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Knowledge graphs are useful resources for numerous AI applications, but they are far from completeness. Previous work such as TransE, TransH and TransR/CTransR regard a relation as translation from head entity to tail entity and the CTransR achieves state-of-the-art performance. In this paper , we propose a more fine-grained model named TransD, which is an(More)
The representation of a knowledge graph (<i>KG</i>) in a latent space recently has attracted more and more attention. To this end, some proposed models (e.g., TransE) embed entities and relations of a KG into a "<i>point</i>" vector space by optimizing a global loss function which ensures the scores of positive triplets are higher than negative ones. We(More)
We present a question answering system (CASIA@V2) over Linked Data (DBpedia), which translates natural language questions into structured queries automatically. Existing systems usually adopt a pipeline framework, which contains four major steps: 1) Decomposing the question and detecting candidate phrases; 2) mapping the detected phrases into semantic items(More)
We present a question answering system (CASIA) over Linked Data (DBpedia), which focuses on construct a bridge between the users and the Linked Data. Based on the Linked Data consisting of subject-property-object (SPO) triples, each natural language question firstly is transformed into a triple-based representation (Query Triple). Then, the corresponding(More)
The authors analyze three critical components in training word embeddings: model, corpus, and training parameters. They systematize existing neural-network-based word embedding methods and experimentally compare them using the same corpus. They then evaluate each word embedding in three ways: analyzing its semantic properties, using it as a feature for(More)
Question Answering over Linked Data (QALD) aims to evaluate a question answering system over structured data, the key objective of which is to translate questions posed using natural language into structured queries. This technique can help common users to directly access open-structured knowledge on the Web and, accordingly, has attracted much attention.(More)
Community question answering (CQA) has become an increasingly popular research topic. In this paper, we focus on the problem of question retrieval. Question retrieval in CQA can automatically find the most relevant and recent questions that have been solved by other users. However, the word ambiguity and word mismatch problems bring about new challenges for(More)
Question answering over knowledge bases is a challenging task for next-generation search engines. The core of this task is to understand the meaning of questions and translate them into structured language-based queries. Previous research has focused on a specific knowledge base with a constrained domain, but with the increase in the size and domain of(More)