CCKS2016: CHINA CONFERENCE ON KNOWLEDGE GRAPH AND SEMANTIC COMPUTING (全国知识图谱与语义计算大会)
PROGRAM FOR TUESDAY, SEPTEMBER 20TH
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09:00-12:00 Session 4: 讲习班(Tutorial 3)
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Location: 1号楼三层银杏大厅(Building No. 1, Gingko Hall)
09:00
Understanding Short Texts

ABSTRACT. Billions of short texts are produced every day, in the form of search queries, ad keywords, tags, tweets, messenger conversations, social network posts, etc. Unlike documents, short texts have some unique characteristics which make them difficult to handle. First, short texts, especially search queries, do not always observe the syntax of a written language. This means traditional NLP techniques, such as syntactic parsing, do not always apply to short texts. Second, short texts contain limited context. The majority of search queries contain less than 5 words, and tweets can have no more than 140 characters. Because of the above reasons, short texts give rise to a significant amount of ambiguity, which makes them extremely difficult to handle. On the other hand, many applications, including search engines, ads, automatic question answering, online advertising, recommendation systems, etc., rely on short text understanding. In all these applications, the necessary first step is to transform an input text into a machine-interpretable representation, namely to “understand” the short text. A growing number of approaches leverage external knowledge to address the issue of inadequate contextual information that accompanies the short texts. These approaches can be classified into two categories: Explicit Representation Model (ERM) and Implicit Representation Model (IRM). In this tutorial, we will present a comprehensive overview of short text understanding based on explicit semantics (knowledge graph representation, acquisition, and reasoning) and implicit semantics (embedding and deep learning). Specifically, we will go over various techniques in knowledge acquisition, representation, and inferencing has been proposed for text understanding, and we will describe massive structured and semi-structured data that have been made available in the recent decade that directly or indirectly encode human knowledge, turning the knowledge representation problems into a computational grand challenge with feasible solutions insight.

10:30-11:00茶歇(Coffee Break)
12:00-14:00午餐(Lunch Break)5号楼大堂二层赏园餐厅
14:00-17:00 Session 5: 讲习班(Tutorial 4)
Location: 1号楼三层银杏大厅(Building No. 1, Gingko Hall)
14:00
知识图谱的摘要和集成

ABSTRACT. 大数据被认为是继信息化和互联网后整个信息革命的又一次高峰,然而如何将大数据转化为知识依然面临巨大挑战。知识图谱旨在描述真实世界中存在的各种实体或概念及其关系,它是数据语义链接的基石,有助于自然语言理解、数据挖掘等领域的发展。然而知识图谱的大规模、异构性等给基于知识图谱的应用带来了挑战。本次报告将首先介绍知识图谱、链接数据和本体的基础知识,接下来介绍知识图谱的摘要技术,其中重点介绍实体描述摘要、实体关联摘要的最新研究进展,最后介绍知识图谱的集成技术,其中重点介绍本体匹配和实体链接技术的最新研究进展。

15:00-15:30茶歇(Coffee Break)