SimPhoNy semantic2dot

{ "cells": [ { "cell_type": "markdown", "id": "5d751f70-0183-4553-8e48-c62eebdc8f75", "metadata": {}, "source": [ "# Visualization" ] }, { "cell_type": "markdown", "id": "9a994990-fcda-497a-9e05-beaf9a5d1ddd", "metadata": {}, "source": [ "

\n", " \n", "[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/simphony/docs/v4.0.0?filepath=docs%2Fusage%2Fvisualization.ipynb \"Click to run this tutorial yourself!\")\n", " \n", "" ] }, { "cell_type": "markdown", "id": "baea79ad-864a-40cf-83e4-27518aa6b8f7", "metadata": {}, "source": [ "SimPhoNy includes two visualization tools:\n", "\n", "- [semantic2dot](#semantic2dot) to visualize ontology entities, including both assertional and terminological knowledge,\n", "- [pretty_print](#pretty_print), used to \"visualize\" ontology individuals as text." ] }, { "cell_type": "markdown", "id": "96557eea-a233-4ab0-998c-6b565ad41d2d", "metadata": {}, "source": [ "## `semantic2dot`" ] }, { "cell_type": "markdown", "id": "6a84feda-a535-42e9-8833-570169ec2911", "metadata": {}, "source": [ "`semantic2dot` makes use of [Graphviz](https://graphviz.org/) and is located under `simphony_osp.tools.semantic2dot`. It can be used to quickly draw ontologies, as well as ontology individuals. In the representations created by the tool, each ontology entity is represented by a graph node. The relationships between ontology entities are the edges connecting them. The attributes values and classes that individuals belong to are written inside the nodes." ] }, { "cell_type": "markdown", "id": "d1d562e8-51e0-4ade-9fc3-3959ec98919b", "metadata": {}, "source": [ "To **draw a namespace** from an installed ontology, import the namespace and pass the namespace object to `semantic2dot`. Several namespace objects can be drawn together in the same picture." ] }, { "cell_type": "code", "execution_count": 3, "id": "a06757f3-eb73-4b8f-b5c0-40284d319d9a", "metadata": {}, "outputs": [], "source": [ "!pico install city foaf" ] }, { "cell_type": "code", "execution_count": 2, "id": "784464ff-3280-4ab3-b1ea-5af63284c70c", "metadata": {}, "outputs": [], "source": [ "from simphony_osp.namespaces import city, foaf\n", "from simphony_osp.tools import semantic2dot" ] }, { "cell_type": "code", "execution_count": 4, "id": "136b3f4c-0f9e-4c72-9d7c-19684dec8025", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "semantic2dot(city, foaf)" ] }, { "cell_type": "markdown", "id": "24485fc5-634b-425a-bdbb-112f1ffe6eee", "metadata": {}, "source": [ "

\n", "

Tip

\n", " \n", "On most web browsers (including mobile ones), you can right-click the picture above and then click \"open in new tab\" to see the picture in its full size.\n", " \n", "

" ] }, { "cell_type": "markdown", "id": "b104cca5-bf84-47f1-8f9c-8a36aeced0fe", "metadata": {}, "source": [ "It is also possible to **draw the contents of a session**. To do so, pass the session object to `semantic2dot`." ] }, { "cell_type": "code", "execution_count": 5, "id": "b7a801ab-31ca-4b20-9c8d-32a0a716f4d4", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from simphony_osp.session import core_session\n", "\n", "freiburg = city.City(name=\"Freiburg\", coordinates=[47.997791, 7.842609])\n", "peter = city.Citizen(name=\"Peter\", age=30)\n", "anne = city.Citizen(name=\"Anne\", age=20)\n", "freiburg[city.hasInhabitant] += peter, anne\n", "\n", "semantic2dot(core_session)" ] }, { "cell_type": "markdown", "id": "890a0490-af0b-45c6-b1a7-f811c9526415", "metadata": {}, "source": [ "Another option is to **draw only specific ontology individuals**." ] }, { "cell_type": "code", "execution_count": 6, "id": "fb786f44-ad1c-460c-a23c-807466f9d4a1", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "semantic2dot(freiburg, anne)" ] }, { "cell_type": "markdown", "id": "3b36bfcd-8c65-4390-a16b-eda1b47ac0af", "metadata": {}, "source": [ "Any combination of such three types of object, any number of times can be provided." ] }, { "cell_type": "code", "execution_count": 7, "id": "57b0d087-3694-4ef7-a8e7-77148ef1b8bb", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "semantic2dot(foaf, city, core_session, freiburg, anne)" ] }, { "cell_type": "markdown", "id": "a0f3bdff-0968-4b02-ae5f-78fa7de3977a", "metadata": {}, "source": [ "A relationship can optionally be passed to `semantic2dot` so that it automatically calls the [find](sessions/search.ipynb#find) method from the search module on all ontology individuals and includes the additional individuals in the picture. " ] }, { "cell_type": "code", "execution_count": 8, "id": "d349b9be-5671-40c5-93c6-e4ff82492544", "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ "\n", "\n", "\n", "\n", "\n" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from simphony_osp.namespaces import owl\n", "\n", "semantic2dot(freiburg, rel=owl.topObjectProperty)" ] }, { "cell_type": "markdown", "id": "5b542461-bc0f-47fe-ad1d-2adcc3ed4f74", "metadata": {}, "source": [ "`semantic2dot` automatically renders its output as `svg` image files on Jupyter notebooks. However, it is possible to save the generated figures in `gv` (Graphviz) and `png` formats using the `render` method." ] }, { "cell_type": "code", "execution_count": 10, "id": "af12838d-b442-4f7c-bd11-1f31e2c90917", "metadata": {}, "outputs": [], "source": [ "semantic2dot(freiburg, rel=owl.topObjectProperty).render(\"./picture.gv\")" ] }, { "cell_type": "markdown", "id": "dbaa3d0f-55d9-40a4-97b8-37cfa558c120", "metadata": {}, "source": [ "After running the code above, two files, `picture.gv` and `picture.gv.png`, are generated in the current directory." ] }, { "cell_type": "markdown", "id": "daf75786-220c-4794-8ca4-7cbc3f309700", "metadata": {}, "source": [ "## `pretty_print`" ] }, { "cell_type": "markdown", "id": "cb148ab2-71c1-45e9-95d1-4a348a7f18d1", "metadata": {}, "source": [ "`pretty_print` is located under `simphony_osp.tools.pretty_print`. It generates a tree-like, text-based representation stemming from a given ontology individual, that includes the IRI, ontology classes and attributes of the involved individuals, as well as the relationships connecting them." ] }, { "cell_type": "code", "execution_count": 11, "id": "b28f51f3-8488-4721-a968-f594c7b79d0c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- Ontology individual:\n", " identifier: 48927171-fd87-42d7-9f40-be7a4f9b7334\n", " type: City (city)\n", " superclasses: City (city), Populated Place (city), Thing (owl), Geographical Place (city)\n", " values: \u0000coordinates: [47.997791 7.842609]\n", " \u0000name: Freiburg\n", " |_Relationship has inhabitant (city):\n", " - Ontology individual of class Citizen\n", " . identifier: 7047ae55-f831-44bc-aa25-6f654c17cdcf\n", " . \u0000age: 20\n", " . \u0000name: Anne\n", " - Ontology individual of class Citizen\n", " identifier: c1816c6e-c441-408f-b0cc-d99641d4f4fc\n", " \u0000age: 30\n", " \u0000name: Peter\n" ] } ], "source": [ "from simphony_osp.tools import pretty_print\n", "\n", "pretty_print(freiburg)" ] }, { "cell_type": "markdown", "id": "350de02f-7ef3-4644-9396-eb4e25fe2cef", "metadata": {}, "source": [ "`pretty_print` recursively finds the ontology individuals that are connected to the given one using the [find](sessions/search.ipynb#find) method from the search module. By default, all relationships are followed, but the relationships to follow can be restricted using the keyword argument `rel`. In the example below, the search has been restricted to several relationships that do not exist between `freiburg` and its citizens. Therefore, only `freiburg` is displayed as output." ] }, { "cell_type": "code", "execution_count": 13, "id": "78c2e666-1706-40cb-8c7f-98f124313358", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- Ontology individual:\n", " identifier: 48927171-fd87-42d7-9f40-be7a4f9b7334\n", " type: City (city)\n", " superclasses: City (city), Populated Place (city), Thing (owl), Geographical Place (city)\n", " values: \u0000coordinates: [47.997791 7.842609]\n", " \u0000name: Freiburg\n" ] } ], "source": [ "pretty_print(freiburg, rel=(city.hasWorker, city.hasMajor))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 5 }