1_CCKS_ATT_XifengYan.pdf

2019全国知识图谱与语义计算大会前沿讲习班PPT和讲义！1_CCKS_ATT_XifengYan

0_CCKS_ATT_Intro.pdf

2019全国知识图谱与语义计算大会前沿讲习班PPT和讲义！0_CCKS_ATT_Intro

CCKS 2019 meng.pdf

Emerging Techniques for Semantic Search: Obvious Advantages, but ChallengesToo

KG研究进展之NLP视角-何世柱.pdf

知识图谱研究进展 - 自然语言处理视角，描述知识图谱领域最新的发展

认知概念图谱构建与应用_8.17_张宁豫.pdf

认知概念图谱构建与应用，认知概念(Concept)是人类在认识过程中，从感性认识上升到理 性认识，把所感知的事物的共同本质特点抽象出来的一种表达。 认知概念不是非黑既白的，是认知场景下的符合实例本质的概率 分布。

WM_《关于FAQ-QA算法中台的思考和实践》-空崖2.pdf

来自达摩院智能服务的小蜜FAQ算法团队，关于FAQ-QA算法中台的思考和实践的报告

An Unrestricted-Hop Relation Extraction Framework for Knowledge-Based QA

In relation extraction for knowledge-based question answering, searching from one entity to another entity via a single relation is called “one hop”. In related work, an exhaustivesearchfromallone-hoprelations,two-hop relations,andsoontothemax-hoprelationsin the knowledge graph is necessary but expensive. Therefore, the number of hops is generally restricted to two or three. In this paper, we propose UHop, an unrestricted-hop framework which relaxes this restriction by use of a transition-based search framework to replace the relation-chain-based search one. We conduct experiments on conventional 1- and 2hop questions aswell as lengthy questions, including datasets such as WebQSP, PathQuestion, and Grid World. Results show that the proposed framework enables the ability to halt, works well with state-of-the-art models, achieves competitive performance without exhaustive searches, and opens the performance gap for long relation paths.

TuckER：Tensor Factorization for Knowledge Graph Completion.pdf

Knowledge graphs are structured representations of real world facts. However, they typically contain only a small subset of all possible facts. Link prediction is a task of inferring missing facts based on existing ones. We propose TuckER, a relatively simple but powerful linear model based on Tucker decomposition of the binary tensor representation of knowledge graph triples. TuckER outperforms all previous state-of-the-art models acrossstandardlinkpredictiondatasets. Weprove that TuckER is a fully expressive model, deriving the bound on its entity and relation embedding dimensionality for full expressiveness which is several orders of magnitude smaller than the bound of previous state-of-the-art models ComplEx and SimplE. We further show that several previously introducedlinearmodelscanbeviewedasspecial cases of TuckER.

Towards Data Poisoning Attack against Knowledge Graph Embedding.pdf

Knowledge graph embedding (KGE) is a technique for learning continuousembeddingsfor entities and relations in the knowledge graph. Due to its benefit to a variety of downstream tasks such as knowledge graph completion, question answering and recommendation, KGE has gained significant attention recently. Despite its effectiveness in a benign environment, KGE’s robustness to adversarialattacks is not well-studied. Existing attack methods on graph data cannot be directly applied to attack the embeddings of knowledge graph due to its heterogeneity. To fill this gap, we propose a collection of data poisoning attack strategies, which can effectively manipulate the plausibility of arbitrary targeted facts in a knowledge graph by adding or deleting facts on the graph. The effectiveness and efficiency of the proposed attack strategies are verified by extensive evaluations on two widely-used benchmarks.

Text Generation from Knowledge Graphs with Graph Transformers.pdf

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.