DEER : Descriptive Knowledge Graph for Explaining Entity Relationships (2022)

Abstract: We propose DEER (Descriptive Knowledge Graph for Explaining Entity Relationships) - an open and informative form of modeling entity relationships. In DEER, relationships between entities are represented by free-text relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be represented as “Machine learning explores the study and construction of algorithms that can learn from and make predictions on data.” To construct DEER, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and generate relation descriptions with a transformer-based relation description synthesizing model, where no human labeling is required. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships. The results suggest that we can build an open and informative knowledge graph without human annotation.

## introduction relationships exist widely between entities. for example, a person may be related to another person or an institution, and a scientific concept can be connected to another concept. at the same time, relationships between entities can be subtle or complex, e.g., the relationship between machine learning and algorithm. to model relationships between entities, researchers usually construct knowledge graphs (kgs) @xcite , where nodes are entities, e.g., machine learning, and edges are relations, e.g., subclass of (figure 2 ). however, kgs usually require a pre-specified set of relation types, and the covered relation types are usually coarse-grained and simple. this indicates existing kgs lack two desired features. the first is openness: for entities with a relationship not covered by the type set, kgs cannot handle their relationship directly. besides, in many cases, the relationship between entities is complex or idiosyncratic that it cannot be simply categorized to a relation type. for instance, for related entities machine learning and algorithm, wikidata @xcite does not include a relation for them, and it is also not easy to come up with a relation type to describe their relationship. the second feature is about informativeness. with the relational facts in kgs, humans may still have difficulty in understanding entity relationships. for instance, from fact "(data mining, facet of, database)" in wikidata, humans may guess data mining and database are related fields, but they cannot understand how exactly they are related, e.g, why is it a facet? and what is the facet? although techniques like knowledge graph reasoning @xcite @xcite or open relation extraction @xcite can represent more complex relationships to some extent, they do not fundamentally solve the limitations as discussed in @xcite . for instance, neither a multi-hop reasoning path in kgs nor a triple extracted by open relation extraction, e.g., (data mining methods, to be integrate within, the framework of traditional database systems), is easy to interpret. based on the above analysis, we propose a new form of modeling relationships between entities: deer (descriptive knowledge graph for explaining entity relationships). we define deer as a graph, where nodes are entities and edges are descriptive statements of entity relationships (refer to figure 1 for an example). deer is open since it does not require a pre-specified set of relation types. in principle, all entity relationships, either explicit or implicit, can be represented by deer, as long as they can be connected in a sentence -which is not possible for kgs. it is in- ## descriptive knowledge graph for explaining entity relationships deer is a graph representing entity relationships with sentence descriptions. formally, we define deer as a directed graph @xmath0, r}, where e is the set of entities and r is the set of relation description facts. a relation description fact is a triple (x, s, y), where x, @xmath1, respectively. s is a sentence describing the relationship between x and y (figure 1 ). to build deer, the first step is to collect entities and identify related entity pairs, which can be simply achieved by utilizing existing resources, e.g., wikipedia, and entity relevance analysis, e.g., cosine similarity of entity embeddings in wikipedia2vec @xcite . and then, we need to acquire high-quality relation descriptions for entity pairs. taking entity pair (machine learning, algorithm) as an example, a relation description of them can be s 1 in there are other requirements to ensure a good relation description, e.g., the sentence is coherent, grammatical, of reasonable length. compared to the above ones, these requirements are general requirements for any sentence, but not specific to our problem; therefore, we put less emphasis on them. to acquire relation descriptions that satisfy the above requirements, we propose a novel two-step # sentence s1 machine learning explores the study and construction of algorithms that can learn from and make predictions on data. s2 machine learning is employed in a range of computing tasks where designing and programming explicit, rule-based algorithms is infeasible. s3 machine learning includes algorithms that are adaptive or have adaptive variants, which usually means that the algorithm parameters are automatically adjusted according to statistics about the optimisation thus far. ## relation description extraction in this section, we introduce our approach for extracting entity relation descriptions from wikipedia according to the requirements discussed in section 3. ## relation description generation in the previous section, we extract relation descriptions for entity pairs with the analysis of dependency patterns and build an initial deer with wikipedia automatically. however, for some related entity pairs, there may not exist a sentence that contains both entities; and although such a sentence exists, it may not be extracted by the system. to solve this problem, in this section, we introduce relation description generation -generating relation descriptions for interesting entity pairs. we form relation description generation as a conditional text generation task: given two entities, generating a sentence describing the relationship between them with the initial deer. formally, we apply the knowledge-enhanced sequenceto-sequence formulation @xcite : given an entity pair (x, y) and an initial deer g 0 , the probability of the output relation description s is computed auto-regressively: to incorporate g 0 for generation, we propose relation description synthesizing (relationsyn). relationsyn consists of two processes: first retrieving relevant relation descriptions (reasoning paths) from the graph and then synthesizing them into a final relation description (figure 4 ). ## conclusion and discussion in this paper, we propose deer -an open and informative form of modeling relationships between entities. to avoid tremendous human efforts, we design a novel self-supervised learning approach to extract relation descriptions from wikipedia. to provide relation descriptions for related entity pairs whose relation descriptions are not extracted in the previous step, we study relation description generation by synthesizing relation descriptions in the retrieved reasoning paths. we believe that deer can not only serve as a direct application to help understand entity relationships but also be utilized as a knowledge source to facilitate related tasks such as relation extraction @xcite and knowledge graph completion @xcite .