Math-KG: Construction and Applications of Mathematical Knowledge Graph
- URL: http://arxiv.org/abs/2205.03772v1
- Date: Sun, 8 May 2022 03:39:07 GMT
- Title: Math-KG: Construction and Applications of Mathematical Knowledge Graph
- Authors: Jianing Wang
- Abstract summary: We propose a mathematical knowledge graph named Math-KG, which automatically constructed by the pipeline method with the natural language processing technology to integrate the resources of the mathematics.
We implement a simple application system to validate the proposed Math-KG can make contributions on a series of scenes, including faults analysis and semantic search.
- Score: 2.1828601975620257
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, the explosion of online education platforms makes a success in
encouraging us to easily access online education resources. However, most of
them ignore the integration of massive unstructured information, which
inevitably brings the problem of \textit{information overload} and
\textit{knowledge trek}. In this paper, we proposed a mathematical knowledge
graph named Math-KG, which automatically constructed by the pipeline method
with the natural language processing technology to integrate the resources of
the mathematics. It is built from the corpora of Baidu Baike, Wikipedia. We
implement a simple application system to validate the proposed Math-KG can make
contributions on a series of scenes, including faults analysis and semantic
search. The system is publicly available at GitHub
\footnote{\url{https://github.com/wjn1996/Mathematical-Knowledge-Entity-Recognition}.}.
Related papers
- Automated conjecturing in mathematics with \emph{TxGraffiti} [0.0]
emphTxGraffiti is a data-driven computer program developed to automate the process of generating conjectures.
We present the design and core principles of emphTxGraffiti, including its roots in the original emphGraffiti program.
arXiv Detail & Related papers (2024-09-28T15:06:31Z) - iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models [0.7165255458140439]
iText2KG is a method for incremental, topic-independent Knowledge Graph construction without post-processing.
Our method demonstrates superior performance compared to baseline methods across three scenarios.
arXiv Detail & Related papers (2024-09-05T06:49:14Z) - State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
Graph-level learning has been applied to many tasks including comparison, regression, classification, and more.
Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures.
Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
arXiv Detail & Related papers (2023-01-14T09:15:49Z) - Joint Language Semantic and Structure Embedding for Knowledge Graph
Completion [66.15933600765835]
We propose to jointly embed the semantics in the natural language description of the knowledge triplets with their structure information.
Our method embeds knowledge graphs for the completion task via fine-tuning pre-trained language models.
Our experiments on a variety of knowledge graph benchmarks have demonstrated the state-of-the-art performance of our method.
arXiv Detail & Related papers (2022-09-19T02:41:02Z) - JiuZhang: A Chinese Pre-trained Language Model for Mathematical Problem
Understanding [74.12405417718054]
This paper aims to advance the mathematical intelligence of machines by presenting the first Chinese mathematical pre-trained language model(PLM)
Unlike other standard NLP tasks, mathematical texts are difficult to understand, since they involve mathematical terminology, symbols and formulas in the problem statement.
We design a novel curriculum pre-training approach for improving the learning of mathematical PLMs, consisting of both basic and advanced courses.
arXiv Detail & Related papers (2022-06-13T17:03:52Z) - Knowledgebra: An Algebraic Learning Framework for Knowledge Graph [15.235089177507897]
Knowledge graph (KG) representation learning aims to encode entities and relations into dense continuous vector spaces such that knowledge contained in a dataset could be consistently represented.
We developed a mathematical language for KG based on an observation of their inherent algebraic structure, which we termed as Knowledgebra.
We implemented an instantiation model, SemE, using simple matrix semigroups, which exhibits state-of-the-art performance on standard datasets.
arXiv Detail & Related papers (2022-04-15T04:53:47Z) - Inter-GPS: Interpretable Geometry Problem Solving with Formal Language
and Symbolic Reasoning [123.06420835072225]
We construct a new large-scale benchmark, Geometry3K, consisting of 3,002 geometry problems with dense annotation in formal language.
We propose a novel geometry solving approach with formal language and symbolic reasoning, called Interpretable Geometry Problem solver (Inter-GPS)
Inter-GPS incorporates theorem knowledge as conditional rules and performs symbolic reasoning step by step.
arXiv Detail & Related papers (2021-05-10T07:46:55Z) - Distilling Wikipedia mathematical knowledge into neural network models [4.874780144224057]
We introduce a pipeline for distilling mathematical expressions embedded in Wikipedia into symbolic encodings to be used in downstream machine learning tasks.
We demonstrate that a $textitmathematical$ $textitlanguage$ $textitmodel$ trained on this "corpus" of expressions can be used as a prior to improve the performance of neural-guided search for the task of symbolic regression.
arXiv Detail & Related papers (2021-04-13T04:16:50Z) - JAKET: Joint Pre-training of Knowledge Graph and Language Understanding [73.43768772121985]
We propose a novel joint pre-training framework, JAKET, to model both the knowledge graph and language.
The knowledge module and language module provide essential information to mutually assist each other.
Our design enables the pre-trained model to easily adapt to unseen knowledge graphs in new domains.
arXiv Detail & Related papers (2020-10-02T05:53:36Z) - 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.