知識圖譜一直是學術工業界關注的焦點，但是知識圖譜的書籍缺非常少。南加州大學計算機科學家Mayank Kejriwal撰寫了《Domain-Specific Knowledge Graph Construction》，總共115頁圖書，包含了知識圖譜的涵義、信息抽取、實體鏈接、知識圖譜補全、知識圖譜實例等內容，值得學習閱讀！
領域知識圖譜構建
特定領域的知識圖譜已經作為一個方向開始出現，並且發展迅速。圖方法在人工智能中已經存在了很長一段時間，可以追溯到該領域最早的時代，但將大量數據自動表示為圖譜是一項相對現代的發明。隨著Web的出現，以及對更智能搜索引擎的需求，穀歌知識圖譜誕生了。穀歌知識圖譜改變了我們與搜索引擎交互的方式，盡管我們常常沒有意識到這一點。例如，用戶在搜索某個東西時不點擊某個鏈接的情況已經不再罕見;一般來說，搜索引擎本身能夠為用戶所麵臨的問題提供解決方案。將傳統的搜索引擎與圖像、新聞和視頻有機地結合起來，為這些交互添加豐富的元素。
領域特定知識圖構建(KGC)是一個活躍的研究領域，最近由於機器學習技術(如深度神經網絡和單詞嵌入)取得了令人印象深刻的進展。本書將以一種引人入勝和可訪問的方式綜合Web數據上的知識圖結構。
知識圖譜示例
Google知識圖譜構建流程
目錄內容：
1.什麼是知識圖譜?
1.1 引言
1.2 示例 1: 學術領域
1.3 示例 2: 產品與公司
1.4 示例 3: 地理政治事件
1.5 結論
2 信息抽取
2.1 引言
2.2 IE挑戰
2.3 IE 任務範疇
2.3.1 命名實體識別
2.3.2 關係提取
2.3.3 事件提取
2.3.4 Web IE
2.4 IE效果評估
2.5 總結
3 實體消歧
3.1 引言
3.2 挑戰與要求
3.3 兩階段框架
3.4 性能度量
3.5 兩階段框架流程擴展
3.6 相關工作概述
3.7 總結
4. 高級主題: 知識圖譜補全
4.1 引言
4.2 知識圖譜嵌入
4.2.1 TransE
4.2.2 TransE Extensions and Alternatives
4.2.3 局限
4.2.4 前沿以及相關工作
4.2.5 KGEs應用
4.3 引言
5 生態係統
5.1 引言
5.2 Web鏈接數據
5.2.1 鏈接數據原則
5.2.2 技術棧
5.2.3 鏈接開放數據
5.2.4 例子: DBpedia
5.3 Google知識圖譜
5.4 Schema.org
5.5 未來展望
下載鏈接:https://pan.baidu.com/s/1vnyVBRn8GclvwEOH_eqM2g提取碼: 4y44
Reader reviews of literary fiction on social media, especially those in persistent, dedicated forums, create and are in turn driven by underlying narrative frameworks. In their comments about a novel, readers generally include only a subset of characters and their relationships, thus offering a limited perspective on that work. Yet in aggregate, these reviews capture an underlying narrative framework comprised of different actants (people, places, things), their roles, and interactions that we label the "consensus narrative framework". We represent this framework in the form of an actant-relationship story graph. Extracting this graph is a challenging computational problem, which we pose as a latent graphical model estimation problem. Posts and reviews are viewed as samples of sub graphs/networks of the hidden narrative framework. Inspired by the qualitative narrative theory of Greimas, we formulate a graphical generative Machine Learning (ML) model where nodes represent actants, and multi-edges and self-loops among nodes capture context-specific relationships. We develop a pipeline of interlocking automated methods to extract key actants and their relationships, and apply it to thousands of reviews and comments posted on Goodreads.com. We manually derive the ground truth narrative framework from SparkNotes, and then use word embedding tools to compare relationships in ground truth networks with our extracted networks. We find that our automated methodology generates highly accurate consensus narrative frameworks: for our four target novels, with approximately 2900 reviews per novel, we report average coverage/recall of important relationships of > 80% and an average edge detection rate of >89\%. These extracted narrative frameworks can generate insight into how people (or classes of people) read and how they recount what they have read to others.
