解讀知識圖譜的2020 : 技術成熟度飛速躍升，與產業互聯結合更加緊密

{"type":"doc","content":[{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"對比2020和2019年Gartner發佈的人工智能領域的技術“成熟度曲線”(Hype Cycle)，"},{"type":"text","marks":[{"type":"strong"}],"text":"在短短1年時間，知識圖譜的成熟度由創新觸發階段一躍達到預期膨脹高峯階段且非常接近最高點。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"知識圖譜逐漸成爲人工智能應用的強大助力。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"曲線表示，知識圖譜的發展還需要 5 - 10 年時間才能到達成熟的階段，知識圖譜依然有很大的發展空間。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.infoq.cn\/resource\/image\/17\/c5\/17ee81f8bd744f4e18b54930580c22c5.png","alt":null,"title":"","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"image","attrs":{"src":"https:\/\/static001.infoq.cn\/resource\/image\/4d\/3c\/4dec7yy3809c8aaf8a804881e65b463c.png","alt":null,"title":"","style":[{"key":"width","value":"75%"},{"key":"bordertype","value":"none"}],"href":"","fromPaste":false,"pastePass":false}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"本文將從"},{"type":"text","marks":[{"type":"strong"}],"text":"知識抽取、知識融合、知識推理"},{"type":"text","text":"的角度探索過去一年知識圖譜在自動構建領域的技術突破，並結合圖機器學習、圖數據庫探討相關領域的技術發展。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","text":"而"},{"type":"text","marks":[{"type":"strong"}],"text":"在應用上，知識圖譜在2020年與產業互聯的結合更加緊密"},{"type":"text","text":"，除了在數據治理、搜索與推薦、問答等通用領域有所突破之外，在智能生產、智慧城市、智能管理、智能運維等衆多領域，以及工業、金融、司法、公安、醫療、教育等衆多行業也都有進一步的場景化落地的突破。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":2},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"一、 重要的技術發展"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"heading","attrs":{"align":null,"level":3},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"知識圖譜構建"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null},"content":[{"type":"text","marks":[{"type":"strong"}],"text":"2020年，利用自然語言處理、機器學習等技術從多源異構的數據資源中自動構建知識圖譜的技術取得長足進展。"}]},{"type":"paragraph","attrs":{"indent":0,"number":0,"align":null,"origin":null}}]}