Keyword Prediction with ARM on Bibliographic RDF Data
نویسندگان
چکیده
منابع مشابه
Scalable Keyword Search on Big RDF Data
Keyword search is a useful tool for exploring large RDF datasets. Existing techniques either rely on constructing a distance matrix for pruning the search space or building summarization from the RDF graphs for query processing. In this work, we show that existing techniques have serious limitations in dealing with realistic, large RDF graphs with tens of millions of triples. Furthermore, the e...
متن کاملPattern-Based Keyword Search on RDF Data
An increasing number of RDF datasets are available on the Web. Querying RDF data requires the knowledge of a query language such as SPARQL; it also requires some information describing the content of these datasets. The goal of our work is to facilitate the interrogation of RDF datasets, and we present an approach for enabling users to search in RDF data using keywords. We propose the notion of...
متن کاملKeyword Search in Bibliographic XML Data
Keyword search is a user-friendly way to query text, HTML, XML documents and even relational databases. The previous well-known semantic of LCA (Lowest Common Ancestor) is used for XML keyword search based on tree model. However, LCA cannot exploit the information in ID references, thus may return a large tree containing irrelevant results. Another keyword search approach based on general digra...
متن کاملEfficient Keyword Search on Large Rdf Data Using Optimization Technique
Now a day’s keyword search in data mining is very emerging topic. Latest keyword search techniques on Semantic Web are moving away from shallow, information retrieval-style approaches that merely find ―keyword matches‖ towards more interpretive approaches that attempt to induce structure from keyword queries. Exploiting identity links among RDF resources allows applications to efficiently integ...
متن کاملDescribing bibliographic references in RDF
In this paper we present two ontologies, i.e., BiRO and C4O, that allow users to describe bibliographic references in an accurate way, and we introduce REnhancer, a proof-of-concept implementation of a converter that takes as input a raw-text list of references and produces an RDF dataset according to the BiRO and C4O ontologies.
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: Procedia Computer Science
سال: 2015
ISSN: 1877-0509
DOI: 10.1016/j.procs.2015.04.019