this framework, which addresses the issues of candidate generation and candidate ranking for ensuring that ambiguous entities are accurately disambiguated in order to determine the exact meaning and denotation.