A Next-generation Web Foundation based on Common Description of Concept Meaning expressed in Natural Language Texts and its Related Intelligent Functions

网络已经发展成为全球信息流动、共享甚至共同创造不可或缺的社交平台。我们在面向下一代Web的语义计算基础上进行了研究，使计算机能够理解Web信息的语义含义，从而在考虑信息表面层次和语义层次的基础上对Web信息进行检索、挖掘、编辑和组织。具体来说，我们的工作重点是CDL（概念描述语言），这是一种用于表示文本中表达的概念含义的通用语言。CDL是一项起源于日本的技术，在我们集团的推动下经历了W3C的国际标准化进程。由于CDL不依赖于某一特定的语言，对所有的自然语言具有通用性，因此它可以被称为“计算机世界语”，也可能有助于克服世界上的语言障碍问题。目前CDL的首要问题是从自然语言文本到CDL表示的转换。像机器翻译一样，它的全自动转换在不久的将来是不可能的。因此，我们研究并开发了一种通过词义消歧过程的半自动转换方法，即当计算机无法确定正确的词义时，由人类作家/操作员选择正确的词义。针对CDL的另一个问题，我们开发了一种针对CDL数据的语义检索方法，有效地实现了语义级的匹配。CDL表示的基础是用预定义的关系标签之一连接句子中的两个单词实体。作为相关研究，我们利用搜索引擎设计了一种基于分布假设计算两个词实体之间相似度的有效方法。这项研究在国际上得到了高度评价，它被WWW2009 （Web技术领域最负盛名的会议）作为一篇完整的论文接受。基于这一研究结果，我们开发了一种新型的搜索引擎，称为潜在关系搜索引擎，它接受带有一个空白元素的两个实体对，例如{(Tokyo, Japan), (？（法国）}，作为查询，并产生一个答案，例如{？=Paris}。这种潜在关系搜索的机制已经申请了专利。此外，作为这种关系搜索的扩展，我们发明了一种高效的共聚类方法，用于从Web文本中提取的大量候选实体对中挖掘典型的有意义的实体对；这个框架被称为开放信息提取。本研究已被WWW2010收录为论文全文，并已提交专利申请。少

The Web has grown to an indispensable social platform for global information flow, sharing and even co-creation. We carried out our research on a foundation of semantic computing toward a next-generation Web, which allows computers to understand the semantic meaning of Web information and thereby to work on the retrieval, mining, editing and organizing Web information considering not only the surface level of information but also the semantic level. Specifically, we worked focusing on CDL (Concept Description Language), which is a common language for representing concept meaning expressed in texts. CDL is a technology originated from Japan, and has been undergone the process of international standardization in W3C promoted by our group. Because CDL is not dependent on a particular language and has a universal property for all the natural languages, it can be called "computer Esperanto language" and may also contribute to overcome the language barrier problem in the world. The current f … More irst issue on CDL is the conversion from natural language texts to the CDL representation. Like in machine translation, its full automatic conversion is impossible in near future. Thus we have studied and developed a semi-automatic conversion method passing through the process of word sense disambiguation, where a human writer/operator selects the correct word sense when the computer cannot determine the correct one. For another issue regarding CDL, we have developed a semantic retrieval method for CDL data, which achieves a semantic-level matching efficiently.The base of the CDL representation is to connect two word entities in a sentence with one of predefined relational labels. As its related research, we have devised an efficient method for computing the similarity between two word entities based on the distributional hypothesis through the use of a search engine. This research was highly evaluated internationally such that it was accepted as a full paper at WWW2009 (the most prestigious conference in the Web technology field). Based on this research result, we have developed a new type search engine named latent relational search engine, which accepts two entity pairs with one blank element, e.g., {(Tokyo, Japan), (?, France)}, as a query, and produces an answer, e.g., {?=Paris} in this case. The mechanism of this latent relational search has been applied for patent.In addition, as an extension of this relational search, we have invented an efficient co-clustering method for mining typical and meaningful entity pairs from lots of entity pair candidates extracted from Web texts ; this framework is called open information extraction. This research was also accepted as a full paper at WWW2010, and has been also submitted for a patent application. Less