Scientific publications, primarily digitized as PDFs, remain static and unstructured, limiting the accessibility and reusability of the contained knowledge. At best, scientific knowledge from publications is provided in tabular formats, which lack semantic context. A more flexible, structured, and semantic representation is needed to make scientific knowledge understandable and processable by both humans and machines. We propose an evolution model of knowledge representation, inspired by the 5-star Linked Open Data (LOD) model, with five stages and defined criteria to guide the stepwise transition from a digital artifact, such as a PDF, to a semantic representation integrated in a knowledge graph (KG). Based on an exemplary workflow implementing the entire model, we developed a hybrid approach, called SciMantify, leveraging tabular formats of scientific knowledge, e.g., results from secondary studies, to support its evolving semantification. In the approach, humans and machines collaborate closely by performing semantic annotation tasks (SATs) and refining the results to progressively improve the semantic representation of scientific knowledge. We implemented the approach in the Open Research Knowledge Graph (ORKG), an established platform for improving the findability, accessibility, interoperability, and reusability of scientific knowledge. A preliminary user experiment showed that the approach simplifies the preprocessing of scientific knowledge, reduces the effort for the evolving semantification, and enhances the knowledge representation through better alignment with the KG structures.

The table reasoning task aims to answer the question according to the given table. Currently, using Large Language Models (LLMs) is the predominant method for table reasoning. Most existing methods employ a fixed tabular format to represent the table, which could limit the performance. Given that each instance requires different capabilities and models possess varying abilities, we assert that different instances and models suit different tabular formats. We prove the aforementioned claim through quantitative analysis of experimental results, where different instances and models achieve different performances using various tabular formats. Building on this discussion, we propose FLEXTAF-Single and FLEXTAF-Vote to enhance table reasoning performance by employing flexible tabular formats. Specifically, (i) FLEXTAF-Single trains a classifier to predict the most suitable tabular format based on the instance and the LLM. (ii) FLEXTAF-Vote integrates the results across different formats. Our experiments on WikiTableQuestions and TabFact reveal significant improvements, with average gains of 2.3% and 4.8% compared to the best performance achieved using a fixed tabular format with greedy decoding and self-consistency decoding, thereby validating the effectiveness of our methods.

You've probably heard the sayings many times: "You're only as good as your tools." These have never been truer than in the world of data science and machine learning. When I was first learning data science and machine learning, I tried a sample data science project using a data set from a recent medical study. For the study, patients came for appointments where investigators took their vital signs (weight, blood pressure, pulse, temperature, etc.) and administered some number of medical tests. The tests could create a few values or many values, depending on which tests the investigators ran on the date of the patient's appointment and depending on how long they'd been part of the study and how often they received tests.