Researcher Representations Based on Aggregating Embeddings of Publication Titles: A Case Study in a J apanese Academic Database (2024)

Abstract: Constructing researcher representations is crucial for search and recommendation in academic databases. While recent studies presented methods based on knowledge graph embeddings, obtaining a complete graph of academic entities might be sometimes challenging due to the lack of linked data.By contrast, the textual list of publications of each researcher, which represents their research interests and expertise, is usually easy to obtain.Therefore, this study focuses on creating researcher representations based on textual embeddings of their publication titles and assesses their practicality. We aggregate embeddings of each researcher’s multiple publications into a single vector and apply it to research field classification and similar researcher search tasks. We experimented with multiple language models and embedding aggregation methods to compare their performance.From the model perspective, we confirmed the effectiveness of using sentence embedding models and a simple averaging approach.

## introduction academic databases equipped with search and recommendation functions have supported researchers' activities. traditional search approaches primarily involve users inputting specific keywords and then retrieving corresponding papers and authors @xcite . however, in actual research scenarios, researchers often need to efficiently find papers to read based on their previous interests or discover individuals in other fields with similar research interests. to address these requirements, methods for constructing researcher representations in a feature space have been actively studied in recent years @xcite @xcite . in particular, with the development of academic graphs such as microsoft academic graph (mag) 1 , there have been presented several methods based on knowledge graph embeddings to construct researchers' vector representations @xcite . however, these studies assume well-organized graph databases as input resources, making it difficult to apply these methods to databases lacking sufficient linked data. for example, grant-in-aid for scientific research database (kaken), one of typical academic databases in japan, comprises records of research accomplishments of grant projects but does not link them to their authors and presentation venues. using such databases would first require data linking to construct a graph structure before applying graph embedding methods. compared with obtaining a complete graph structure, the textual list of publications of each researcher is more accessible. recent development of language models pre-trained using large-scale text data @xcite @xcite has significantly improved several retrieval and recommendation tasks, and academicspecific language models were released for certain languages @xcite @xcite . under such background, this study attempts to construct researcher representations solely from the textual publication information using pre-trained language models. the usefulness of these representations is then examined through experiments on two practical application tasks: research field classification and similar researcher search. ## related work researcher representations have been studied using a variety of methods, such as topic models @xcite and document embeddings @xcite . recent methods focused on the graph structure of academic entities and calculated the node features using graph embedding methods. mag was a typical example of a large-scale academic graph, and its successor project, openalex @xcite , has recently been used. @xcite constructed an rdf-based knowledge graph platform called semopenalex and experimentally evaluated the embedding methods for various nodes such as researchers and papers on the graph. such knowledge graph-derived researcher representations are used to solve academic tasks such as author name disambiguation @xcite . on the other hand, the newly emerging pretrained language models have substantially promoted the development of text processing for information retrieval and recommendation, and we can expect their effectiveness on researcher embeddings. it is known that a certain domain task often benefits from a language model pre-trained using text in the target domain. however, in scientific domains, we observed cases where general domain models are sufficient @xcite . taking into account the above matters, this paper includes an experiment that compares the performance between general language models and domain-specific models in a single language embedding setting. ## methodology our method aims to construct a researcher's representation using a comprehensive list of publication titles related to their research grant project. since most researchers in japan use both english and japanese languages to present their research, we investigate in experiments whether we should embed both the two languages' text to calculate the researchers' representations. ## experiments we used the kaken api foot_2 to collect kaken projects as of october 2023 and the publication titles linked to the projects' members. we explain details about the kaken api in appendix a. to verify the effectiveness of the constructed researcher representations, we conducted two practical application tasks: research field classification (see section 4.1) and similar researcher search (see section 4.2). since there is no multilingual pretrained model in the scientific domain, we also investigate the performance of domain-specific models in single language embedding setting (see section 4.3). ## conclusion in this paper, we focused on the titles of researchers' publications in academic databases and constructed researcher representations by aggregating their text embeddings. we then evaluated the performance of these representations in two practical tasks. the results of the experiments showed that the performance of each task improved with sentence embedding models. additionally, inputting publica-tions in multiple languages was more effective than in a single language. however, there were practical challenges with domain-specific models, as general-domain models outperformed them. these results suggest the need for the development of new multilingual models that are robust in sentence representation and have domain knowledge. researcher id name affiliation research projects id title research category members keywords publications id title authors (unparsed) organizer (unparsed) publication date figure 1: researcher data that can be obtained using the kaken api. zhe zheng, xin-zheng lu, ke-yin chen, yu-cheng zhou, and jia-rui lin. 2022. pretrained domainspecific language model for general information retrieval tasks in the aec domain. arxiv preprint arxiv:2203.04729.