Followed instructions from https://dev.grakn.ai/docs/general/quickstart and immediatly reached these errors :

grakn-core-all-windows-1.7.1>grakn.bat console --keyspace social_network --file ./schema.gql

22:08:17,078 |-INFO in ch.qos.logback.classic.LoggerContext[default] - Could NOT find resource [logback-test.xml]
22:08:17,078 |-INFO in ch.qos.logback.classic.LoggerContext[default] - Could

I wanted to use pytextrank together with spacy_udpipe to get keywords from texts in other languages (see https://stackoverflow.com/questions/59824405/spacy-udpipe-with-pytextrank-to-extract-keywords-from-non-english-text) but I realized, that udpipe-spacy somehow "overrides" the original spacy's pipeline so the noun_chunks are not generated (btw: the noun_chunks are created in lang/en/syntax_itera

您好，很高兴看到您的项目，我使用您的方法把数据导入neo4j数据库

但是我访问 localhost:7474,并没有看到知识图谱，这是怎么回事呢?

请您多指教一下，谢谢!

Self-explanatory.

I'm reading your paper and have a question about section 3.2. Could you explain what are the vertex matrix and context matrix? Are they simply the source and the destination of edges?

Currently TiddlyMap outputs to Png and JSON, but being able to do it in .SVG would be pretty neat as well as an option for some TiddlyWiki projects.

一句话总结：

针对abstractive text summarization task的seq2seq模型有两个缺点：重现的细节不准确，经常重复自己。

这篇文章我们提出一个框架来增强seq2seq, in two orthogonal ways。首先提出一个a hybrid pointer-generator network将source text里的word准确pointing到结果中去，并能通过generator保持产生novel words的能力。第二点，使用coverage来减少repitiion的情况。

资源：

• pdf
• code
• [pape

The AtomTable uses a mutex to guard access to the TypeIndex. This mutex could be mostly avoided by using a concurrent hash map. The one described here: https://preshing.com/20160201/new-concurrent-hash-maps-for-cpp/ seems like a good choice. implementing this requires

• Copying the concurrent hash map source to cog-utils
• Modifying TypeIndex to use it.
• Removing the mutex lock in `AtomT

_____________________ ERROR collecting test_krl_transe.py _____________________
test_krl_transe.py:26: in
krl.train(train_path, model_type=model_type, dev_path=train_path, save_path='./krl_{}_saves'.format(model_type))
..........\Anaconda3\envs\python367\lib\site-packages\lightkg\krl\module.py:29: in train
train_dataset = rl_tool.get_dataset(train_path)
..........\Ana

To help users unfamiliar with SMW to find features and supplementary functions which are normally not enabled by default, I created [0] as starting point.

I would appreciate if some people from the community find the time to finalize and complete the list.

Tasks

• Identify features and supplementary functions
  • SMWSearch
• Add separate summary sections to the smw@wiki

Instead of:

{
  Get {
    Things {
      Article {
        title
        url
        wordCount
        HasAuthors {
          ... on Author {
            name
          }
          ... on Publication {
            name
          }
        }
      }
    }
  }
}

Use

{
  Get {
    Things {
      Article {
        title
        url