基于领域知识图谱的生命医学学科知识发现探析

【目的】 探讨融合多源数据,开展深层次学科知识发现研究与服务的方法。【方法】 通过构建科技文献SPO语义网络形成领域知识图谱的核心;通过&ldquo;实体对齐、概念层次融合与关系融合&rdquo;实现多源异构数据融合,生成完整领域知识图谱;基于领域知识图谱开展深层次学科知识发现;选择造血干细胞癌症治疗进行实证研究。【结果】 提出一套基于知识图谱的学科知识发现方法框架KGSKD,可多维度、细粒度融合多源异构数据,定义数据间复杂语义关系,原生支持知识推理、路径发现、链路预测等知识发现应用。【局限】 KGSKD存在容易出现数据过饱和、知识发现过程可解释性较差、与领域专家沟通难度较高等局限。【结论】 KGSKD具有数据类型更丰富、知识关联更全面、挖掘方法更先进、发现结果更深入等优势,可更有效地支持生命医学学科深层次知识发现研究与服务。</p

科研项目产出绩效评价研究―以干细胞科研领域为例

干细胞与再生医学是现代生命科学领域的前沿,有望成为继药物治疗、手术治疗后的第三种疾病治疗途径,现在已成为十分重要的研究方向。本文基于对2008―2018年国家自然科学基金在干细胞领域的资助情况进行多维度分析,揭示出我国干细胞领域受资助项目的数量、机构、地区和领域分布。同时,从数量、质量和影响力三个维度对依托这些项目所发表的SCI论文进行评价,以评估项目的资助效果,对研究我国干细胞领域的科研现状和政策制定具有一定的参考意义。</p

A knowledge graph of stem cell oriented to subject knowledge discovery

随着科学研究领域不断扩张、细化和交叉，科技文献与科学数据呈&ldquo;井喷式&rdquo;增长，学科的知识结构、知识脉络越来越复杂。知识图谱是一种对多源数据进行多维度、细粒度知识挖掘与语义关联的新型知识组织技术，是知识互联的基础。通过构建知识图谱对学科领域的科技文献与科技数据进行集成、分析和挖掘与可视化呈现，从而勾勒学科的知识结构和发展脉络，可帮助科研人员和管理人员在不同层面和视角对学科研究发展进行分析和把握，已经成为图书馆学、情报学、学科信息学的研究重点。知识图谱在知识组织上实现了信息资源内部知识单元的多层次、细粒度、富语义组织，在服务形式上支持智能检索、知识计算、知识挖掘、辅助决策等知识发现应用，可为专业型、计算型、战略型、政策型、方法型&ldquo;五型融合&rdquo;的情报研究与知识发现新范式提供理论与方法支撑。本文在对科技大数据背景下的学科知识发现需求分析的基础上,以干细胞学科领域为例,综合科技论文、发明专利等科技文献与临床实验、药物产品等科学数据，对学科从整体、科学仪器、动物模型、方法技术、细胞器官、基因、疾病、实验试剂、治疗等层面进行画像，构建干细胞知识图谱，全景式描绘了干细胞领域的知识结构和知识脉络。在此基础上进行干细胞领域知识发现研究，揭示了造血干细胞领域重大病症的知识脉络与干细胞新兴主题。</p

A big data knowledge computing platform for information research

Information research is a method of using modern information technology and soft science research methods to form valuable information products by collecting, selecting, evaluating and synthesizing information resources. With the advent of the era of big data, the core work of information analysis with data is facing enormous opportunities and challenges. How to make good use of big data in an effort to solve the problem of big data, optimize and improve the traditional information research methods and tools, innovation and research based on big data are the key issues that need to be studied and solved in current information research work. Through the analysis of information research methods and common tools under the background of big data, we sort out the processes and requirements of the information research work under big data environment, design and implement a universal knowledge computing platform for information research, which enables intelligence analysts to easily use all kinds of big data analysis algorithms without writing programs.</p

Research on subject profile of stem cell based on knowledge graph

With the expansion of research fields and rapid increase of scientific literatures, knowledge structures of subjects become more and more complex. Subject profile can reveal the multi-level and multidimensional knowledge structures of subjects like user profile characterizes users. As a new knowledge organization technology, knowledge graph can be used to construct subject profile. Stem cell and regenerative medicine is the frontier of life science, and it is expected to become the third way to treat diseases after drug therapy and surgery. Based on multi-source scientific literatures and information, a domain knowledge graph of stem cell was constructed. Based on the knowledge graph, we are designing and constructing subject profile of stem cell, which is of great significance to help decision-makers and researchers to grasp the overall research of stem cell at different levels and perspectives.</p

Technology Evolution Analysis Based on SPO using patent documents: a Case Study of Induced Pluripotent Stem Cells

SPO predications consist of a Subject argument (noun phrase), an object argument (noun phrase), and the relation that binds them (verb phrase), which can represent science and technology (S&amp;T) information with more details in a simple manner and have been widely applied in Knowledge Discovery in Biomedical Literature (KDiBL). The SPO predications are extracted from literature and cleaned. The technology is stated by SPO predications. Young et al. (2008) approached a method that can be used to draw technology evolution map of keywords by calculating the distributions of keywords over the documents cluster groups. This paper follows Young&rsquo;s research using SPO predications instead of keywords. Induced Pluripotent Stem Cells (IPSC) patent documents are selected as a case study