Ontology assisted crowd mining
نویسندگان
چکیده
منابع مشابه
Ontology Assisted Crowd Mining
We present OASSIS (for Ontology ASSISted crowd mining), a prototype system which allows users to declaratively specify their information needs, and mines the crowd for answers. The answers that the system computes are concise and relevant, and represent frequent, significant data patterns. The system is based on (1) a generic model that captures both ontological knowledge, as well as the indivi...
متن کاملTowards Crowd-Assisted Data Mining
Copyright retained by authors. Abstract Mining massive datasets can benefit from human input, but current approaches require making tradeoffs between overburdening end users or under-informing the system – algorithms become more accurate given more training data, but requiring more exemplars takes significant user effort. In this paper, we suggest an approach that engages nonexpert and semi-exp...
متن کاملAn Ontology Assisted Framework Co-location Pattern Mining
The importance of spatial data mining is growing with the increasing incidence and importance of large geo-spatial datasets such as maps, location based mobile app data, medical data, crime data, education system data, traffic data and many more. Co-location pattern mining is one of the important task in spatial data mining. The co-location patterns represent subsets of Boolean spatial features...
متن کاملMining the Crowd
Harnessing a crowd of Web users for data collection has recently become a wide-spread phenomenon. A key challenge is that the human knowledge forms an open world and it is thus difficult to know what kind of information we should be looking for. Classic databases have addressed this problem by data mining techniques that identify interesting data patterns. These techniques, however, are not sui...
متن کاملIon Channel ElectroPhysiology Ontology (ICEPO) – a case study of text mining assisted ontology development
BACKGROUND Computational modeling of biological cascades is of great interest to quantitative biologists. Biomedical text has been a rich source for quantitative information. Gathering quantitative parameters and values from biomedical text is one significant challenge in the early steps of computational modeling as it involves huge manual effort. While automatically extracting such quantitativ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Proceedings of the VLDB Endowment
سال: 2014
ISSN: 2150-8097
DOI: 10.14778/2733004.2733039