Name
Abstract | ||
|---|---|---|
Information retrieval approaches are considered as a key technology to empower lay users to access the Web of Data. A large number of related approaches such as Question Answering and Semantic Search have been developed to address this problem. While Question Answering promises more accurate results by returning a specific answer, Semantic Search engines are designed to retrieve the best top-K ranked resources. In this work, we propose *path, a Semantic Search approach that explores term networks for querying RDF knowledge graphs. The adequacy of the approach is evaluated employing benchmark datasets against state-of-the-art Question Answering as well as Semantic Search systems. The results show that *path achieves better F-1-score than the currently best performing Semantic Search system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-49157-8_22 | Communications in Computer and Information Science |
Field | DocType | Volume |
Data mining,Computer science,SPARQL,Artificial intelligence,Natural language processing,Simple Knowledge Organization System,RDF,Question answering,Semantic search,Information retrieval,Ranking,Semantic analytics,RDF Schema | Conference | 672 |
ISSN | Citations | PageRank |
1865-0929 | 1 | 0.40 |
References | Authors | |
13 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Edgard Marx | 1 | 139 | 14.31 |
| Konrad Höffner | 2 | 262 | 12.66 |
| Saeedeh Shekarpour | 3 | 200 | 17.29 |
| Axel-Cyrille Ngonga Ngomo | 4 | 1775 | 139.40 |
| Jens Lehmann | 5 | 5375 | 355.08 |
| Sören Auer | 6 | 5711 | 418.56 |