# Missing Movie Synergistic Completion across Multiple Isomeric Online Movie Knowledge Libraries

## TL;DR

This paper introduces a deep learning framework called IDEA to identify and rank missing movie entities across multiple online knowledge libraries, addressing the challenge of knowledge completion in rapidly growing data environments.

## Contribution

The paper proposes a novel deep learning framework, IDEA, for synergistic completion of multiple online knowledge libraries, specifically applied to movie data, with a comprehensive analysis of Douban and IMDB.

## Key findings

- IDEA effectively identifies missing entities across libraries.
- IDEA outperforms baseline methods in accuracy and ranking.
- The framework demonstrates scalability on real-world datasets.

## Abstract

Online knowledge libraries refer to the online data warehouses that systematically organize and categorize the knowledge-based information about different kinds of concepts and entities. In the era of big data, the setup of online knowledge libraries is an extremely challenging and laborious task, in terms of efforts, time and expense required in the completion of knowledge entities. Especially nowadays, a large number of new knowledge entities, like movies, are keeping on being produced and coming out at a continuously accelerating speed, which renders the knowledge library setup and completion problem more difficult to resolve manually. In this paper, we will take the online movie knowledge libraries as an example, and study the "Multiple aligned ISomeric Online Knowledge LIbraries Completion problem" (Miso-Klic) problem across multiple online knowledge libraries. Miso-Klic aims at identifying the missing entities for multiple knowledge libraries synergistically and ranking them for editing based on certain ranking criteria. To solve the problem, a thorough investigation of two isomeric online knowledge libraries, Douban and IMDB, have been carried out in this paper. Based on analyses results, a novel deep online knowledge library completion framework "Integrated Deep alignEd Auto-encoder" (IDEA) is introduced to solve the problem. By projecting the entities from multiple isomeric knowledge libraries to a shared feature space, IDEA solves the Miso-Klic problem via three steps: (1) entity feature space unification via embedding, (2) knowledge library fusion based missing entity identification, and (3) missing entity ranking. Extensive experiments done on the real-world online knowledge library dataset have demonstrated the effectiveness of IDEA in addressing the problem.