Exploiting Hybrid Semantics of Relation Paths for Multi-hop Question Answering over Knowledge Graphs (2022)

Abstract: Answering natural language questions on knowledge graphs (KGQA) remains a great challenge in terms of understanding complex questions via multi-hop reasoning. Previous efforts usually exploit large-scale entity-related text corpus or knowledge graph (KG) embeddings as auxiliary information to facilitate answer selection. However, the rich semantics implied in off-the-shelf relation paths between entities is far from well explored. This paper proposes improving multi-hop KGQA by exploiting relation paths’ hybrid semantics. Specifically, we integrate explicit textual information and implicit KG structural features of relation paths based on a novel rotate-and-scale entity link prediction framework. Extensive experiments on three existing KGQA datasets demonstrate the superiority of our method, especially in multi-hop scenarios. Further investigation confirms our method’s systematical coordination between questions and relation paths to identify answer entities.

## introduction answering natural language questions on knowledge graphs (kgqa) is a challenging task @xcite . recent works mainly pay attention to a complex scenario, namely multi-hop kgqa @xcite @xcite , where sophisticated reasoning over multiple edges (or relations) is required to infer the correct answer in the kg @xcite . the main challenge of multi-hop kgqa is to understand complicated questions and reason under incomplete kg, usually without supervision signals at the intermediate reasoning steps @xcite . one common strategy to alleviate this dilemma is to exploit auxiliary information to enrich knowledge representation. for example, † corresponding authors. to the best of our knowledge, @xcite is the only effort involving exploiting the off-theshelf relation path information. they use relation paths as simple coarse-grained input features by concatenating their text descriptions. from a more fine-grained and systematic perspective, given a question as a semantic view for the implied relational fact of @xmath0, target @xmath1, a relation path can serve as another highly-related yet complementary one. therefore, we propose coordinating the question view and the relation path view to identify target entities more accurately. to make this idea work, we face two main challenges: 1) how to accurately represent relation paths and 2) how to fuse a relation path representation with the question representation. for the first problem, we propose exploiting hybrid features of relation paths by integrating both explicit textual semantics and implicit kg embedding features. firstly, previous works have shown the merits of introducing entity-related texts @xcite @xcite , while we conjecture that relationrelated texts (e.g., relation names or descriptions) can potentially offer helpful clues to answer a question. meanwhile, relation-related texts are naturally available and on a much smaller scale compared with entity-related texts. therefore, we utilize the explicit text description of a relation path (a relation set) as an extra feature of kgqa models to facilitate target entity selection. secondly, in addition to a text description, a relation also has a kg-based representation (relation embedding) that implicitly contains rich kg structural semantics. therefore, we introduce rotate, a kg embedding model that can well support relation composition by entity rotation in the complex vector space @xcite . with rotate, we can synthesize relation path representation by performing simple elementwise multiplication of individual relation embeddings. finally, we characterize beneficial knowledge in candidate relation paths by fusing their structural and textual representations in a questionaware manner, which also facilitates filtering appropriate relation paths semantically consistent with the question among numerous noisy candidates. for the second problem, inspired by @xcite , we project the well-designed questionaware mixed representations of relation paths, as well as the question representation, into a rotating entity link prediction framework. however, our pilot experiments showed that the rotating-based link prediction did not yield the robust performance we expected. further investigation revealed that the modulus of entity embeddings by rotate mattered. for example, compared with 1-hop answer entities, 2-hop answer entities more significantly differ from their topic entities on modulus. this observation inspires us to match the integrated semantics of questions and relation paths by both entity rotation and entity modulus scaling. after introducing an entity modulus scaling mechanism, we achieve a promising rotate-and-scale prediction framework, which better coordinate knowledge of questions and relation paths for kgqa. extensive experiments on three existing kgqa datasets @xcite , complexwebquestions @xcite , and metaqa @xcite ) verify the superiority of our method, especially in multi-hop scenarios. our contributions are as follows: ## problem statement kgqa is the task of factoid question answering over a knowledge graph. a knowledge graph is denoted as g ⊆ e × r × e , where e is the set of all entities in the kg and r is the set of all relations. a triple can be formally described as (h, r, t) , where h, @xmath2. given a natural language question @xmath3, . . . , w |q| and a topic entity @xmath4, which should be present in the question, the task of kgqa is to extract answer entities c @xmath5. in practice, we perform entity linking on the question q, producing a set of topic entities h. ## related work there are two categories of kgqa methods commonly known as semantic parsing-based methods and information retrieval-based methods @xcite . we mainly focus on the second one. @xcite to answer the question, but it cannot work in the scenario of kgqa. embedkgqa @xcite leverages kg embeddings and projects the question into a link prediction framework. ## conclusion we have presented our method for kgqa, which offers a novel perspective of exploiting hybrid features of the off-the-shelf relation paths. we distill essential relation path features by fusing explicit textual information and implicit structural features via a question-aware manner. by project-ing a natural language question as well as the obtained hybrid features of candidate relation paths into a novel rotate-and-scale entity link prediction framework, we effectively coordinate question and relation paths to select the answer entity. we reveal that questions and relation paths can be seen as two relevant yet complementary facets of their corresponding relations between a topic entity and a target entity.