Knowledge extraction

Abstract: Rough set theory, introduced by Zdzislaw Pawlak in the early 1980s [11, 12], is a new mathematical tool to deal with vagueness and uncertainty. This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.

Abstract: ■ Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges involved in real-world applications of knowledge discovery, and current and future research directions in the field.

Abstract: We present YAGO, a light-weight and extensible ontology with high coverage and quality. YAGO builds on entities and relations and currently contains more than 1 million entities and 5 million facts. This includes the Is-A hierarchy as well as non-taxonomic relations between entities (such as HASONEPRIZE). The facts have been automatically extracted from Wikipedia and unified with WordNet, using a carefully designed combination of rule-based and heuristic methods described in this paper. The resulting knowledge base is a major step beyond WordNet: in quality by adding knowledge about individuals like persons, organizations, products, etc. with their semantic relationships - and in quantity by increasing the number of facts by more than an order of magnitude. Our empirical evaluation of fact correctness shows an accuracy of about 95%. YAGO is based on a logically clean model, which is decidable, extensible, and compatible with RDFS. Finally, we show how YAGO can be further extended by state-of-the-art information extraction techniques.

Abstract: It is our great pleasure to welcome you to the 2016 ACM Conference on Knowledge Discovery and Data Mining -- KDD'16. We hope that the content and the professional network at KDD'16 will help you succeed professionally by enabling you to: identify technology trends early; make new/creative contributions; increase your productivity by using newer/better tools, processes or ways of organizing teams; identify new job opportunities; and hire new team members. We are living in an exciting time for our profession. On the one hand, we are witnessing the industrialization of data science, and the emergence of the industrial assembly line processes characterized by the division of labor, integrated processes/pipelines of work, standards, automation, and repeatability. Data science practitioners are organizing themselves in more sophisticated ways, embedding themselves in larger teams in many industry verticals, improving their productivity substantially, and achieving a much larger scale of social impact. On the other hand we are also witnessing astonishing progress from research in algorithms and systems -- for example the field of deep neural networks has revolutionized speech recognition, NLP, computer vision, image recognition, etc. By facilitating interaction between practitioners at large companies & startups on the one hand, and the algorithm development researchers including leading academics on the other, KDD'16 fosters technological and entrepreneurial innovation in the area of data science. This year's conference continues its tradition of being the premier forum for presentation of results in the field of data mining, both in the form of cutting edge research, and in the form of insights from the development and deployment of real world applications. Further, the conference continues with its tradition of a strong tutorial and workshop program on leading edge issues of data mining. The mission of this conference has broadened in recent years even as we placed a significant amount of focus on both the research and applied aspects of data mining. As an example of this broadened focus, this year we have introduced a strong hands-on tutorial program nduring the conference in which participants will learn how to use practical tools for data mining. KDD'16 also gives researchers and practitioners a unique opportunity to form professional networks, and to share their perspectives with others interested in the various aspects of data mining. For example, we have introduced office hours for budding entrepreneurs from our community to meet leading Venture Capitalists investing in this area. We hope that KDD 2016 conference will serve as a meeting ground for researchers, practitioners, funding agencies, and investors to help create new algorithms and commercial products. The call for papers attracted a significant number of submissions from countries all over the world. In particular, the research track attracted 784 submissions and the applied data science track attracted 331 submissions. Papers were accepted either as full papers or as posters. The overall acceptance rate either as full papers or posters was less than 20%. For full papers in the research track, the acceptance rate was lower than 10%. This is consistent with the fact that the KDD Conference is a premier conference in data mining and the acceptance rates historically tend to be low. It is noteworthy that the applied data science track received a larger number of submissions compared to previous years. We view this as an encouraging sign that research in data mining is increasingly becoming relevant to industrial applications. All papers were reviewed by at least three program committee members and then discussed by the PC members in a discussion moderated by a meta-reviewer. Borderline papers were thoroughly reviewed by the program chairs before final decisions were made.