Question: What models does this overview cover?
Evidence: This intuition inspired the TransE model—a well-known embedding model for KB completion or link prediction in KBs BIBREF2 .
Embedding models for KB completion have been proven to give state-of-the-art link prediction performances, in which entities are represented by latent feature vectors while relation types are represented by latent feature vectors and/or matrices and/or third-order tensors BIBREF24 , BIBREF25 , BIBREF2 , BIBREF26 , BIBREF27 , BIBREF28 , BIBREF29 , BIBREF19 , BIBREF30 , BIBREF3 , BIBREF31 , BIBREF32 , BIBREF33 . This article briefly overviews the embedding models for KB completion, and then summarizes up-to-date experimental results on two standard evaluation tasks: i) the entity prediction task—which is also referred to as the link prediction task BIBREF2 —and ii) the triple classification task BIBREF34 .
The Unstructured model BIBREF22 assumes that the head and tail entity vectors are similar. As the Unstructured model does not take the relationship into account, it cannot distinguish different relation types. The Structured Embedding (SE) model BIBREF35 assumes that the head and tail entities are similar only in a relation-dependent subspace, where each relation is represented by two different matrices. Furthermore, the SME model BIBREF22 uses four different matrices to project entity and relation vectors into a subspace. The TransE model BIBREF2 is inspired by models such as the Word2Vec Skip-gram model BIBREF0 where relationships between words often correspond to translations in latent feature space. TorusE BIBREF36 embeds entities and relations on a torus to handle TransE's regularization problem.
The TransH model BIBREF26 associates each relation with a relation-specific hyperplane and uses a projection vector to project entity vectors onto that hyperplane. TransD BIBREF37 and TransR/CTransR BIBREF28 extend the TransH model by using two projection vectors and a matrix to project entity vectors into a relation-specific space, respectively. Similar to TransR, TransR-FT BIBREF38 also uses a matrix to project head and tail entity vectors. TEKE_H BIBREF39 extends TransH to incorporate rich context information in an external text corpus. lppTransD BIBREF40 extends TransD to additionally use two projection vectors for representing each relation. STransE BIBREF41 and TranSparse BIBREF42 can be viewed as direct extensions of the TransR model, where head and tail entities are associated with their own projection matrices. Unlike STransE, the TranSparse model uses adaptive sparse matrices, whose sparse degrees are defined based on the number of entities linked by relations. TranSparse-DT BIBREF43 is an extension of TranSparse with a dynamic translation. ITransF BIBREF44 can be considered as a generalization of STransE, which allows sharing statistic regularities between relation projection matrices and alleviates data sparsity issue.
DISTMULT BIBREF45 is based on the Bilinear model BIBREF24 , BIBREF22 , BIBREF25 where each relation is represented by a diagonal rather than a full matrix. The neural tensor network (NTN) model BIBREF34 uses a bilinear tensor operator to represent each relation while ER-MLP BIBREF27 and ProjE BIBREF46 can be viewed as simplified versions of NTN. Such quadratic forms are also used to model entities and relations in KG2E BIBREF47 , TransG BIBREF48 , ComplEx BIBREF31 , TATEC BIBREF3 , RSTE BIBREF49 and ANALOGY BIBREF50 . In addition, the HolE model BIBREF33 uses circular correlation–a compositional operator–which can be interpreted as a compression of the tensor product.
ConvE BIBREF51 and ConvKB BIBREF52 are based on convolutional neural networks. ConvE uses a 2D convolutional layer directly over head-entity and relation vector embeddings while ConvKB applies a convolutional layer over embedding triples. Unlike ConvE and ConvKB, the IRN model BIBREF53 uses a shared memory and recurrent neural network-based controller to implicitly model multi-step structured relationships.
The Path Ranking Algorithm (PRA) BIBREF21 is a random walk inference technique which was proposed to predict a new relationship between two entities in KBs. BIBREF61 used PRA to estimate the probability of an unseen triple as a combination of weighted random walks that follow different paths linking the head entity and tail entity in the KB. BIBREF23 made use of an external text corpus to increase the connectivity of the KB used as the input to PRA. BIBREF62 improved PRA by proposing a subgraph feature extraction technique to make the generation of random walks in KBs more efficient and expressive, while BIBREF63 extended PRA to couple the path ranking of multiple relations. PRA can also be used in conjunction with first-order logic in the discriminative Gaifman model BIBREF64 . In addition, BIBREF65 used a recurrent neural network to learn vector representations of PRA-style relation paths between entities in the KB. Other random-walk based learning algorithms for KB completion can be also found in BIBREF66 , BIBREF67 , BIBREF68 and BIBREF69 . Recently, BIBREF70 have proposed a Neural Logic Programming (LP) framework to learning probabilistic first-order logical rules for KB reasoning, producing competitive link prediction performances. See other methods for learning from KBs and multi-relational data in BIBREF4 .
 the TransE model
Embedding models
The Unstructured model BIBREF22
The TransH model BIBREF26
DISTMULT BIBREF45
ConvE BIBREF51 and ConvKB BIBREF52
The Path Ranking Algorithm (PRA) BIBREF21
Answer: The answers are shown as follows:
* This article presented a brief overview of embedding models of entity and relationships for KB completion.