## 1 Introduction  
A knowledge base (KB) [1] stores a lot of information, which is complex and structured; they describe things (or entities) and their relationships. KB offers a more readable knowledge network for a machine and provides a more natural way to obtain abundant underlying knowledge. Freebase [2] is one such knowledge base that describes and organizes more than 3 billion facts in a consistent ontology. In fact, KB is usually represented as triples [3], such as, (subject, relation, object), where the subject and object represent entities, and the relation describes the semantic relations between subject and object. These triples are often referred to as facts and can be used for answering questions. For example, the triple ([PERSON], President, America) can be used to answer the question \"Who is the president of America\". KB is increasingly used for building question answering systems [4; 5]. Knowledge base question answering (KBQA) aims to analyze the semantics of natural language questions and return accurate answer from the knowledge base. At present, the methods proposed to tackle the KBQA task can be roughly categorized into two groups: semantic parsing (SP) methods and information retrieval (IR) methods. SP-based methods [6] aim to transform natural language problems into logical expressions through semantic analysis, then they are transformed into a query language such as SPARQL to retrieve the knowledge base and obtain the answer [7]. Although many SP-based methods can achieve good results in the limited domain, many important components in these works, such as vocabularies and rulesets in Combinatory categorial grammar (CCG) [8], are written manually. Traditional semantic parses [9] require labeled training data and are limited to narrow domains with a small number of logical predicates, but manually labeling data is time-consuming and laborious. Recent studies handle these limitations through the construction of hand-crafted rules or features [6; 10], schema matching [11], and using weak supervision from external resources [12]. SP-based methods are still based on symbolic logic and lack flexibility. When analyzing question semantics, it will be affected by the semantic differences between symbols. IR-based methods directly retrieve answers from the KB in light of the information conveyed in the questions. These IR-based methods can adapt better to large and complex KBs, as they do not need hand-made rules. In recent years, with the rapid development of deep learning technology, deep learning is used more and more in KBQA. On the basis of IR-based approaches, many embedding-based methods [13; 14] have been proposed and have shown promising results. Compared with the traditional KBQA methods based on symbols, the KBQA method based on representation learning [14] is more robust, and it has gradually exceeded the traditional method in effect. These methods adopt various ways to encode questions and KB subgraphs into a common embedding space, then directly match them in that space, finally typically trained in an end-to-end manner. Although the above methods have shown good results, they are not satisfactory in some specific problems, such as questions with implicit temporal constraints. In order to solve this problem, we introduce external knowledge on the basis of Bidirectional Attentive Memory Networks (BAMnet) [15], called Temporal Attention Networks (TAnet), that captures the implicit temporal information in question. We use a novel bidirectional attentive mechanism to obtain the temporal information in question in the light of external knowledge. In the experiments, we prove that our method not only shows better results in the original datasets, but also in the data with implicit temporal constraints.