Type-Constrained Representation Learning in Knowledge Graphs

Abstract

Abstract. Large knowledge graphs increasingly add value to various applications that require machines to recognize and understand queries and their semantics, as in search or question answering systems. Latent variable models have increasingly gained attention for the statistical modeling of knowledge graphs, showing promising results in tasks related to knowledge graph completion and cleaning. Besides storing facts about the world, schema-based knowledge graphs are backed by rich semantic descriptions of entities and relation-types that allow machines to understand the notion of things and their semantic relationships. In this work, we study how type-constraints can generally support the statistical modeling with latent variable models. More precisely, we integrated prior knowledge in form of type-constraints in various state of the art latent variable approaches. Our experimental results show that prior knowledge on relation-types significantly improves these models up to 77% in linkprediction tasks. The achieved improvements are especially prominent when a low model complexity is enforced, a crucial requirement when these models are applied to very large datasets. Unfortunately, typeconstraints are neither always available nor always complete e.g., they can become fuzzy when entities lack proper typing. We show that in these cases, it can be beneficial to apply a local closed-world assumption that approximates the semantics of relation-types based on observations made in the data.

Knowledge graph fact prediction via knowledge-enriched tensor factorization

“…Methods like RESCAL [9] and its schema-based extension [12] decompose a tensor into a shared factor matrix and a shared compact factor tensor [33]. To better model protein interaction networks and social network data, Krompass et al [13] imposed non-negativity constraints on these factors, but as we show empirically in Section 6, doing so increases the running time of the factorization and introduces scalability issues.…”

Relation path embedding in knowledge graphs

“…Another research direction focuses on improving the prediction performance by using prior knowledge in the form of relation-specific type constraints [7,16,30]. Note that each relation should contain Domain and Range fields to indicate the subject and object types, respectively.…”

“…By exploiting these limited rules, the harmful influence of a merely data-driven pattern can be avoided in part. For example, Type-constrained TransE [16] imposes these constraints on the global margin-loss function to better distinguish similar embeddings in latent space.…”

“…By exploiting the compositional path projection, RPE can embed each entity into proposed path spaces for better tackling various relations with multi-mapping properties. Moreover, we extend the relation-specific type constraints [16] to the novel path-specific type constraints to improve the prediction quality. Our model is evaluated on three benchmark datasets with link prediction and triple classification.…”

Regularizing Knowledge Graph Embeddings via Equivalence and Inversion Axioms

“…For instance, 71% of the people described in Freebase have no known place of birth, 75% have no known nationality, and the coverage for less used relations can be even lower (Dong et al, 2014). Similarly, in DBpedia, 66% of the persons are also missing a place of birth, while 58% of the scientists are missing a fact stating what they are known for (Krompaß et al, 2015).…”