
 In the era of big data and global biodiversity decline, there is a pressing need to transform data and information into findable and actionable knowledge. We propose a conceptual classification scheme for invasion science that goes beyond hypothesis networks and allows to organize publications and data sets, guide research directions, and identify knowledge gaps. Combining expert knowledge with literature analysis, we identified five major research themes in this field: introduction pathways, invasion success and invasibility, impacts of invasion, managing biological invasions, and meta-invasion science. We divided these themes into 10 broader research questions and linked them to 39 major hypotheses forming the theoretical foundation of invasion science. As artificial intelligence advances, such classification schemes will become important references for organizing scientific information. Our approach can be extended to other research fields, fostering cross-disciplinary connections to leverage the scientific knowledge needed to address Anthropocene challenges.

A conceptual classification scheme of invasion science

With the exponential increase in scientific publications, new conceptual and technological tools are needed to help scientists, students, managers and policy-makers to navigate and digest current scientific knowledge. Hi Knowledge is an initiative to synthesise and visualise scientific knowledge, with an initial focus on invasion biology that is currently expanding to include urban ecology, restoration ecology and freshwater ecology. In a workshop on 5-6 June 2023 in Berlin, Germany, we discussed and tested a collection of new open tools related to this initiative in order to publish, curate, explore and synthesise concepts and results in ecology. Three main themes were discussed during in-person breakout group sessions: (1) building and using open tools for knowledge curation, exploration and synthesis; (2) making open knowledge searchable and machine friendly by improving modelling and annotation of scientific knowledge; and (3) extending beyond the field of invasion biology. We report on the discussions of all twelve sessions pertaining to these themes. A main underlying goal of our workshop was to build a community of scientists involved in openly co-designing and using these tools. Overall, the participants were enthusiastic about the usefulness of these tools and discussions gravitated around improving them and finding strategies to scale-up participation by the community. Follow-up user tests and publications are planned for individual tools and topics.

Building an atlas of knowledge for invasion biology and beyond! 2nd enKORE-INAS Workshop

We investigate the problem of identifying the major hypothesis that is addressed in a scientific paper. To this end, we present a dataset from the domain of invasion biology that organizes a set of 954 papers into a network of fine-grained domain-specific categories of hypotheses. We carry out experiments on classifying abstracts according to these categories and present a pilot study on annotating hypothesis statements within the text. We find that hypothesis statements in our dataset are complex, varied and more or less explicit, and, importantly, spread over the whole abstract. Experiments with BERT-based classifiers show that these models are able to classify complex hypothesis statements to some extent, without being trained on sentence-level text span annotations.

Linking a Hypothesis Network From the Domain of Invasion Biology to a Corpus of Scientific Abstracts: The INAS Dataset

pdf/enhancing-domain-specific-encoder-models-with-llm-generated-j10e79wi54c9.pdf

Enhancing Domain-Specific Encoder Models with LLM-Generated Data: How to Leverage Ontologies, and How to Do Without Them

We introduce SemCSE, an unsupervised method for learning semantic embeddings of scientific texts. Building on recent advances in contrastive learning for text embeddings, our approach leverages LLM-generated summaries of scientific abstracts to train a model that positions semantically related summaries closer together in the embedding space. This resulting objective ensures that the model captures the true semantic content of a text, in contrast to traditional citation-based approaches that do not necessarily reflect semantic similarity. To validate this, we propose a novel benchmark designed to assess a model's ability to understand and encode the semantic content of scientific texts, demonstrating that our method enforces a stronger semantic separation within the embedding space. Additionally, we evaluate SemCSE on the comprehensive SciRepEval benchmark for scientific text embeddings, where it achieves state-of-the-art performance among models of its size, thus highlighting the benefits of a semantically focused training approach.

SemCSE: Semantic Contrastive Sentence Embeddings Using LLM-Generated Summaries For Scientific Abstracts

pdf/efficient-scientific-full-text-classification-the-case-of-juye2xo5q4ig.pdf

Efficient Scientific Full Text Classification: The Case of EICAT Impact Assessments

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