[DOC] add logo in doc

docs/README.rst

@@ -1,89 +0,0 @@
-ABLkit
-=======
-
-.. raw:: html
-
-    <p><b>ABLkit</b> is an efficient Python toolkit for <a href="https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf"><b>Abductive Learning (ABL)</b></a>.
-    ABL is a novel paradigm that integrates machine learning and 
-    logical reasoning in a unified framework. It is suitable for tasks
-    where both data and (logical) domain knowledge are available.</p>
-
-.. image:: _static/img/ABL.png
-
-Key Features of ABLkit:
-
-- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components.
-- **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code.
-- **High-Performance**: Optimization for high accuracy and fast training speed.
-
-ABLkit encapsulates advanced ABL techniques, providing users with
-an efficient and convenient toolkit to develop dual-driven ABL systems,
-which leverage the power of both data and knowledge.
-
-.. image:: _static/img/ABLkit.png
-
-Installation
-------------
-
-Install from PyPI
-^^^^^^^^^^^^^^^^^
-
-The easiest way to install ABLkit is using ``pip``:
-
-.. code:: bash
-
-    pip install ablkit
-
-Install from Source
-^^^^^^^^^^^^^^^^^^^
-
-Alternatively, to install from source code, 
-sequentially run following commands in your terminal/command line.
-
-.. code:: bash
-
-    git clone https://github.com/AbductiveLearning/ABLkit.git
-    cd ABLkit
-    pip install -v -e .
-
-(Optional) Install SWI-Prolog
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-
-If the use of a :ref:`Prolog-based knowledge base <prolog>` is necessary, the installation of `SWI-Prolog <https://www.swi-prolog.org/>`_ is also required:
-
-For Linux users:
-
-.. code:: bash
-
-    sudo apt-get install swi-prolog
-
-For Windows and Mac users, please refer to the `SWI-Prolog Install Guide <https://github.com/yuce/pyswip/blob/master/INSTALL.md>`_.
-
-References
-----------
-
-For more information about ABL, please refer to: `Zhou, 2019 <http://scis.scichina.com/en/2019/076101.pdf>`_ 
-and `Zhou and Huang, 2022 <https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf>`_.
-
-.. code-block:: latex
-
-    @article{zhou2019abductive,
-        title     = {Abductive learning: towards bridging machine learning and logical reasoning},
-        author    = {Zhou, Zhi-Hua},
-        journal   = {Science China Information Sciences},
-        volume    = {62},
-        number    = {7},
-        pages     = {76101},
-        year      = {2019}
-    }
-
-    @incollection{zhou2022abductive,
-        title     = {Abductive Learning},
-        author    = {Zhou, Zhi-Hua and Huang, Yu-Xuan},
-        booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art},
-        editor    = {Pascal Hitzler and Md. Kamruzzaman Sarker},
-        publisher = {{IOS} Press},
-        pages     = {353--369},
-        address   = {Amsterdam},
-        year      = {2022}
-    }

docs/index.rst

@@ -1,4 +1,96 @@
-.. include:: README.rst
+ABLkit
+======
+
+.. raw:: html
+
+    <img alt="logo" class="align-right" src="_static/img/logo.png" style="width: 140px; height: 135.1px; margin-left: 20px; margin-right: 10px">
+    <p>
+    <b>ABLkit</b> is an efficient Python toolkit for <a href="https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf"><b>Abductive Learning (ABL)</b></a>.
+    </p>
+
+ABL is a novel paradigm that integrates machine learning and 
+logical reasoning in a unified framework. It is suitable for tasks
+where both data and (logical) domain knowledge are available.
+
+.. image:: _static/img/ABL.png
+
+Key Features of ABLkit:
+
+- **Great Flexibility**: Adaptable to various machine learning modules and logical reasoning components.
+- **User-Friendly**: Provide **data**, :blue-bold:`model`, and :green-bold:`KB`, and get started with just a few lines of code.
+- **High-Performance**: Optimization for high accuracy and fast training speed.
+
+ABLkit encapsulates advanced ABL techniques, providing users with
+an efficient and convenient toolkit to develop dual-driven ABL systems,
+which leverage the power of both data and knowledge.
+
+.. image:: _static/img/ABLkit.png
+
+Installation
+------------
+
+Install from PyPI
+^^^^^^^^^^^^^^^^^
+
+The easiest way to install ABLkit is using ``pip``:
+
+.. code:: bash
+
+    pip install ablkit
+
+Install from Source
+^^^^^^^^^^^^^^^^^^^
+
+Alternatively, to install from source code, 
+sequentially run following commands in your terminal/command line.
+
+.. code:: bash
+
+    git clone https://github.com/AbductiveLearning/ABLkit.git
+    cd ABLkit
+    pip install -v -e .
+
+(Optional) Install SWI-Prolog
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+If the use of a :ref:`Prolog-based knowledge base <prolog>` is necessary, the installation of `SWI-Prolog <https://www.swi-prolog.org/>`_ is also required:
+
+For Linux users:
+
+.. code:: bash
+
+    sudo apt-get install swi-prolog
+
+For Windows and Mac users, please refer to the `SWI-Prolog Install Guide <https://github.com/yuce/pyswip/blob/master/INSTALL.md>`_.
+
+References
+----------
+
+For more information about ABL, please refer to: `Zhou, 2019 <http://scis.scichina.com/en/2019/076101.pdf>`_ 
+and `Zhou and Huang, 2022 <https://www.lamda.nju.edu.cn/publication/chap_ABL.pdf>`_.
+
+.. code-block:: latex
+
+    @article{zhou2019abductive,
+        title     = {Abductive learning: towards bridging machine learning and logical reasoning},
+        author    = {Zhou, Zhi-Hua},
+        journal   = {Science China Information Sciences},
+        volume    = {62},
+        number    = {7},
+        pages     = {76101},
+        year      = {2019}
+    }
+
+    @incollection{zhou2022abductive,
+        title     = {Abductive Learning},
+        author    = {Zhou, Zhi-Hua and Huang, Yu-Xuan},
+        booktitle = {Neuro-Symbolic Artificial Intelligence: The State of the Art},
+        editor    = {Pascal Hitzler and Md. Kamruzzaman Sarker},
+        publisher = {{IOS} Press},
+        pages     = {353--369},
+        address   = {Amsterdam},
+        year      = {2022}
+    }
 
 .. toctree::
    :maxdepth: 1