Learning Interpretable Relationships between Entities, Relations and Concepts via B ayesian Structure Learning on Open Domain Facts (2020)

Abstract: Concept graphs are created as universal taxonomies for text understanding in the open-domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to concepts, showing that an entity is an instance of a concept. In this paper, we propose the task of learning interpretable relationships from open-domain facts to enrich and refine concept graphs. The Bayesian network structures are learned from open-domain facts as the interpretable relationships between relations of facts and concepts of entities. We conduct extensive experiments on public English and Chinese datasets. Compared to the state-of-the-art methods, the learned network structures help improving the identification of concepts for entities based on the relations of entities on both datasets.

## introduction concept graphs are created as universal taxonomies for text understanding and reasoning in the open domain knowledge @xcite @xcite @xcite . the nodes in concept graphs include both entities and concepts. the edges are from entities to concepts, showing that an entity is an instance of a concept. the task of extracting and building concept graphs from user-generated texts has attracted a lot of research attentions for a couple of decades @xcite @xcite @xcite . most of these methods rely on high quality syntactic patterns to determine whether an entity belongs to a concept. for example, given the pattern "x is a y " or "y , including x" appearing in sentences, we can infer that the entity x is an instance of the concept y . these pattern-based methods require that an entity and concept pair co-occurs in sentences. however, due to the different expressions of a certain concept, an entity and a concept may rarely appear in sentences together. we conduct a data analysis of millions of sentences extracted from wikipedia and discover that only 10.61% of entity-concept pairs co-occur in sentences out of more than six million of pairs from the public microsoft concept graph ( https: //concept.research.microsoft.com ). we also analyze baidu baike ( http://baike.baidu.com ) and its corresponding concept graph. a similar phenomenon is observed that only 8.56% entityconcept pairs co-occur in sentences. ## finding interpretable relationships we formulate the relationships between entities, relations, and concepts as follows: in concept graphs, a concept is associated with a set of entities which share some common behaviors or attributes. however, the essence of a concept is a set of relations, and entities which associate with these relations automatically become the instance of the concept. so our formulation of the relationships between entities, relations and concepts can be illustrated by figure 2 . in the closed domain, a knowledge base has a predefined ontology and the relationships in figure 2 are already known. for example, dbpedia @xcite builds a knowledge graph from wikipedia to encode the relationships between entities and relations in the forms of facts. the relationships between relations and concepts are represented in the ontology structure of dbpedia, where each concept is associated with a group of relations. however, in the open domain, a predefined ontology does not exist, and hence the components in figure 2 may not be associated with each other. for instance, given an open domain concept graph, we can discover the relationships between entities and concepts. given the open domain corpus/facts, we can find the relationships between entities and relations. but the relationships between open domain concepts and relations are not available, to our knowledge. in this paper, we aim to find the connection between open domain relations and concepts, so that we can provide interpretations to the question "why the entity is associated with those concepts in open domain". where p(c|(e, r)) = p(c|r) is due to our bayesian network assumption (see figure 2 ). by learning with the observed triplets with above model, we can infer the missing triplets, especially give interpretable relationship between entities and concepts. ## experiments with the learned relationship between relations and concepts from bnsl, we indirectly associate entities with their concepts and give interpretations to the question "why the entity is associated with those concepts in open domain". the hypernymy detection task aims to identify concepts for entities in open domain. it is helpful for us to evaluate the quality of the learned relationships from bnsl. in this section, we conduct extensive experiments to evaluate the performance of bnsl. ## conclusion in this paper, we investigate the task of learning interpretable relationships between entities, relations and concepts from open domain facts to help enriching and refining concept graphs. the bayesian network structures are learned from open domain facts as the discovered meaningful dependencies between relations of facts and concepts of entities. experimental results on an english dataset and a chinese dataset reveal that the learned network structures can better identify concepts for entities based on the relations of entities from open domain facts, which will further help building a more complete concept graph.