kcem class file


Keywords
kcem
License
GPL-3.0
Install
pip install kcem==7.1

Documentation

kcem

An Ontology tool kit which is based on kcm and kem

It has 311612 distinct concepts and 1221087 distinct concept-instances

Info of Wiki Dumps

Database changed
mysql> SELECT COUNT(*) FROM categorylinks;
+----------+
| COUNT(*) |
+----------+
| 12490991 |
+----------+
1 row in set (10.10 sec)

mysql> SELECT COUNT(*) FROM page;
+----------+
| COUNT(*) |
+----------+
|  5380929 |
+----------+
1 row in set (5.68 sec)

mysql> 

Install

Manually Install

If you want to integrate kcem into your own django project, use manually install.

  • pip install kcem

Config

Cause this is a django app

so need to finish these django setups.

  1. settings.py:
INSTALLED_APPS = [
    'kcem'
     ...
]
  1. urls.py:
import kcem.urls
urlpatterns += [
    url(r'^kcem/', include(kcem.urls))
]
  1. python3 manage.py buildkcem --lang <lang, e.g., zh or en or th>
  2. fire python manage.py runserver and go 127.0.0.1:8000/ to check whether the config is all ok.

API

  1. Get correlated keywords:/kcem
    • keyword

    • num (default=10)

    • example1:http://udiclab.cs.nchu.edu.tw/kcem?keyword=周杰倫&lang=zh

      {
        "origin": "周杰倫",
        "similarity": 1,
        "value": [
          [
            "臺灣男歌手",
            0.1077329254516259
          ],
          [
            "臺灣創作歌手",
            0.08618136696067945
          ],
          [
            "臺灣作詞家",
            0.08453908870383998
          ],
          [
            "臺灣男演員",
            0.07853875803243504
          ],
          [
            "客家裔臺灣人",
            0.07775312164574238
          ],
          [
            "臺灣作曲家",
            0.06795213648026219
          ],
          [
            "臺灣億萬富豪",
            0.06205651770108215
          ],
          [
            "臺灣新教徒",
            0.06011534659254377
          ],
          [
            "以臺灣音樂家命名的分類",
            0.04864802203579143
          ],
          [
            "臺灣電視主持人",
            0.04471306738037418
          ],
          [
            "叱吒樂壇我最喜愛的歌曲得主",
            0.035176987895887926
          ],
          [
            "全球華語歌曲排行榜最受歡迎男歌手",
            0.03386244943947794
          ],
          [
            "金曲獎演奏類最佳專輯製作人獎獲得者",
            0.03333042924853456
          ],
          [
            "華語流行音樂歌手",
            0.027811995551754405
          ]
        ],
        "key": "周杰倫"
      }

Evaluate

python3 manage.py evaluate --lang <zh or other supported language>

Contributors

License

This package use GPL3.0 License.

KCEM experiments

kcem有兩個參數,kcm取幾個、kem取幾個,本實驗使用google的知識圖譜約500筆做測試

為了畫成二維的圖,把kcm的參數表示成顏色、loss為y軸、kem數量為x軸

參數的範圍從2~30 range(2, 30, 2)

  1. 舊的kcem跟取第一段維基百科去斷詞,而且不是紀錄頻率而是用set儲存所做出來的kcem效果的比較 我的kcem_compare_kcem.png
  2. hybrid的loss V.S. kcem的loss: hybridVSkcem.png
  3. hybrid 的loss V.S. new method的loss: hybridVSnew.png
  4. 四種版本的kcem比較: 同一種方法但是不同kcm、kem參數的loss都畫成同一顏色的線 4個趨勢.png
  5. 四種全部都取平均: 同一種方法但是不同kcm、kem參數的loss都取平均,畫成同一顏色的線 4個平均.png

10/13小結論:

new method因為只取第1段的表現比較不穩定,所以採用hybrid方法

new method相關的實驗程式放在new_kcem_method

將用hybrid的min:kcm:22 kem:12 loss:21.458033917027294 這組參數當作目前最佳的is-a去做後續應用