[incubator-hugegraph-doc] branch master updated: update README.md (#208)

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     new 0fde5cb7 update README.md (#208)
0fde5cb7 is described below

commit 0fde5cb7a51565145a2c76638d1b082ab5ce49b2
Author: John Whelan <Wh...@users.noreply.github.com>
AuthorDate: Tue May 2 23:18:32 2023 -0500

    update README.md (#208)
    
    I made some slight grammatical fixes and removed a redundant paragraph.
---
 content/en/docs/introduction/README.md | 20 +++++++++-----------
 1 file changed, 9 insertions(+), 11 deletions(-)

diff --git a/content/en/docs/introduction/README.md b/content/en/docs/introduction/README.md
index fd0c1e93..ca6eeef5 100644
--- a/content/en/docs/introduction/README.md
+++ b/content/en/docs/introduction/README.md
@@ -8,12 +8,10 @@ weight: 1
 
 HugeGraph is an easy-to-use, efficient, general-purpose open source graph database system(Graph Database, [GitHub project address](https://github.com/hugegraph/hugegraph)),
 implemented the [Apache TinkerPop3](https://tinkerpop.apache.org) framework and is fully compatible with the [Gremlin](https://tinkerpop.apache.org/gremlin.html) query language,
-With complete toolchain components, it helps users to easily build applications and products based on graph databases. HugeGraph supports fast import of more than 10 billion vertices and edges, and provides millisecond-level relational query capability (OLTP). 
+With complete toolchain components, it helps users easily build applications and products based on graph databases. HugeGraph supports fast import of more than 10 billion vertices and edges, and provides millisecond-level relational query capability (OLTP). 
 It supports large-scale distributed graph computing (OLAP).
 
-Typical application scenarios of HugeGraph include deep relationship exploration, association analysis, path search, feature extraction, data clustering, community detection, knowledge graph, etc., and are applicable to business fields such as network security, telecommunication fraud, financial risk control, advertising recommendation, social network and intelligence Robots etc.
-
-Typical application scenarios of HugeGraph include deep relationship exploration, association analysis, path search, feature extraction, data clustering, community detection, knowledge graph, etc., and are applicable to business fields such as network security, telecommunication fraud, financial risk control, advertising recommendation, social network and intelligence Robots etc.
+Typical application scenarios of HugeGraph include deep relationship exploration, association analysis, path search, feature extraction, data clustering, community detection, knowledge graph, etc., and are applicable to business fields such as network security, telecommunication fraud, financial risk control, advertising recommendation, social network, and intelligence Robots, etc.
 
 ### Features
 
@@ -24,14 +22,14 @@ This system has the following features:
 
 - Ease of use: HugeGraph supports Gremlin graph query language and RESTful API, provides common interfaces for graph retrieval, and has peripheral tools with complete functions to easily implement various graph-based query and analysis operations.
 - Efficiency: HugeGraph has been deeply optimized in graph storage and graph computing, and provides a variety of batch import tools, which can easily complete the rapid import of tens of billions of data, and achieve millisecond-level response for graph retrieval through optimized queries. Supports simultaneous online real-time operations of thousands of users.
-- Universal: HugeGraph supports the Apache Gremlin standard graph query language and the Property Graph standard graph modeling method, and supports graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big data platform.
-- Scalable: supports distributed storage, multiple copies of data and horizontal expansion, built-in multiple back-end storage engines, and can easily expand the back-end storage engine through plug-ins.
-- Open: HugeGraph code is open source (Apache 2 License), customers can modify and customize independently, and selectively give back to the open source community.
+- Universal: HugeGraph supports the Apache Gremlin standard graph query language and the Property Graph standard graph modeling method, and supports graph-based OLTP and OLAP schemes. Integrate Apache Hadoop and Apache Spark big data platforms.
+- Scalable: supports distributed storage, multiple copies of data, and horizontal expansion, built-in multiple back-end storage engines, and can easily expand the back-end storage engine through plug-ins.
+- Open: HugeGraph code is open source (Apache 2 License), customers can modify and customize independently, and selectively give back to the open-source community.
 
 The functions of this system include but are not limited to: 
 
-- Supports batch import of data from multiple data sources (including local files, HDFS files, MySQL databases and other data sources), and supports import of multiple file formats (including TXT, CSV, JSON and other formats)
-- With a visual operation interface, it can be used for operation, analysis and display diagrams, reducing the threshold for users to use
+- Supports batch import of data from multiple data sources (including local files, HDFS files, MySQL databases, and other data sources), and supports import of multiple file formats (including TXT, CSV, JSON, and other formats)
+- With a visual operation interface, it can be used for operation, analysis, and display diagrams, reducing the threshold for users to use
 - Optimized graph interface: shortest path (Shortest Path), K-step connected subgraph (K-neighbor), K-step to reach the adjacent point (K-out), personalized recommendation algorithm PersonalRank, etc.
 - Implemented based on Apache TinkerPop3 framework, supports Gremlin graph query language
 - Support attribute graph, attributes can be added to vertices and edges, and support rich attribute types
@@ -46,11 +44,11 @@ The functions of this system include but are not limited to:
 
 - [HugeGraph-Server](/docs/quickstart/hugegraph-server): HugeGraph-Server is the core part of the HugeGraph project, including submodules such as Core, Backend, and API;
   - Core: Graph engine implementation, connecting the Backend module downward and supporting the API module upward;
-  - Backend: Realize the storage of graph data to the backend. The supported backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL and PostgreSQL. Users can choose one according to the actual situation;
+  - Backend: Realize the storage of graph data to the backend. The supported backends include: Memory, Cassandra, ScyllaDB, RocksDB, HBase, MySQL, and PostgreSQL. Users can choose one according to the actual situation;
   - API: Built-in REST Server, provides RESTful API to users, and is fully compatible with Gremlin query.
 - [HugeGraph-Client](/docs/quickstart/hugegraph-client): HugeGraph-Client provides a RESTful API client for connecting to HugeGraph-Server. Currently, only Java version is implemented. Users of other languages can implement it by themselves;
 - [HugeGraph-Loader](/docs/quickstart/hugegraph-loader): HugeGraph-Loader is a data import tool based on HugeGraph-Client, which converts ordinary text data into graph vertices and edges and inserts them into graph database;
-- [HugeGraph-Computer](/docs/quickstart/hugegraph-computer): HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on Kubernetes framework;
+- [HugeGraph-Computer](/docs/quickstart/hugegraph-computer): HugeGraph-Computer is a distributed graph processing system for HugeGraph (OLAP). It is an implementation of [Pregel](https://kowshik.github.io/JPregel/pregel_paper.pdf). It runs on the Kubernetes framework;
 - [HugeGraph-Hubble](/docs/quickstart/hugegraph-hubble): HugeGraph-Hubble is HugeGraph's web visualization management platform, a one-stop visual analysis platform. The platform covers the whole process from data modeling, to rapid data import, to online and offline analysis of data, and unified management of graphs;
 - [HugeGraph-Tools](/docs/quickstart/hugegraph-tools): HugeGraph-Tools is HugeGraph's deployment and management tools, including functions such as managing graphs, backup/restore, Gremlin execution, etc.