Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases (2022)

Abstract: Automatic extraction of funding information from academic articles adds significant value to industry and research communities, including tracking research outcomes by funding organizations, profiling researchers and universities based on the received funding, and supporting open access policies. Two major challenges of identifying and linking funding entities are: (i) sparse graph structure of the Knowledge Base (KB), which makes the commonly used graph-based entity linking approaches suboptimal for the funding domain, (ii) missing entities in KB, which (unlike recent zero-shot approaches) requires marking entity mentions without KB entries as NIL. We propose an entity linking model that can perform NIL prediction and overcome data scarcity issues in a time and data-efficient manner. Our model builds on a transformer-based mention detection and a bi-encoder model to perform entity linking. We show that our model outperforms strong existing baselines.

## introduction entity linking (el) aims to annotate text with corresponding entity identifiers from a knowledge base (kb) and is a building block for different tasks, such as document ranking @xcite , entity retrieval @xcite , and question understanding in conversations @xcite . recent years have witnessed the flourishing of entity linking approaches for zeroshot @xcite and opendomain setups @xcite . while zero-shot entity linking can generalize to new specialized domains and entity dictionaries, existing approaches cannot perform nil prediction; i.e., identifying entity mentions without a target entity in a knowledge base and assigning them to nil. open-domain entity linkers, on the other hand, build on the availability of rich entity relations and descriptions in kbs. this makes ex-isting el approaches suboptimal for real-world applications of entity linking in domains with incomplete knowledge bases, where both in-kb and out-of-kb entities should be identified. in this paper, we aim to address entity linking in the funding domain @xcite , which is essential for funding organizations to track the outcome of the research they funded @xcite and also helps to comply with open access rules @xcite . knowledge bases of funding organizations, either proprietary or open access (e.g., the funding kb crossref foot_0 ), contain brief information about entities (e.g., official name and acronym). they also have extremely sparse graph structure with large amount of missing entities that need to be found from research articles. this implies that el in the funding domain requires detecting mentions with out-of-kb entities while handling sparse entity relations and descriptions. the approach, should also be able to operate with limited training data, as large public datasets are rarely available for domain-specific applications. we propose a two-step el approach, where we first identify entity mentions using task adaptive pre-training @xcite of bert @xcite and then perform entity disambiguation (ed) by utilizing a bi-encoder model to learn dense entity and mention representations. our bi-encoder model and training approach using negative sampling are specifically designed to operate with out-of-kb entities. the bi-encoder is followed by a modest feature-based model to map the entities to an entity in kb or nil. we create two new datasets for el in the funding domain and compare our mention detection and ed approaches with strong neural and feature-based models. we show that our model improves over existing baselines for both entity disambiguation and end-to-end entity linking. mention encoder w element-wise multiplication f(m, e) national science [me] foundation for their [sep] support. [cls] we [ms] thank [ea] nsf united [eb] states [sep] [cls] national foundation science cls entity encoder cls entity/nil selection top-k entities lp(m), simi(e, m) <"national science foundation", @xmath0, our contributions include: (i) proposing a data-efficient model for entity linking (with nil prediction) in funding domain that is efficient and can be used with modest computational power, (ii) improving upon existing el approaches for funding organization, and (iii) releasing new training and evaluation datasets for entity linking in funding domain. to our knowledge, this is the first and largest publicly available dataset for entity linking in funding domain. the code and datasets created in this paper are made publicly available. 2 2 method in this section, we provide a formal definition of the task, followed by the description of our mention detection (md) approach and funding entity disambiguation model, referred to as fund. ## results mention detection entity disambiguation entity linking putting the pieces together, we show the results of end-to-end entity linking on test set of elfund dataset in efficiency finally, we measure the run time of ed model by running it on a random sample of 100 sentences with 306 mentions. we pass 12 candidate entities to both gbm f 26 and fund and measure the run time in seconds. the experiment is repeated 10 times on a machine with an intel xeon e-2276m (2.80ghz, 32gb ram) cpu and an nvidia quadro t1000 gpu with 4gb memory. ## conclusions in this paper, we have introduced an entity linking method for funding domain, where the knowledge base has sparse graph structure and limited information is available about entities. the model builds on bert to perform mention detection, and a bi-encoder model to conduct the entity disambiguation. we compared our method to strong feature-based and zero-shot models and showed that our model can perform nil assignments and overcome data scarcity issues more efficient and effective than comparable baselines. as future work, we would like to explore the benefit of employing contrastive learning for the highly ambiguous entity mentions, which could provide further robustness to extracting and linking such entities in scientific texts that span across all sciences.