CAREER: Creation, Visualization, and Mining of Domain Textual Graphs: Integrating Domain Knowledge and Human Intelligence

据了解，文本信息正以惊人的速度增长，这给试图发现隐藏在其中的有价值信息的分析师带来了巨大的挑战。例如，新的非平凡的趋势、模式和感兴趣的实体之间的联系，如基因、蛋白质和疾病之间的联系，以及不同地方或人的共同点之间的联系，都是这种潜在知识的形式。这项研究的目标是探索自动解决方案，以筛选这些广泛的文档集合，以发现有趣的链接和隐藏的信息，这些链接和隐藏的信息连接了事实、命题或假设。此外，还将编写一份深入而简明的跨文件摘要，解释每一种联系的基本含义，以及从维基百科知识库获得的相关链接和解释，作为补充或加强文本集合中现有信息的主要手段，从而提供对已发现知识的更全面的看法。该项目将影响许多领域，如国土安全、航空安全、生物医学和医疗保健应用。这些技术将有可能揭示大型文档集中可用的新信息，并通过整合领域知识和从维基百科获得的相关信息，提供对发现的假设的多视角视角。该项目将提供以研究为基础的教育和培训机会，使各级学生在信息分析和发现方面做好准备。这一项目的重点是探索一种新的文本知识表示、整合和挖掘框架，该框架将涵盖以下领域：(1)自动构建实体关系发现的图形框架，这是一种有利于细粒度信息搜索和发现的新表示；(2)有效整合来自多个来源的信息，包括代表性数据集合中包含的知识、特定领域的知识(例如领域本体)和世界知识(例如WordNet等词汇资源和维基百科等大型知识储存库)；(3)识别实体之间隐藏联系的新的发现算法和工具；(4)通过实现自动本体驱动的场景检测和主题级建模来增强领域建模；以及(5)图形框架和发现的假设的交互式可视化工具。这项研究提出，下一代搜索工具需要能够整合来自多个相互关联的单位的信息，并结合各种证据来源，这将使信息搜索和发现的当前技术水平取得根本性进展。将探索自然语言处理(NLP)、信息提取(IE)、信息检索(IR)、数据挖掘、机器学习和语义网等技术的组合，以解决关键信息发现问题。欲了解更多信息，请访问项目网站：http://www.cs.ndsu.nodak.edu/~wjin/WSD-RelMiner.

It is understood that textual information is growing at an astounding pace, creating an enormous challenge for analysts trying to discover valuable information that is buried within. For example, new non-trivial trends, patterns, and associations among entities of interest, such as associations between genes, proteins and diseases, and the connections between different places or the commonalities of people, are such forms of underlying knowledge. The goal of this research is to explore automated solutions for sifting through these extensive document collections to detect interesting links and hidden information that connect facts, propositions or hypotheses. In addition, a more comprehensive view of discovered knowledge will be provided by generating an in-depth and concise cross-document summary explaining the underlying meaning of each connection, along with relevant links and explanations acquired from the Wikipedia knowledge base, which serves as the primary means of complementing or enhancing existing information in text collections. The project will impact many areas, such as homeland security, aviation safety, biomedical and healthcare applications. The techniques will have the potential to expose new information available in large document collections and to provide a multi-view perspective of discovered hypotheses by integrating domain knowledge and relevant information acquired from Wikipedia. Research-based education and training opportunities will be offered by this project to prepare students at all levels in information analysis and discovery. Specific attention will also be paid to promoting the participation of underrepresented groups in the research efforts.This project focuses on the exploration of a novel textual knowledge representation, integration, and mining framework that will cover the following areas: (i) automatic construction of graphical frameworks for entity relationship discovery, a new representation conducive to fine-grained information search and discovery; (ii) effective integration of information from multiple sources, including knowledge contained in representative data collections, domain-specific knowledge (e.g., domain ontologies), and world knowledge (e.g., lexical resources such as WordNet and large-scale knowledge repositories such as Wikipedia); (iii) new discovery algorithms and tools that identify hidden connections among entities; (iv) enhancement of domain modeling through enabling automatic ontology-driven scenario detection and topic-level modeling; and (v) interactive visualization tools for the graphical framework and discovered hypotheses. This research proposes that next-generation search tools require the capability of integrating information from multiple interrelated units and combining various evidence sources, which will make fundamental advances in the current state of the art for information search and discovery. A combination of techniques in Natural Language Processing (NLP), Information Extraction (IE), Information Retrieval (IR), Data Mining, Machine Learning, and Semantic Web will be explored to attack critical information discovery problems. For further information see the project web site: http://www.cs.ndsu.nodak.edu/~wjin/WSD-RelMiner.