Knowledge Graph meet Python Yongpan Sheng 目录 CONTENTS The Pipeline of Knowledge Graph Construction by Data- driven manner Python Tools for Graph Data Management Domain-specific Knowledge Graph Construction relation, object>  Mapping from natural questions to structured queries executable on knowledge graph （机器的潜台词：“我”会推理，so easy ！）。 所以，通俗的来说，在AI system中：要么从原有的知识体系中直接提取信息来使用，要 么进行推理。 将知识融合在机器中，使机器能够利 BigKE将显著提升机器的认知水平。 Preliminaries 本页PPT借鉴于复旦大学肖仰华老师《大数据时代的知识工程与知识管理》 Knowledge Graph – KG引领KE复兴  Knowledge graph is a large-scale semantic network consisting of entities and concepts as well as

additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

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additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

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additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

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additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

0 码力 | 1188 页 | 1.42 MB | 1 年前

3

additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

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additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

0 码力 | 1188 页 | 1.42 MB | 1 年前

3

additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

0 码力 | 1215 页 | 1.44 MB | 1 年前

3

additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

0 码力 | 1216 页 | 1.44 MB | 1 年前

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additional components can be defined by the user. The worker is built up using “bootsteps” — a dependency graph enabling fine grained control of the worker’s internals. Framework Integration Celery is easy to be called when all of the tasks in a group have finished executing. This is often required for algorithms that aren’t embarrassingly parallel: >>> from celery import chord >>> res = chord((add.s(i, i) children[0].get() 64 The result instance also has a collect() method that treats the result as a graph, enabling you to iterate over the results: >>> list(res.collect()) [(

0 码力 | 1219 页 | 1.44 MB | 1 年前

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