Open Knowledge Graph Link Prediction with Segmented Embedding

Abstract

Open Knowledge Graph (OpenKG) link prediction is important for using OpenKGs in applications such as question answering and text comprehension. The noun phrases (NPs) and relation phrases in OpenKGs are not canonicalized, making OpenKG link prediction highly challenging. Existing methods addressing this problem infuse canonicalization information into knowledge graph embedding models. However, they still fail to fully exploit the semantics of NPs. First, two different NPs, even referring to the same entity, can carry different versions of information, which has been ignored by previous methods. Second, neighborhood information of NPs in OpenKGs has not been utilized, which contains abundant information for link prediction. Based on these observations, we propose the OpenKG Segmented Embedding (OKGSE) method. Specifically, to fully capture the dissimilarity of NPs belonging to the same cluster, we learn separate parts of embedding for both the NP cluster and NP. Meanwhile, we exploit neighborhood information by integrating graph context into the semantic matching score function. Extensive experiments across four benchmarks show that OKGSE can achieve state-of-the-art performance as well as effectively capture the unique semantics of each NP.