III-CTX-Small: Adaptive Integration of Structured and Unstructured Data from Many Sources in a Biological Domain

III-CTX-Small：生物领域多源结构化和非结构化数据的自适应集成

• 批准号: 0811562
• 负责人: William Cohen
• 金额: $ 44.31万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0811562&HistoricalAwards=false
• 关键词: III; CTX; Small; Adaptive; Integration

This project involves the design and building ofa knowledge base (KB) system thatlearns to more accurately integrate the many heterogeneous sources ofinformation that are relevant to a single scientist's research needs.The system works by loosely integrating data of many sorts (includingunstructured text) into a single typed directed graph, and thenquerying the graph using a query language that allows "schema-freesimilarity queries". These queries specify a set of query terms(e.g. keywords, entities in the KB, etc) and constraints on thedesired output (e.g. a target data type). The result of a query is aranked list of KB entities, ordered by similarity to the query terms.After a query, a user can optionally label any subset of the rankedlist of suggested answers as ``relevant'' or ``non-relevant''. Theselabels drive a learning phase, the goal of which is to produce abetter ranking. Types of learning currently being investigatedinclude EM-based parameter turning, learning to discriminativelyre-rank, and learning to restructure the graph (by adding or deletingedges or vertexes). Queries collected in the laboratory of workingbiologists are used to evaluate these learning methods.The broadest impact of this project is on the problem of learning tointegrate heterogeneous data sources (including free text andstructured data). However, if successful, the KB system will havebroad impact in the biological research community; in particular, webelieve that adaptive personal KB systems of this sort will be avaluable complement to existing biological KBs.Further information on this project can be found athttp://www.cs.cmu.edu/~wcohen/querendipity/

该项目涉及知识库（KB）系统的设计和构建系统，该系统更准确地整合了与单个科学家的研究需求相关的许多异构信息来源。该系统通过松散整合了许多类型文本的数据（包括非结构化文本）的数据来使用单个键入的定向图表，然后使用图形来进行图形，并允许使用“ schema”。 这些查询指定了一组查询项（例如，关键字，KB中的实体等）以及对thedesired输出（例如目标数据类型）的约束。查询的结果是KB实体列表，按与查询术语相似。查询后，用户可以选择将建议答案的排名列表的任何子集标记为``相关''或``''''或``''' 这些标签推动了学习阶段，其目的是产生教abter排名。 目前正在研究的学习类型包括基于EM的参数转动，学习歧视级别的级别以及学习重组图形（通过添加或删除或顶点）。 在工作生物学家实验室中收集的查询用于评估这些学习方法。该项目的最广泛影响是学习综合性异构数据源（包括自由文本和结构化数据）的问题。但是，如果成功的话，KB系统将对生物学研究界产生影响。特别是，这种适应性的个人KB系统将是可避免与现有生物KBS的补充。有关该项目的further信息可以找到athttp：//www.cs.cmu.edu.edu/~wcohen/clecreendipity/