39 research outputs found
Searching for Embeddings in a Haystack:Link Prediction on Knowledge Graphs with Subgraph Pruning
Embedding-based models of Knowledge Graphs (KGs) can be used to predict the existence of missing links by ranking the entities according to some likelihood scores. An exhaustive computation of all likelihood scores is very expensive if the KG is large. To counter this problem, we propose a technique to reduce the search space by identifying smaller subsets of promising entities. Our technique first creates embeddings of subgraphs using the embeddings from the model. Then, it ranks the subgraphs with some proposed ranking functions and considers only the entities in the top k subgraphs. Our experiments show that our technique is able to reduce the search space significantly while maintaining a good recall
MBA: A market-based approach to data allocation and migration for cloud database
With the coming shift to cloud computing, cloud database is emerging to provide database service over the Internet. In the cloud-based environment, data are distributed at internet scale and the system needs to handle a huge number of user queries simultaneously without delay. How data are distributed among the servers has a crucial impact on the query load distribution and the system response time. In this paper, we propose a market-based control method, called MBA, to achieve query load balance via reasonable data distribution. In MBA, database nodes are treated as traders in a market, and certain market rules are used to intelligently decide data allocation and migration. We built a prototype system and conducted extensive experiments. Experimental results show that the MBA method signicantly improves system performance in terms of average query response time and fairness
Interaction Embeddings for Prediction and Explanation in Knowledge Graphs
Knowledge graph embedding aims to learn distributed representations for
entities and relations, and is proven to be effective in many applications.
Crossover interactions --- bi-directional effects between entities and
relations --- help select related information when predicting a new triple, but
haven't been formally discussed before. In this paper, we propose CrossE, a
novel knowledge graph embedding which explicitly simulates crossover
interactions. It not only learns one general embedding for each entity and
relation as most previous methods do, but also generates multiple triple
specific embeddings for both of them, named interaction embeddings. We evaluate
embeddings on typical link prediction tasks and find that CrossE achieves
state-of-the-art results on complex and more challenging datasets. Furthermore,
we evaluate embeddings from a new perspective --- giving explanations for
predicted triples, which is important for real applications. In this work, an
explanation for a triple is regarded as a reliable closed-path between the head
and the tail entity. Compared to other baselines, we show experimentally that
CrossE, benefiting from interaction embeddings, is more capable of generating
reliable explanations to support its predictions.Comment: This paper is accepted by WSDM201