The number of published research papers has experienced exponential growth in
recent years, which makes it crucial to develop new methods for efficient and
versatile information extraction and knowledge discovery. To address this need,
we propose a Semantic Knowledge Graph (SKG) that integrates semantic concepts
from abstracts and other meta-information to represent the corpus. The SKG can
support various semantic queries in academic literature thanks to the high
diversity and rich information content stored within. To extract knowledge from
unstructured text, we develop a Knowledge Extraction Module that includes a
semi-supervised pipeline for entity extraction and entity normalization. We
also create an ontology to integrate the concepts with other meta information,
enabling us to build the SKG. Furthermore, we design and develop a dataflow
system that demonstrates how to conduct various semantic queries flexibly and
interactively over the SKG. To demonstrate the effectiveness of our approach,
we conduct the research based on the visualization literature and provide
real-world use cases to show the usefulness of the SKG.
The dataset and codes for this work are available at
this https URL

本文提出语义知识图谱（SKG）来集成抽象和其他元信息中的语义概念以表示语料库，并利用半监督流程进行实体抽取和实体标准化，以及设计了一个数据流系统来展示如何灵活和交互地进行各种语义查询。最后，在可视化文献的基础上进行研究并提供现实世界的用例来展示 SKG 的有用性。

SKG: 一种用语义知识图谱对学术论文进行信息检索和分析的通用框架

SKG: A Versatile Information Retrieval and Analysis Framework for  Academic Papers with Semantic Knowledge Graphs

We present an overview of the design and first proof-of-concept
implementation for AIDA, an autonomous intelligent developer agent that
develops software from scratch. AIDA takes a software requirements
specification and uses reasoning over a semantic knowledge graph to interpret
the requirements, then designs and writes software to satisfy them. AIDA uses
both declarative and procedural knowledge in the core domains of data,
algorithms, and code, plus some general knowledge. The reasoning codebase uses
this knowledge to identify needed components, then designs and builds the
necessary information structures around them that become the software. These
structures, the motivating requirements, and the resulting source code itself
are all new knowledge that are added to the knowledge graph, becoming available
for future reasoning. In this way, AIDA also learns as she writes code and
becomes more efficient when writing subsequent code.

AIDA 是一种自主智能开发代理人，利用语义知识图推理软件需求规范，生成满足要求的代码并且随着写代码学习和提高效率。