Semantic Technologies for Scientific Literature Management (v2)

在研究人员的生活中，典型的一天包括寻找、阅读和分析与她手头的任务相关的科学文献。在线数字图书馆、学术出版数据库和开放获取库允许访问过多的信息，这给试图获取所需信息的研究人员带来了重大挑战：大多数现代搜索引擎在几秒钟内返回数千篇文章，研究人员不得不手动梳理结果，以找到可能有助于他们研究活动的信息--这是一项既耗时又费力的任务，在此期间很容易错过关键知识。人类处理信息的能力与科学出版前所未有的增长速度明显不成比例，这鼓励了各个领域的研究人员从不断增长的知识库中的海量可用文献中寻找新的方法来提取、组织和管理知识。这些探索的前沿是来自蓬勃发展的语义出版领域的研究人员--一个由语义网研究人员、计算语言学家、图书馆员和出版公司组成的协同社区，旨在建立一个人类和机器都能访问的科学文献传播平台。 有了这项提议，我们就加入了这一运动，通过开发新的方法，将科学文献从孤立的书面内容转变为适合机器翻译的可查询的文件网络。我们概念化了这样的知识库可以以个人研究代理的形式为研究人员提供的无数可能性，帮助研究人员查找、撰写和学习科学文献。我们展示了来自语义网和自然语言处理领域的最先进技术的融合如何实现一个主动代理，该代理可以根据研究人员的背景知识、兴趣和任务为他们提供个性化服务。代理的知识库由使用文本挖掘技术从给定领域的科学文献中自动提取的知识填充。我们使用语义网技术和链接开放数据(LOD)云中可用的丰富知识，通过将其修辞块和内容中讨论的主题形式化，向代理表示科学文献的结构和语义。类似地，我们通过包含他们的个人元数据、兴趣、任务和背景知识的正式表示的他们的简档来将研究人员介绍给代理。 我们的方法是第一次利用将用户和科学出版物的正式模型联系起来的机会，以便提供新颖的、个性化的服务，以减少研究人员的信息过载。

A typical day in the life of a researcher involves finding, reading and analyzing scientific literature that are related to her task at hand. Online digital libraries, academic publishing databases and open access repositories grant access to a plethora of information, which poses major challenges to researchers when trying to obtain the information they need: Most modern search engines return thousands of articles in a matter of seconds and researchers have to manually comb through the results to find the information that may aid them in their research activities - a time-consuming and laborious task, during which critical knowledge can be easily missed. This apparent disproportion of humans' capabilities in processing information versus the unprecedented rate of scientific publishing growth has encouraged researchers from various domains to look for new approaches to extract, organize, and manage knowledge from the immense amount of available literature in ever-growing repositories. The frontiers of these explorations are researchers from the thriving domain of semantic publishing - a synergic community of semantic web researchers, computational linguists, librarians and publishing companies aiming towards a platform for dissemination of scientific literature accessible to both humans and machines. With this proposal, we are joining the movement, by developing new methodologies for transforming scientific literature from written content in isolation into a queryable web of documents suitable for machine interpretation. We conceptualize the myriad of possibilities that such a knowledge base can provide for researchers in form of a Personal Research Agent that helps researchers in finding, writing and learning from scientific literature. We demonstrate how a confluence of state-of-the-art techniques from the Semantic Web and Natural Language Processing domains can realize a proactive agent that can offer personalized services to researchers in retrieval and understanding of scientific literature, based on their background knowledge, interests and tasks. The agent's knowledge base, is populated with knowledge automatically extracted from scientific literature of a given domain using text mining techniques. We represent the structure and semantics of scientific literature to the agent by formalizing its rhetorical blocks and topics discussed within its content using semantic web technologies and the wealth of knowledge available in the Linked Open Data (LOD) cloud. Similarly, we introduce researchers to the agent through their \emph{profiles} that contain a formal representation of their personal metadata, interests, tasks and background knowledge. Our approach is the first to exploit the opportunities of linking a formal model of users and scientific publications in order to offer novel, personalized services to reduce a researcher's information overload.