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Semantic network  语义网络

From Wikipedia, the free encyclopedia
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Not to be confused with semantic neural network.
不要与语义神经网络混淆。

Example of a semantic network
语义网络示例
Part of a series on
语义网络 - 维基百科的一部分
Network science  网络科学
Internet_map_1024.jpg
Network types  网络类型
Graphs  图
Features  功能
Types  类型
Models  模型
Topology  拓扑学
Dynamics  动态
  • Lists  列表
  • Categories  类别
detail of a Tree of Knowledge after Diderot & d'Alembert's Encyclopédie, by Chrétien Frédéric Guillaume Roth
Information mapping  信息映射
Topics and fields  主题和领域
Node–link approaches  节点-链接方法
See also  参见

A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts,[1] mapping or connecting semantic fields. A semantic network may be instantiated as, for example, a graph database or a concept map. Typical standardized semantic networks are expressed as semantic triples.
语义网络或框架网络是一种知识库，它以网络的形式表示概念之间的语义关系。这通常用作知识表示的一种形式。它是一个由顶点和边组成的定向或无向图，其中顶点代表概念，边代表概念之间的语义关系， [1] 映射或连接语义领域。语义网络可以实例化为，例如，图数据库或概念图。典型的标准化语义网络以语义三元组的形式表达。

Semantic networks are used in neurolinguistics and natural language processing applications such as semantic parsing[2] and word-sense disambiguation.[3] Semantic networks can also be used as a method to analyze large texts and identify the main themes and topics (e.g., of social media posts), to reveal biases (e.g., in news coverage), or even to map an entire research field.[4]
语义网络用于神经语言学和自然语言处理应用，如语义解析和词义消歧。语义网络还可以用作分析大量文本、识别主要主题和话题（例如社交媒体帖子）、揭示偏见（例如新闻报道）或甚至映射整个研究领域的方法。

History  历史

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Examples of the use of semantic networks in logic, directed acyclic graphs as a mnemonic tool, dates back centuries, the earliest documented use being the Greek philosopher Porphyry's commentary on Aristotle's categories in the third century AD.
语义网络在逻辑、作为记忆辅助工具的有向无环图中的应用，其历史可以追溯到几个世纪以前，最早的文献记载是公元 3 世纪希腊哲学家波菲利对亚里士多德范畴的注释。

In computing history, "Semantic Nets" for the propositional calculus were first implemented for computers by Richard H. Richens of the Cambridge Language Research Unit in 1956 as an "interlingua" for machine translation of natural languages,[5] although the importance of this work and the Cambridge Language Research Unit was only belatedly realized.
在计算机历史中，“语义网”最初由剑桥语言研究单位的理查德·H·里奇斯于 1956 年实现，用于命题演算，作为自然语言机器翻译的“中介语”，尽管这项工作和剑桥语言研究单位的重要性直到后来才被认识到。

Semantic networks were also independently implemented by Robert F. Simmons[6] and Sheldon Klein, using the first-order predicate calculus as a base, after being inspired by a demonstration of Victor Yngve. The "line of research was originated by the first President of the Association for Computational Linguistics, Victor Yngve, who in 1960 had published descriptions of algorithms for using a phrase structure grammar to generate syntactically well-formed nonsense sentences. Sheldon Klein and I about 1962–1964 were fascinated by the technique and generalized it to a method for controlling the sense of what was generated by respecting the semantic dependencies of words as they occurred in text."[7] Other researchers, most notably M. Ross Quillian[8] and others at System Development Corporation helped contribute to their work in the early 1960s as part of the SYNTHEX project. It's these publications at System Development Corporation that most modern derivatives of the term "semantic network" cite as their background. Later prominent works were done by Allan M. Collins and Quillian (e.g., Collins and Quillian;[9][10] Collins and Loftus[11] Quillian[12][13][14][15]). Still later in 2006, Hermann Helbig fully described MultiNet.[16]
语义网络也由罗伯特·F·西蒙斯和舍伦德·克莱因独立实现，他们受到维克多·英格演示的启发，以一阶谓词演算为基础。这一研究路线最初由计算语言学协会第一任主席维克多·英格发起，他在 1960 年发表了使用短语结构语法生成语法上正确但无意义的句子的算法描述。舍伦德·克莱因和我大约在 1962-1964 年间对这一技术着迷，并将其推广为一种控制生成内容意义的方法，即通过尊重文本中词语出现的语义依存关系来实现。其他研究人员，特别是 M.罗斯·奎利安和系统开发公司的其他研究人员，在 20 世纪 60 年代初作为 SYNTHEX 项目的一部分，帮助贡献了他们的工作。现代“语义网络”术语的大多数衍生作品都引用系统开发公司的这些出版物作为其背景。后来的重要工作由艾伦·M·柯林斯和奎利安完成（例如，柯林斯和奎利安；柯林斯和洛夫特斯；奎利安；）。 2006 年稍晚些时候，赫尔曼·赫尔比格全面描述了 MultiNet。

In the late 1980s, two universities in the Netherlands, Groningen and Twente, jointly began a project called Knowledge Graphs, which are semantic networks but with the added constraint that edges are restricted to be from a limited set of possible relations, to facilitate algebras on the graph.[17] In the subsequent decades, the distinction between semantic networks and knowledge graphs was blurred.[18][19] In 2012, Google gave their knowledge graph the name Knowledge Graph.
20 世纪 80 年代末，荷兰的两所大学格罗宁根和特文特联合启动了一个名为知识图谱的项目，这些是语义网络，但增加了限制，即边被限制在可能的有限关系集中，以方便图上的代数运算。 [17] 在接下来的几十年里，语义网络和知识图谱之间的区别变得模糊。 [18] [19] 2012 年，谷歌将他们的知识图谱命名为知识图谱。

The semantic link network was systematically studied as a semantic social networking method. Its basic model consists of semantic nodes, semantic links between nodes, and a semantic space that defines the semantics of nodes and links and reasoning rules on semantic links. The systematic theory and model was published in 2004.[20] This research direction can trace to the definition of inheritance rules for efficient model retrieval in 1998[21] and the Active Document Framework ADF.[22] Since 2003, research has developed toward social semantic networking.[23] This work is a systematic innovation at the age of the World Wide Web and global social networking rather than an application or simple extension of the Semantic Net (Network). Its purpose and scope are different from that of the Semantic Net (or network).[24] The rules for reasoning and evolution and automatic discovery of implicit links play an important role in the Semantic Link Network.[25][26] Recently it has been developed to support Cyber-Physical-Social Intelligence.[27] It was used for creating a general summarization method.[28] The self-organised Semantic Link Network was integrated with a multi-dimensional category space to form a semantic space to support advanced applications with multi-dimensional abstractions and self-organised semantic links[29][30] It has been verified that Semantic Link Network play an important role in understanding and representation through text summarisation applications.[31][32] Semantic Link Network has been extended from cyberspace to cyber-physical-social space. Competition relation and symbiosis relation as well as their roles in evolving society were studied in the emerging topic: Cyber-Physical-Social Intelligence[33]
语义链接网络作为一种语义社交网络方法被系统研究。其基本模型由语义节点、节点间的语义链接以及定义节点和链接语义和推理规则的语义空间组成。这一系统理论和模型于 2004 年发表。 [20] 这一研究方向可以追溯到 1998 年对高效模型检索的继承规则的定义 [21] 以及 Active Document Framework ADF。 [22] 自 2003 年以来，研究已发展到社交语义网络。 [23] 这项工作是在万维网和全球社交网络时代的系统性创新，而不是语义网（网络）的应用或简单扩展。其目的和范围与语义网（或网络）不同。 [24] 推理和演化的规则以及隐含链接的自动发现对语义链接网络起着重要作用。 [25] [26] 最近它已发展到支持网络物理社会智能。 [27] 它被用于创建一种通用摘要方法。 自组织语义链接网络与多维分类空间集成，形成一个语义空间，以支持具有多维抽象和自组织语义链接的高级应用。已经证实，语义链接网络在通过文本摘要应用进行理解和表示中发挥着重要作用。语义链接网络已从网络空间扩展到物理-社会空间。在新兴主题“物理-社会智能”中，研究了竞争关系和共生关系以及它们在演变社会中的作用。

More specialized forms of semantic networks has been created for specific use. For example, in 2008, Fawsy Bendeck's PhD thesis formalized the Semantic Similarity Network (SSN) that contains specialized relationships and propagation algorithms to simplify the semantic similarity representation and calculations.[34]
更专业的语义网络形式已被创建用于特定用途。例如，在 2008 年，Fawsy Bendeck 的博士论文正式化了语义相似性网络（SSN），该网络包含专门的关系和传播算法，以简化语义相似性的表示和计算。

Basics of semantic networks
语义网络基础

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A semantic network is used when one has knowledge that is best understood as a set of concepts that are related to one another.
语义网络用于当一个人拥有的知识最好被理解为一组相互关联的概念时。

Most semantic networks are cognitively based. They consist of arcs (spokes) and nodes (hubs) which can be organized into a taxonomic hierarchy. Different semantic networks can also be connected by bridge nodes. Semantic networks contributed to the ideas of spreading activation, inheritance, and nodes as proto-objects.
大多数语义网络都是基于认知的。它们由弧（辐条）和节点（中心）组成，可以组织成分类层次。不同的语义网络也可以通过桥接节点相互连接。语义网络对传播激活、继承以及节点作为原初对象的想法做出了贡献。

One process of constructing semantic networks, known also as co-occurrence networks, includes identifying keywords in the text, calculating the frequencies of co-occurrences, and analyzing the networks to find central words and clusters of themes in the network.[35]
构建语义网络的一种方法，也称为共现网络，包括在文本中识别关键词、计算共现频率，并分析网络以找到网络中的中心词和主题集群。

In linguistics  语言学

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In the field of linguistics, semantic networks represent how the human mind handles associated concepts. Typically, concepts in a semantic network can have one of two different relationships: either semantic or associative.
在语言学领域，语义网络表示人类大脑如何处理相关概念。通常，语义网络中的概念可以有两种不同的关系：要么是语义关系，要么是联想关系。

If semantic in relation, the two concepts are linked by any of the following semantic relationships: synonymy, antonymy, hypernymy, hyponymy, holonymy, meronymy, metonymy, or polysemy. These are not the only semantic relationships, but some of the most common.
如果语义相关，两个概念通过以下任何一种语义关系相连：同义关系、反义关系、上位关系、下位关系、整体关系、部分关系、转喻关系或多义关系。这些不是唯一的语义关系，但是最常见的其中一些。

If associative in relation, the two concepts are linked based on their frequency to occur together. These associations are accidental, meaning that nothing about their individual meanings requires them to be associated with one another, only that they typically are. Examples of this would be pig and farm, pig and trough, or pig and mud. While nothing about the meaning of pig forces it to be associated with farms, as pigs can be wild, the fact that pigs are so frequently found on farms creates an accidental associated relationship. These thematic relationships are common within semantic networks and are notable results in free association tests.
如果关联关系是联想的，则两个概念基于它们共同出现的频率而相互链接。这些关联是偶然的，这意味着它们各自的含义没有任何需要它们相互关联的原因，只是它们通常是这样。这种关联的例子包括猪和农场、猪和水槽或猪和泥。尽管猪的含义并没有强迫它与农场相关联，因为猪可以是野生的，但猪在农场中如此频繁的出现，形成了一种偶然的关联关系。这些主题关系在语义网络中很常见，并且在自由联想测试中是显著的结果。

As the initial word is given, activation of the most closely related concepts begin, spreading outward to the lesser associated concepts. An example of this would be the initial word pig prompting mammal, then animal, and then breathes. This example shows that taxonomic relationships are inherent within semantic networks. The most closely related concepts typically share semantic features, which are determinants of semantic similarity scores. Words with higher similarity scores are more closely related, thus have higher probability of being a close word in the semantic network.
随着初始词给出，最密切相关念的激活开始，向外扩散至关联度较低的概念。例如，初始词“猪”会引发“哺乳动物”，然后是“动物”，最后是“呼吸”。这个例子表明，分类关系是语义网络内在的。最密切相关念通常共享语义特征，这些特征是语义相似度分数的决定因素。相似度分数较高的词更密切相关，因此更有可能在语义网络中成为近义词。

These relationships can be suggested into the brain through priming, where previous examples of the same relationship are shown before the target word is shown. The effect of priming on a semantic network linking can be seen through the speed of the reaction time to the word. Priming can help to reveal the structure of a semantic network and which words are most closely associated with the original word.
这些关系可以通过启动（priming）引入大脑，即在显示目标词之前先展示相同关系的先前例子。可以通过对词语反应时间的速度来观察启动对语义网络链接的影响。启动有助于揭示语义网络的结构以及哪些词语与原始词语最为紧密相关。

Disruption of a semantic network can lead to a semantic deficit (not to be confused with as semantic dementia).
语义网络的破坏可能导致语义缺陷（不要与语义痴呆混淆）。

In the brain  在大脑中

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There exists physical manifestation of semantic relationships in the brain as well. Category-specific semantic circuits show that words belonging to different categories are processed in circuits differently located throughout the brain. For example, the semantic circuits for a word associated with the face or mouth (such as lick) is located in a different place of the brain than a word associated with the leg or foot (such as kick). This is a primary result of a 2013 study published by Friedemann Pulvermüller[citation needed]. These semantic circuits are directly tied to their sensorimotor areas of the brain. This is known as embodied semantics, a subtopic of embodied language processing.
大脑中还存在语义关系的物理表现。特定类别语义回路表明，属于不同类别的词语在大脑中不同的回路中被处理。例如，与面部或嘴巴相关的词语（如舔）的语义回路位于大脑的不同位置，而与腿部或脚部相关的词语（如踢）的语义回路则位于大脑的另一处。这是 2013 年 Friedemann Pulvermüller 发表的一项研究的主要结果。这些语义回路直接与大脑的感官运动区域相连。这被称为具身语义，是具身语言处理的一个子主题。

If brain damage occurs, the normal processing of semantic networks could be disrupted, leading to preference into what kind of relationships dominate the semantic network in the mind.
如果发生脑损伤，语义网络的正常处理可能会被破坏，导致在心中占主导地位的关系类型偏好。

Examples  示例

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In Lisp  Lisp

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The following code shows an example of a semantic network in the Lisp programming language using an association list.
以下代码展示了使用关联列表在 Lisp 编程语言中实现的语义网络示例。

(setq *database*
'((canary  (is-a bird)
           (color yellow)
           (size small))
  (penguin (is-a bird)
           (movement swim))
  (bird    (is-a vertebrate)
           (has-part wings)
           (reproduction egg-laying))))

To extract all the information about the "canary" type, one would use the assoc function with a key of "canary".[36]
提取关于“金丝雀”类型的所有信息，可以使用带有“金丝雀”键的 assoc 函数。 [36]

WordNet  WordNet - 同义词词典

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Main article: WordNet  主条目：WordNet

An example of a semantic network is WordNet, a lexical database of English. It groups English words into sets of synonyms called synsets, provides short, general definitions, and records the various semantic relations between these synonym sets. Some of the most common semantic relations defined are meronymy (A is a meronym of B if A is part of B), holonymy (B is a holonym of A if B contains A), hyponymy (or troponymy) (A is subordinate of B; A is kind of B), hypernymy (A is superordinate of B), synonymy (A denotes the same as B) and antonymy (A denotes the opposite of B).
语义网络的一个例子是 WordNet，这是一个英语词汇数据库。它将英语单词分组为称为同义词集的集合，提供简短的一般定义，并记录这些同义词集之间的各种语义关系。定义的一些最常见的语义关系包括组成关系（如果 A 是 B 的组成部分，则称 A 是 B 的组成关系），整体关系（如果 B 包含 A，则称 B 是 A 的整体关系），下位关系（或转换关系）（A 是 B 的下位；A 是 B 的一种），上位关系（A 是 B 的上位），同义关系（A 表示与 B 相同）和反义关系（A 表示与 B 相反）。

WordNet properties have been studied from a network theory perspective and compared to other semantic networks created from Roget's Thesaurus and word association tasks. From this perspective the three of them are a small world structure.[37]
WordNet 属性已从网络理论的角度进行研究，并与从 Roget 同义词典和词语联想任务中创建的其他语义网络进行比较。从这个角度来看，这三个网络都是一个小世界结构。

Other examples  其他示例

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It is also possible to represent logical descriptions using semantic networks such as the existential graphs of Charles Sanders Peirce or the related conceptual graphs of John F. Sowa.[1] These have expressive power equal to or exceeding standard first-order predicate logic. Unlike WordNet or other lexical or browsing networks, semantic networks using these representations can be used for reliable automated logical deduction. Some automated reasoners exploit the graph-theoretic features of the networks during processing.
语义网络也可以用来表示逻辑描述，例如查尔斯·桑德斯·皮尔斯的存在图或约翰·F·索瓦的相关概念图。这些表示的表达能力等于或超过标准的一阶谓词逻辑。与 WordNet 或其他词汇或浏览网络不同，使用这些表示的语义网络可用于可靠的自动化逻辑推理。一些自动化推理器在处理过程中利用网络的图论特征。

Other examples of semantic networks are Gellish models. Gellish English with its Gellish English dictionary, is a formal language that is defined as a network of relations between concepts and names of concepts. Gellish English is a formal subset of natural English, just as Gellish Dutch is a formal subset of Dutch, whereas multiple languages share the same concepts. Other Gellish networks consist of knowledge models and information models that are expressed in the Gellish language. A Gellish network is a network of (binary) relations between things. Each relation in the network is an expression of a fact that is classified by a relation type. Each relation type itself is a concept that is defined in the Gellish language dictionary. Each related thing is either a concept or an individual thing that is classified by a concept. The definitions of concepts are created in the form of definition models (definition networks) that together form a Gellish Dictionary. A Gellish network can be documented in a Gellish database and is computer interpretable.
语义网络的其他例子是 Gellish 模型。Gellish 英语及其 Gellish 英语词典是一种正式语言，定义为概念及其名称之间关系的一个网络。Gellish 英语是自然英语的正式子集，就像 Gellish 荷兰语是荷兰语的正式子集一样，而多种语言共享相同的概念。其他 Gellish 网络由用 Gellish 语言表达的知识模型和信息模型组成。Gellish 网络是事物之间（二元）关系的一个网络。网络中的每个关系都是事实的表达，该事实由关系类型分类。每个关系类型本身是 Gellish 语言词典中定义的概念。每个相关的事物要么是一个概念，要么是一个由概念分类的个体事物。概念的定义以定义模型（定义网络）的形式创建，这些模型共同构成 Gellish 词典。Gellish 网络可以用 Gellish 数据库进行文档记录，并且是计算机可解释的。

SciCrunch is a collaboratively edited knowledge base for scientific resources. It provides unambiguous identifiers (Research Resource IDentifiers or RRIDs) for software, lab tools etc. and it also provides options to create links between RRIDs and from communities.
SciCrunch 是一个科学资源的协作编辑知识库。它为软件、实验室工具等提供明确的标识符（研究资源标识符或 RRID），并提供创建 RRID 与社区之间链接的选项。

Another example of semantic networks, based on category theory, is ologs. Here each type is an object, representing a set of things, and each arrow is a morphism, representing a function. Commutative diagrams also are prescribed to constrain the semantics.
语义网络的一个例子，基于范畴论的是 ologs。在这里，每种类型都是一个对象，代表一组事物，每个箭头是一个态射，代表一个函数。还规定了交换图来约束语义。

In the social sciences people sometimes use the term semantic network to refer to co-occurrence networks.[38][39] The basic idea is that words that co-occur in a unit of text, e.g. a sentence, are semantically related to one another. Ties based on co-occurrence can then be used to construct semantic networks. This process includes identifying keywords in the text, constructing co-occurrence networks, and analyzing the networks to find central words and clusters of themes in the network. It is a particularly useful method to analyze large text and big data.[40]
在社会科学中，人们有时使用“语义网络”一词来指代共现网络。基本思想是，在文本单元（例如句子）中共同出现的词语在语义上是相互关联的。基于共现的关联可以用来构建语义网络。这个过程包括识别文本中的关键词、构建共现网络以及分析网络以找到网络中的中心词和主题簇。这是一种特别有用的分析大量文本和大数据的方法。

Software tools  软件工具

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There are also elaborate types of semantic networks connected with corresponding sets of software tools used for lexical knowledge engineering, like the Semantic Network Processing System (SNePS) of Stuart C. Shapiro[41] or the MultiNet paradigm of Hermann Helbig,[42] especially suited for the semantic representation of natural language expressions and used in several NLP applications.
语义网络还有与相应的软件工具集相关的复杂类型，这些工具用于词汇知识工程，如 Stuart C. Shapiro 的语义网络处理系统（SNePS）或 Hermann Helbig 的多网（MultiNet）范式，特别适合于自然语言表达的语义表示，并在多个自然语言处理应用中使用。

Semantic networks are used in specialized information retrieval tasks, such as plagiarism detection. They provide information on hierarchical relations in order to employ semantic compression to reduce language diversity and enable the system to match word meanings, independently from sets of words used.
语义网络用于专门的信息检索任务，如剽窃检测。它们提供关于层次关系的信

The Knowledge Graph proposed by Google in 2012 is actually an application of semantic network in search engine.
谷歌在 2012 年提出的知识图谱实际上是搜索引擎中语义网络的应用。

Modeling multi-relational data like semantic networks in low-dimensional spaces through forms of embedding has benefits in expressing entity relationships as well as extracting relations from mediums like text. There are many approaches to learning these embeddings, notably using Bayesian clustering frameworks or energy-based frameworks, and more recently, TransE[43] (NeurIPS 2013). Applications of embedding knowledge base data include Social network analysis and Relationship extraction.
通过嵌入形式在低维空间中对多关系数据（如语义网络）进行建模，在表达实体关系以及从文本等媒介中提取关系方面具有优势。学习这些嵌入有许多方法，特别是使用贝叶斯聚类框架或基于能量的框架，以及最近的研究 TransE [43] （NeurIPS 2013）。嵌入知识库数据的应用包括社交网络分析和关系提取。

See also  参见

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Other examples

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References

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Further reading

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