TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG as components of R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space. TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications. With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding. We achieve new state-of-the-art performances with signficant margins in Link Prediction and Triple Classification on FB122 dataset, with boosted performance even on test instances that cannot be inferred by logical rules. The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.
The authors propose TransINT, a novel graph embedding method that builds upon methods such as TransH (Knowledge graph embedding by translating on hyperplanes, Wang et al 2014), TransE (Translating Embeddings for Modeling Multi-relational Data, Bordes et al 2013), and appears to offer state of the art performance on the Freebase link prediction benchmark. The manuscript is OK overall in terms of writing (although it could use some copy editing for grammar) and relevant to this community. This reviewer is not familiar with knowledge graph embeddings, and found it difficult to follow the main ideas and contributions. The introduction could certainly be written in a clearer way, first stating the general problem and then building up from existing solutions to the proposed method. Many of the figure captures seem to contain typos (details below), making it particularly confusing to follow. The results appear impressive, but I will leave it to reviewers with more experience in the field to judge how it stacks up against recent work. Minor comments: - In the first paragraph, there are two instances where closing brackets have been replaced by opening brackets. - Section 2, where transH is mentioned for the first time, it*s missing a reference - Incomplete sentence *Let ri be a binary relation x, y entities*. - Figure 2: h,t are not defined. is_family is mislabled as is_parent -Figure 3: The line for is_parent_of is mislabeled as is_mother of. - Section 5, *We evaluate TransINT on Freebase 122 (respectively created by ...*: What do you mean by *respectively* if there only Freebase 122 and no other dataset? Maybe forgot KALE here, it*s mentioned a few sentences later for the first time and never defined.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
TransINT: Embedding Implication Rules in Knowledge Graphs with Isomorphic Intersections of Linear Subspaces
Knowledge Graphs (KG), composed of entities and relations, provide a structured representation of knowledge. For easy access to statistical approaches on relational data, multiple methods to embed a KG as components of R^d have been introduced. We propose TransINT, a novel and interpretable KG embedding method that isomorphically preserves the implication ordering among relations in the embedding space. TransINT maps set of entities (tied by a relation) to continuous sets of vectors that are inclusion-ordered isomorphically to relation implications. With a novel parameter sharing scheme, TransINT enables automatic training on missing but implied facts without rule grounding. We achieve new state-of-the-art performances with signficant margins in Link Prediction and Triple Classification on FB122 dataset, with boosted performance even on test instances that cannot be inferred by logical rules. The angles between the continuous sets embedded by TransINT provide an interpretable way to mine semantic relatedness and implication rules among relations.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



The authors propose TransINT, a novel graph embedding method that builds upon methods such as TransH (Knowledge graph embedding by translating on hyperplanes, Wang et al 2014), TransE (Translating Embeddings for Modeling Multi-relational Data, Bordes et al 2013), and appears to offer state of the art performance on the Freebase link prediction benchmark. The manuscript is OK overall in terms of writing (although it could use some copy editing for grammar) and relevant to this community. This reviewer is not familiar with knowledge graph embeddings, and found it difficult to follow the main ideas and contributions. The introduction could certainly be written in a clearer way, first stating the general problem and then building up from existing solutions to the proposed method. Many of the figure captures seem to contain typos (details below), making it particularly confusing to follow. The results appear impressive, but I will leave it to reviewers with more experience in the field to judge how it stacks up against recent work. Minor comments: - In the first paragraph, there are two instances where closing brackets have been replaced by opening brackets. - Section 2, where transH is mentioned for the first time, it*s missing a reference - Incomplete sentence *Let ri be a binary relation x, y entities*. - Figure 2: h,t are not defined. is_family is mislabled as is_parent -Figure 3: The line for is_parent_of is mislabeled as is_mother of. - Section 5, *We evaluate TransINT on Freebase 122 (respectively created by ...*: What do you mean by *respectively* if there only Freebase 122 and no other dataset? Maybe forgot KALE here, it*s mentioned a few sentences later for the first time and never defined.
assistant
The authors propose TransINT, a novel graph embedding method that builds upon methods such as TransH (Knowledge graph embedding by translating on hyperplanes, Wang et al 2014), TransE (Translating Embeddings for Modeling Multi-relational Data, Bordes et al 2013), and appears to offer state of the art performance on the Freebase link prediction benchmark. The manuscript is OK overall in terms of writing (although it could use some copy editing for grammar) and relevant to this community. This reviewer is not familiar with knowledge graph embeddings, and found it difficult to follow the main ideas and contributions. The introduction could certainly be written in a clearer way, first stating the general problem and then building up from existing solutions to the proposed method. Many of the figure captures seem to contain typos (details below), making it particularly confusing to follow. The results appear impressive, but I will leave it to reviewers with more experience in the field to judge how it stacks up against recent work. Minor comments: - In the first paragraph, there are two instances where closing brackets have been replaced by opening brackets. - Section 2, where transH is mentioned for the first time, it*s missing a reference - Incomplete sentence *Let ri be a binary relation x, y entities*. - Figure 2: h,t are not defined. is_family is mislabled as is_parent -Figure 3: The line for is_parent_of is mislabeled as is_mother of. - Section 5, *We evaluate TransINT on Freebase 122 (respectively created by ...*: What do you mean by *respectively* if there only Freebase 122 and no other dataset? Maybe forgot KALE here, it*s mentioned a few sentences later for the first time and never defined.