Knowledge retrieval for scientific literatures
نویسندگان
چکیده
منابع مشابه
Recommending Scientific Literatures in a Collaborative Tagging Environment
Recently, collaborative tagging has become popular in the web2.0 world. Tags can be helpful if used for the recommendation since they reflect characteristic content features of the resources. However, there are few researches which introduce tags into the recommendation. This paper proposes a tag-based recommendation framework for scientific literatures which models the user interests with tags...
متن کاملModeling Topic-Level Academic Influence in Scientific Literatures
Scientific articles are not born equal. Some generate an entire discipline while others make relatively fewer contributions. When reviewing scientific literatures, it would be useful to identify those important articles and understand how they influence others. In this paper, we introduce J-Index, a quantitative metric modeling topic-level academic influence. J-Index is calculated based on the ...
متن کاملAnnotation of Scientific Summaries for Information Retrieval
We present a methodology combining surface NLP and Machine Learning techniques for ranking asbtracts and generating summaries based on annotated corpora. The corpora were annotated with meta-semantic tags indicating the category of information a sentence is bearing (objective, findings, newthing, hypothesis, conclusion, future work, related work). The annotated corpus is fed into an automatic s...
متن کاملAKMiner: Domain-Specific Knowledge Graph Mining from Academic Literatures
Existing academic search systems like Google Scholar usually return a long list of scientific articles for a given research domain or topic (e.g. “document summarization”, “information extraction”), and users need to read volumes of articles to get some ideas of the research progress for a domain, which is very tedious and time-consuming. In this paper, we propose a novel system called AKMiner ...
متن کاملKnowledge Extraction for Information Retrieval
Document retrieval is the task of returning relevant textual resources for a given user query. In this paper, we investigate whether the semantic analysis of the query and the documents, obtained exploiting state-of-the-art Natural Language Processing techniques (e.g., Entity Linking, Frame Detection) and Semantic Web resources (e.g., YAGO, DBpedia), can improve the performances of the traditio...
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 American Society for Information Science and Technology
سال: 2012
ISSN: 0044-7870
DOI: 10.1002/meet.14504901152