构建基于文献信息网络的知识发现系统应用模型的设想

文献检索能够帮助用户快速地查找与获取目标文献，但非结构化的文献仍然需要人工阅读才能获得有效的知识，严重限制了从大量文献中发现知识的效率。基于文献的知识发现研究是通过文献集中潜在的关联发现来形成科学假设，因此利用文本挖掘技术将非结构化的文献集转化为图结构模型，对进一步知识发现的实施与深入挖掘都具有重要作用。有鉴于此，我们利用复杂网络的方法对文献集进行文本挖掘，探讨了关联知识的图结构组织对文献知识发现的重要作用，揭示了非相关文献中的隐含知识，并构建了文献信息网络的知识发现应用模型。

Ideas on establishing application model of knowledge discovery system based on literature information network

Literature retrieval can help users quickly find their target literature. However, useful knowledge can only be acquired by reading the non-structured literature,which will reduce the efficiency to discover knowledge from massive literature.Studies on literature-based knowledge discovery are to discover the unformed scientific hypothesis through the potential links in literature set. Transforming the non-structured literature to a figure structure model with text-mining techniques thus plays an important role in further discovering and mining knowledge.A literature information network-based application model of knowledge discovery was established by studying the important role the figure structure of linked knowledge plays in knowledge discovery using the complex network techniques, which revealed the invisible knowledge in non-related literature.