This paper proposes a method to construct Bilingual Movie Knowledge Graph (BMKG). The authors first builds Bilingual Movie Ontology (BMO) through a semi-automatic way, and aligns each data source with it in order to ensure semantic consistency of heterogeneous data sources. For entity linking, the proposed method makes best use of the field characteristics and calculate entity similarity based on both Word2Vec and TFIDF models, which greatly improve entity linking. For entity matching, a similarity flooding based algorithm is proposed, which utilizes the intrinsic links between the movie data sources, addressing the problem of similarity computation between cross-lingual entities. The experiment results show that the entity matching precision is over 90% when the threshold is above 0.75. In addition, a movie knowledge graph sharing platform is also built to provide open data access and query interface.

Graphene-based field-effect transistors (FETs) were fabricated by electron beam lithography and lift-off process and the photoresponse in the transistor was investigated. Significant photocurrents can be detected when the channel graphene near the metal contact is illuminated by a laser spot (λ = 633 nm). Both the magnitude and direction of the photocurrent can be effectively modulated by the back-gate voltage. In addition, the photocurrent will saturate by increasing the gate voltage. A maximum responsivity of 46.5 μA/W is achieved, which can be contributed to develop the novel graphene-based photodetectors.