EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph

Pollacci, Laura

doi:10.48550/arxiv.2203.09159

Cited by 1 publication

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“…• Academic KB is based on the Microsoft Academic Knowledge Graph (MAKG) [7], containing over 8 billion triples on scientific publications and entities. Due to the absence of KBQA benchmarks related the MAKG, we collect 100 question patterns from other Academic datasets [21] and replace concrete entities for creating questions and answers.…”

Section: Experiments 51 Experimental Setupmentioning

confidence: 99%

Commercialization of state-owned broadcast networks in China

Yan¹

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Visual contents, such as illustrations and images, play a big role in product manual understanding. Existing Product Manual Question Answering (PMQA) datasets tend to ignore visual contents and only retain textual parts. In this work, to emphasize the importance of multimodal contents, we propose a Multimodal Product Manual Question Answering (MPMQA) task. For each question, MPMQA requires the model not only to process multimodal contents but also to provide multimodal answers. To support MPMQA, a large-scale dataset PM209 is constructed with human annotations, which contains 209 product manuals from 27 well-known consumer electronic brands. Human annotations include 6 types of semantic regions for manual contents and 22,021 pairs of question and answer. Especially, each answer consists of a textual sentence and related visual regions from manuals. Taking into account the length of product manuals and the fact that a question is always related to a small number of pages, MP-MQA can be naturally split into two subtasks: retrieving most related pages and then generating multimodal answers. We further propose a unified model that can perform these two subtasks all together and achieve comparable performance with multiple task-specific models. The PM209 dataset is available at https://github.com/AIM3-RUC/MPMQA.

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Section: Experiments 51 Experimental Setupmentioning

confidence: 99%

Commercialization of state-owned broadcast networks in China

Yan¹

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EMAKG: An Enhanced Version Of The Microsoft Academic Knowledge Graph

Abstract: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Cited by 1 publication

References 39 publications

Commercialization of state-owned broadcast networks in China

Commercialization of state-owned broadcast networks in China

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