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、共享甚至共同创造的不可或缺的社交平台。我们的研究是以面向下一代Web的语义计算为基础的，它使计算机能够理解Web信息的语义，从而不仅考虑信息的表面层次，而且考虑语义层次，对Web信息进行检索、挖掘、编辑和组织。具体来说，我们专注于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是源于日本的一项技术，在本集团的推动下，已经经历了W3C国际标准化的过程。由于CDL不依赖于特定的语言，对所有的自然语言都具有普适性，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F ...更多信息 CDL的第一个问题是从自然语言文本到CDL表示的转换。像机器翻译一样，它的完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半自动转换方法，通过词义消歧的过程中，一个人的作家/操作员选择正确的词义时，计算机不能确定正确的。针对CDL的另一个问题，本文提出了一种CDL数据的语义检索方法，该方法通过一个预定义的关系标签将句子中的两个词实体连接起来，有效地实现了语义级的匹配。作为其相关研究，我们设计了一个有效的方法来计算两个词实体之间的相似性的基础上的分布假设，通过使用搜索引擎。这项研究在国际上得到了高度评价，在WWW 2009（Web技术领域最负盛名的会议）上被接受为全文。在此基础上，我们开发了一种新型的搜索引擎--潜在关系搜索引擎，它接受两个实体对和一个空白元素，例如，{（日本东京），（？，France）}作为查询，并产生答案，例如，{?=巴黎。此外，作为这种关系搜索的扩展，我们发明了一种有效的协同聚类方法，用于从Web文本中提取的大量实体对候选者中挖掘出典型的、有意义的实体对;这个框架被称为开放信息提取。这项研究也被接受为一个完整的论文在WWW 2010年，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