Answers.Body: <p>To put this insert to context, we should take at least this much of text from the paper:</p>
  
  <blockquote>
    <p>One line of research focuses on making recommendations using knowledge graph embedding models, such as TransE [2] and node2vec [5]. These approaches align the knowledge graph in a regularized vector space and uncover the similarity between entities by calculating their representation distance [30]. However,pure KG embedding methods lack the ability to discover multi-hop relational paths.</p>
  </blockquote>
  
  <p>In my understanding the <code>pure KG embedding</code> here refers to <code>TransE</code> and <code>node2vec</code> solutions. To learn more about those, we should read links [1] and [2]. From [1]:</p>
  
  <blockquote>
    <p>Usually, we use a triple (head, relation, tail) to represent a knowledge. Here, head and tail are entities. For example, (sky tree, location, Tokyo). We can use the one-hot vector to represent this knowledge.</p>
  </blockquote>
  
  <p>Later on same source there is an explanation on the end of definition section of <code>TransE</code> solution:</p>
  
  <blockquote>
    <p>But this model only can take care with one-to-one relation, not suitable for one-to-many/many-to-one relation, for example, there is two knowledge, (skytree, location, tokyo) and (gundam, location, tokyo). After training, the 'sky tree' entity vector will be very close with 'gundam' entity vector. But they do not have such similarity in real.</p>
  </blockquote>
  
  <p>On other hand, [3] tells:</p>
  
  <blockquote>
    <p>Knowledge graphs as described above represents a static snapshot of our knowledge. It does not reflect the process of it’s how the knowledge built up. In the real world, we learn by observing temporal patterns. While it’s possible to learn the similarity between nodes A and node B, it will be hard to see the similarity between node A and node C as it was 3 years ago.</p>
  </blockquote>
  
  <p>Solution in paper states "--,each recommended item is associated with around 1.6 reasoning paths." which is supposedly impossible for <code>TransE</code> solution.</p>
  
  <p>So, knowledge graph, purely, is a static snapshot that can identify by great accuracy one to one findings. Actually according what [2] tells how <code>node2vec</code> works, they can describe and combine also more information (<code>node2vec</code> combines different types of similarities at same time), but anyways I think the main point is actually in one word in the citation: <code>discover</code>! </p>
  
  <p>The model suggested on paper adds Reinforcement Learning principles to the KG modeling, so to say, the pure KG embedding always tells one distance based statistical solution, but a RL based solution may learn more aspects behind the scenes as it learns by try and error more complex paths underlying in the behavior.</p>
  
  <p>On the other hand, the paper says, when relating their solution to pure <code>TransE</code>:</p>
  
  <blockquote>
    <p>It can be regarded as a single-hop latent matching method, but the post-hoc explanations do not necessarily reflect the true reason of generating a recommendation. In contrast, our methods generate recommendations through an explicit path reasoning process over knowledge graphs, so that the explanations directly reflect how the decisions are generated, which makes the system transparent.</p>
  </blockquote>
  
  <p>So, whether <code>TransE</code> and those could actually recommend things in the given environment, the recommendation reasoning paths may stay obscure.</p>
  
  <p><strong>Sources:</strong></p>
  
  <p>[1] <a href="https://towardsdatascience.com/summary-of-translate-model-for-knowledge-graph-embedding-29042be64273" rel="nofollow noreferrer">https://towardsdatascience.com/summary-of-translate-model-for-knowledge-graph-embedding-29042be64273</a></p>
  
  <p>[2] <a href="https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef" rel="nofollow noreferrer">https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef</a></p>
  
  <p>[3] <a href="https://towardsdatascience.com/extracting-knowledge-from-knowledge-graphs-e5521e4861a0" rel="nofollow noreferrer">https://towardsdatascience.com/extracting-knowledge-from-knowledge-graphs-e5521e4861a0</a></p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-05-16T18:39:17.293Z
Answers.LastActivityDate: 2020-05-16T18:39:17.293Z
Answers.UserDisplayName: mico
Body: <p>According to <a href="https://arxiv.org/abs/1906.05237" rel="nofollow noreferrer">Reinforcement Knowledge Graph Reasoning for Explainable Recommendation</a></p>
  
  <blockquote>
    <p>pure KG embedding methods lack the ability to discover multi-hop relational paths.</p>
  </blockquote>
  
  <p>Why is it so?</p>
  
Tags: <reinforcement-learning><recommender-system><knowledge-graph><knowledge-graph-embeddings>
Title: Why can't pure KG embedding methods discover multi-hop relations paths?
UserDisplayName: stoic-santiago
ThreadText: # Why can't pure KG embedding methods discover multi-hop relations paths?
  
  (Asked by: username_0 on 2020-05-07)
  
  According to [Reinforcement Knowledge Graph Reasoning for Explainable Recommendation](https://arxiv.org/abs/1906.05237)
  
  > pure KG embedding methods lack the ability to discover multi-hop relational paths.
  
  Why is it so?
  
  **Tags:** reinforcement-learning, recommender-system, knowledge-graph, knowledge-graph-embeddings
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2020-05-16. Score: 1 | Accepted answer
  
  To put this insert to context, we should take at least this much of text from the paper:
  
  > One line of research focuses on making recommendations using knowledge graph embedding models, such as TransE [2] and node2vec [5]. These approaches align the knowledge graph in a regularized vector space and uncover the similarity between entities by calculating their representation distance [30]. However,pure KG embedding methods lack the ability to discover multi-hop relational paths.
  
  In my understanding the `pure KG embedding` here refers to `TransE` and `node2vec` solutions. To learn more about those, we should read links [1] and [2]. From [1]:
  
  > Usually, we use a triple (head, relation, tail) to represent a knowledge. Here, head and tail are entities. For example, (sky tree, location, Tokyo). We can use the one-hot vector to represent this knowledge.
  
  Later on same source there is an explanation on the end of definition section of `TransE` solution:
  
  > But this model only can take care with one-to-one relation, not suitable for one-to-many/many-to-one relation, for example, there is two knowledge, (skytree, location, tokyo) and (gundam, location, tokyo). After training, the 'sky tree' entity vector will be very close with 'gundam' entity vector. But they do not have such similarity in real.
  
  On other hand, [3] tells:
  
  > Knowledge graphs as described above represents a static snapshot of our knowledge. It does not reflect the process of it’s how the knowledge built up. In the real world, we learn by observing temporal patterns. While it’s possible to learn the similarity between nodes A and node B, it will be hard to see the similarity between node A and node C as it was 3 years ago.
  
  Solution in paper states "--,each recommended item is associated with around 1.6 reasoning paths." which is supposedly impossible for `TransE` solution.
  
  So, knowledge graph, purely, is a static snapshot that can identify by great accuracy one to one findings. Actually according what [2] tells how `node2vec` works, they can describe and combine also more information (`node2vec` combines different types of similarities at same time), but anyways I think the main point is actually in one word in the citation: `discover`!
  
  The model suggested on paper adds Reinforcement Learning principles to the KG modeling, so to say, the pure KG embedding always tells one distance based statistical solution, but a RL based solution may learn more aspects behind the scenes as it learns by try and error more complex paths underlying in the behavior.
  
  On the other hand, the paper says, when relating their solution to pure `TransE`:
  
  > It can be regarded as a single-hop latent matching method, but the post-hoc explanations do not necessarily reflect the true reason of generating a recommendation. In contrast, our methods generate recommendations through an explicit path reasoning process over knowledge graphs, so that the explanations directly reflect how the decisions are generated, which makes the system transparent.
  
  So, whether `TransE` and those could actually recommend things in the given environment, the recommendation reasoning paths may stay obscure.
  
  **Sources:**
  
  [1] <https://towardsdatascience.com/summary-of-translate-model-for-knowledge-graph-embedding-29042be64273>
  
  [2] <https://towardsdatascience.com/node2vec-embeddings-for-graph-data-32a866340fef>
  
  [3] <https://towardsdatascience.com/extracting-knowledge-from-knowledge-graphs-e5521e4861a0>
  
  
  ---
  
  