To enhance research on multimodal knowledge base and multimodal information processing, we propose a new task called multimodal entity tagging (MET) with a multimodal knowledge base (MKB). We also develop a dataset for the problem using an existing MKB. In an MKB, there are entities and their associated texts and images. In MET, given a text-image pair, one uses the information in the MKB to automatically identify the related entity in the text-image pair. We solve the task by using the information retrieval paradigm and implement several baselines using state-of-the-art methods in NLP and CV. We conduct extensive experiments and make analyses on the experimental results. The results show that the task is challenging, but current technologies can achieve relatively high performance. We will release the dataset, code, and models for future research.

In this paper we introduce the notion of mapping-based knowledge base (MKB) to formalize the situation where both the extensional and the intensional level of the ontology are determined by suitable mappings to a set of (relational) data sources. This allows for making the intensional level of the ontology as dynamic as traditionally the extensional level is. To do so, we resort to the meta-modeling capabilities of higher-order Description Logics, which allow us to see concepts and roles as individuals, and vice versa. The challenge in this setting is to design tractable query answering algorithms. Besides the definition of MKBs, our main result is that answering instance queries posed to MKBs expressed in Hi(DL-LiteR) can be done efficiently. In particular, we define a query rewriting technique that produces first-order (SQL) queries to be posed to the data sources.