MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities

• URL: http://arxiv.org/abs/2408.04661v1
• Date: Mon, 5 Aug 2024 21:42:59 GMT
• Title: MaterioMiner -- An ontology-based text mining dataset for extraction of process-structure-property entities
• Authors: Ali Riza Durmaz, Akhil Thomas, Lokesh Mishra, Rachana Niranjan Murthy, Thomas Straub,
• Abstract summary: We present the MaterioMiner dataset and the materials ontology where ontological concepts are associated with textual entities. We explore the consistency between the three raters and perform fine-process-trained models to showcase the feasibility of named-process recognition model training.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: While large language models learn sound statistical representations of the language and information therein, ontologies are symbolic knowledge representations that can complement the former ideally. Research at this critical intersection relies on datasets that intertwine ontologies and text corpora to enable training and comprehensive benchmarking of neurosymbolic models. We present the MaterioMiner dataset and the linked materials mechanics ontology where ontological concepts from the mechanics of materials domain are associated with textual entities within the literature corpus. Another distinctive feature of the dataset is its eminently fine-granular annotation. Specifically, 179 distinct classes are manually annotated by three raters within four publications, amounting to a total of 2191 entities that were annotated and curated. Conceptual work is presented for the symbolic representation of causal composition-process-microstructure-property relationships. We explore the annotation consistency between the three raters and perform fine-tuning of pre-trained models to showcase the feasibility of named-entity recognition model training. Reusing the dataset can foster training and benchmarking of materials language models, automated ontology construction, and knowledge graph generation from textual data.

Related papers

• From Tokens to Materials: Leveraging Language Models for Scientific Discovery [12.211984932142537]
  This study investigates the application of language model embeddings to enhance material property prediction in materials science. We demonstrate that domain-specific models, particularly MatBERT, significantly outperform general-purpose models in extracting implicit knowledge from compound names and material properties.
  arXiv  Detail & Related papers  (2024-10-21T16:31:23Z)
• MatText: Do Language Models Need More than Text & Scale for Materials Modeling? [5.561723952524538]
  MatText is a suite of benchmarking tools and datasets designed to systematically evaluate the performance of language models in modeling materials. MatText provides essential tools for training and benchmarking the performance of language models in the context of materials science.
  arXiv  Detail & Related papers  (2024-06-25T05:45:07Z)
• Visual Analytics for Fine-grained Text Classification Models and Datasets [3.6873612681664016]
  SemLa is a novel visual analytics system tailored for fine-grained text classification. This paper details the iterative design study and the resulting innovations featured in SemLa.
  arXiv  Detail & Related papers  (2024-03-21T17:26:28Z)
• Exploiting Contextual Target Attributes for Target Sentiment Classification [53.30511968323911]
  Existing PTLM-based models for TSC can be categorized into two groups: 1) fine-tuning-based models that adopt PTLM as the context encoder; 2) prompting-based models that transfer the classification task to the text/word generation task. We present a new perspective of leveraging PTLM for TSC: simultaneously leveraging the merits of both language modeling and explicit target-context interactions via contextual target attributes.
  arXiv  Detail & Related papers  (2023-12-21T11:45:28Z)
• How Well Do Text Embedding Models Understand Syntax? [50.440590035493074]
  The ability of text embedding models to generalize across a wide range of syntactic contexts remains under-explored. Our findings reveal that existing text embedding models have not sufficiently addressed these syntactic understanding challenges. We propose strategies to augment the generalization ability of text embedding models in diverse syntactic scenarios.
  arXiv  Detail & Related papers  (2023-11-14T08:51:00Z)
• Leveraging Contextual Information for Effective Entity Salience Detection [21.30389576465761]
  We show that fine-tuning medium-sized language models with a cross-encoder style architecture yields substantial performance gains over feature engineering approaches. We also show that zero-shot prompting of instruction-tuned language models yields inferior results, indicating the task's uniqueness and complexity.
  arXiv  Detail & Related papers  (2023-09-14T19:04:40Z)
• Topics as Entity Clusters: Entity-based Topics from Large Language Models and Graph Neural Networks [0.6486052012623045]
  We propose a novel topic clustering approach using bimodal vector representations of entities. Our approach is better suited to working with entities in comparison to state-of-the-art models.
  arXiv  Detail & Related papers  (2023-01-06T10:54:54Z)
• An Empirical Investigation of Commonsense Self-Supervision with Knowledge Graphs [67.23285413610243]
  Self-supervision based on the information extracted from large knowledge graphs has been shown to improve the generalization of language models. We study the effect of knowledge sampling strategies and sizes that can be used to generate synthetic data for adapting language models.
  arXiv  Detail & Related papers  (2022-05-21T19:49:04Z)
• Unified Graph Structured Models for Video Understanding [93.72081456202672]
  We propose a message passing graph neural network that explicitly models relational-temporal relations. We show how our method is able to more effectively model relationships between relevant entities in the scene.
  arXiv  Detail & Related papers  (2021-03-29T14:37:35Z)
• Prototypical Representation Learning for Relation Extraction [56.501332067073065]
  This paper aims to learn predictive, interpretable, and robust relation representations from distantly-labeled data. We learn prototypes for each relation from contextual information to best explore the intrinsic semantics of relations. Results on several relation learning tasks show that our model significantly outperforms the previous state-of-the-art relational models.
  arXiv  Detail & Related papers  (2021-03-22T08:11:43Z)
• Exploiting Structured Knowledge in Text via Graph-Guided Representation Learning [73.0598186896953]
  We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon entity-level masked language models, our first contribution is an entity masking scheme. In contrast to existing paradigms, our approach uses knowledge graphs implicitly, only during pre-training.
  arXiv  Detail & Related papers  (2020-04-29T14:22:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.