Open AccessPosted Content
Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
Hung Nghiep Tran,Atsuhiro Takasu +1 more
TL;DR: In this article, a multi-embedding interaction mechanism is proposed to unify and generalize these models and derive them theoretically via this mechanism and provide empirical analyses and comparisons between them.
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Abstract: Knowledge graph is a popular format for representing knowledge, with many applications to semantic search engines, question-answering systems, and recommender systems. Real-world knowledge graphs are usually incomplete, so knowledge graph embedding methods, such as Canonical decomposition/Parallel factorization (CP), DistMult, and ComplEx, have been proposed to address this issue. These methods represent entities and relations as embedding vectors in semantic space and predict the links between them. The embedding vectors themselves contain rich semantic information and can be used in other applications such as data analysis. However, mechanisms in these models and the embedding vectors themselves vary greatly, making it difficult to understand and compare them. Given this lack of understanding, we risk using them ineffectively or incorrectly, particularly for complicated models, such as CP, with two role-based embedding vectors, or the state-of-the-art ComplEx model, with complex-valued embedding vectors. In this paper, we propose a multi-embedding interaction mechanism as a new approach to uniting and generalizing these models. We derive them theoretically via this mechanism and provide empirical analyses and comparisons between them. We also propose a new multi-embedding model based on quaternion algebra and show that it achieves promising results using popular benchmarks. Source code is available on github at this https URL
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Citations
Exploring Scholarly Data by Semantic Query on Knowledge Graph Embedding Space
Hung Nghiep Tran,Atsuhiro Takasu +1 more
- 09 Sep 2019
TL;DR: In this article, the authors propose a general framework for data exploration by semantic queries and discuss the solution to some traditional scholarly data exploration tasks and propose some new interesting tasks that can be solved based on the uncanny semantic structures of the embedding space.
7
Information Prediction using Knowledge Graphs for Contextual Malware Threat Intelligence.
TL;DR: This paper proposes an end-to-end approach to generate a Malware Knowledge Graph called MalKG, the first open-source automated knowledge graph for malware threat intelligence, and proposes a framework to automate the extraction of thousands of entities and relations into RDF triples at the sentence level from 1,100 malware threat Intelligence reports and from the com-mon vulnerabilities and exposures (CVE) database.
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A Path-Based Personalized Recommendation Using Q Learning
Hyeseong Park,Kyung-Whan Oh +1 more
- 01 Jan 2021
TL;DR: In this paper, a path-based recommendation using Q learning is proposed to select meaningful latent features in the knowledge graph between users and items, which provides higher accuracy compared to a uniform random walk based algorithm.
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•Posted Content
Role-Aware Modeling for N-ary Relational Knowledge Bases
Yu Liu,Quanming Yao,Yong Li +2 more
TL;DR: Zhang et al. as discussed by the authors proposed a role-aware model for facts in n-ary relational knowledge bases, which explores a latent space that contains basis vectors, and represents roles by linear combinations of these vectors.
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Predicting malware threat intelligence using KGs
Nidhi Rastogi,Sharmishtha Dutta,Ryan Christian,Jared Gridley,Mohammad Zaki,Alex Gittens,Charu C. Aggarwal +6 more
TL;DR: MalKG as mentioned in this paper is an open-source knowledge graph for malware threat intelligence, which contains approximately 40,000 triples generated from 27,354 unique entities and 34 relations.
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