没有合适的资源?快使用搜索试试~ 我知道了~
首页整合常识知识图谱:原则、应用与影响
整合常识知识图谱:原则、应用与影响
需积分: 9 5 下载量 109 浏览量
更新于2024-07-15
1
收藏 1.09MB PDF 举报
《常识知识图谱概述》是一篇综述论文,由Filip Ilievski、Pedro Szekely和Bin Zhang共同撰写,来自南加州大学的信息科学研究所。该研究关注的是常识性知识在自然语言理解、计算机视觉和知识图谱等领域的应用。这些领域中的知识源各自拥有互补的见解和模型,但因为关注点的不同、建模方法的多样性以及知识之间的稀疏重叠,将它们有效地整合起来是一项挑战。 论文的核心目标是提出并遵循五个原则来整合这些关键的常识知识源,从而构建一个首屈一指的集成常识知识图谱(CSKG)。CSKG的创建不仅仅是简单地将多个数据集拼接,而是经过精心设计的整合过程,旨在提供一个连贯且丰富的知识网络,这对于后续的自然语言处理任务至关重要。 作者们展示了CSKG的结构分析,证明了它是一个高度连接的图谱,其嵌入技术不仅提供了访问知识图谱的有效入口,还为推理任务提供了有力的支持。通过实验,他们证实了CSKG在检索推理证据和预训练语言模型以实现更广泛下游推理任务方面的积极作用。 此外,论文公开了CSKG和所有相关的嵌入模型,以促进学术界和业界的研究者们进一步探索和利用这些常识知识资源。这一共享行为有助于推动常识知识图谱研究的标准化和进步,同时也为人工智能系统的普遍理解和智能决策提供了强有力的知识基础。 《常识知识图谱概述》论文不仅阐述了常识知识整合的重要性,还展示了如何通过实践构建和利用这样一个知识图谱,以及其对提升AI系统性能的实际贡献。对于任何关注知识表示学习、自然语言处理或知识驱动的AI开发者来说,这篇论文都是值得深入研究的宝贵参考资料。
资源详情
资源推荐
![](https://csdnimg.cn/release/download_crawler_static/13765299/bg4.jpg)
4 Filip Ilievski, Pedro Szekely, and Bin Zhang
ConceptNet. Nevertheless, these high-quality mappings provide an opening for
consolidation of commonsense knowledge, a goal we pursue in this paper.
2.2 Challenges
Combining these sources in a single graph faces three key challenges:
1. The sources follow different knowledge modeling approaches. One such
difference concerns the relation set: there are very few relations in ConceptNet
and WordNet, but (tens of) thousands of them in Wikidata and Visual Genome.
Consolidation requires a global decision on how to model the relations. The gran-
ularity of knowledge is another factor of variance. While regular RDF triples fit
some sources (e.g., ConceptNet), representing entire frames (e.g., in FrameNet),
event conditions (e.g., in ATOMIC), or compositional image data (e.g., Visual
Genome) might benefit from a more open format. An ideal representation would
support the entire granularity spectrum.
2. As a number of these sources have been created to support natural language
applications, they often contain imprecise descriptions. Natural language
phrases are often the main node types in the provided knowledge sources, which
provides the benefit of easier access for natural language algorithms, but it intro-
duces ambiguity which might be undesired from a formal semantics perspective.
An ideal representation would consolidate various phrasings that share a concept
or a referent, while still allowing easy and efficient access to these concepts based
on their natural language labels or aliases.
3. Although these sources contain links to existing ones, we observe sparse
overlap. As these external links are typically to WordNet, and vary in terms
of their version (3.0 or 3.1) or target (lemma or synset), the sources are still
disjoint and establishing (identity) connections is difficult. Bridging these gaps,
through optimally leveraging existing links, or extending them with additional
ones automatically, is a modeling and integration challenge.
Previous efforts that combine commonsense resources exist. A unidirectional
manual mapping from VerbNet classes to WordNet and FrameNet is provided
by the Unified Verb Index [31]. The Predicate Matrix [6] has a full automatic
mapping between lexical resources, including FrameNet, WordNet, and Verb-
Net. PreMOn [5] formalizes these in RDF. In [19], the authors produce partial
mappings between WordNet and Wikipedia/DBpedia. BabelNet [21] integrates
several commonsense sources, but extending BabelNet is prevented by its license.
Recent systems integrate parts of these sources in an ad-hoc manner to reason
on a downstream task, e.g., [35] combine edges from Visual Genome, WordNet,
and ConceptNet in a neural network that produces a scene graph from an image.
3 The Common Sense Knowledge Graph
3.1 Principles
To address the aforementioned challenges, we devise principles and a respective
representation format that are driven by pragmatic goals of simplicity, modular-
ity, and utility. Specifically, it should be simple to integrate the graph represented
剩余15页未读,继续阅读
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://profile-avatar.csdnimg.cn/0f5d51af96824612be4ad2fd47b10fbf_syp_net.jpg!1)
syp_net
- 粉丝: 158
- 资源: 1187
上传资源 快速赚钱
我的内容管理 展开
我的资源 快来上传第一个资源
我的收益
登录查看自己的收益我的积分 登录查看自己的积分
我的C币 登录后查看C币余额
我的收藏
我的下载
下载帮助
![](https://csdnimg.cn/release/wenkucmsfe/public/img/voice.245cc511.png)
最新资源
- 岩石滑动与断层冲击地压:声发射特征分析
- 新时代煤炭工业八大战略新取向剖析
- 开采强度对华亭矿区冲击地压危险性的影响分析
- 新安煤矿探采对比:影响安全生产的关键因素与技术支撑
- 210MW循环流化床锅炉引风机变频改造优化与节能效果
- 浅埋煤层开采沉陷预测:方法应用与影响分析
- 优化巷道过陷落柱支护方案:数值模拟与现场试验
- 坚硬顶板定向断裂爆破技术:安全高效采煤新方案
- 北航2019算法课作业解析:投资策略与凸子多边形优化
- 断层破碎带垮冒下堆积体边界抗力分布关键影响因素
- 哈拉沟选煤厂优化末煤系统提高效率与处理能力
- 瓦斯涌出影响下采动煤体蠕变失稳研究
- 提升煤矿装载效率的可控箕斗定量装载设备设计
- 水分调控下烟煤瓦斯吸附特性对比研究
- 重介质悬浮液密度控制硬件设计与选型探讨
- 坚硬顶板巷道支护参数设计:现场测试与数值分析
资源上传下载、课程学习等过程中有任何疑问或建议,欢迎提出宝贵意见哦~我们会及时处理!
点击此处反馈
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035711.png)
![](https://img-home.csdnimg.cn/images/20220527035111.png)
安全验证
文档复制为VIP权益,开通VIP直接复制
![](https://csdnimg.cn/release/wenkucmsfe/public/img/green-success.6a4acb44.png)