Name
Abstract | ||
|---|---|---|
The task of entity linking (EL) is often perceived as an algorithmic problem, where the novelty of systems lies in the decision making process, while the knowledge is relatively fixed. As a consequence, we lack an understanding about the importance and the relevance of diverse knowledge types in EL. However, knowledge and relevance are crucial: following the Gricean maxim, an author relies on assumptions about the knowledge of the reader and uses the most efficient and scarce, yet understandable, level of detail when conveying a message. In this paper, we seek to understand the EL task from a knowledge and relevance perspective. We define four categories of contextual knowledge relevant for EL and observe that two of these are systematically absent in existing entity linkers. Consequently, many contextual cases, in particular long-tail entities, can never be interpreted by existing systems. Finally, we present our ideas on developing knowledge-intensive systems and long-tail datasets. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-59888-8_12 | Lecture Notes in Artificial Intelligence |
Keywords | Field | DocType |
Entity linking,Context,Long tail,Knowledge,Reasoning | Entity linking,Level of detail,Road map,Maxim,Knowledge management,Novelty,Geography,Decision-making | Conference |
Volume | ISSN | Citations |
10318 | 0302-9743 | 1 |
PageRank | References | Authors |
0.36 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filip Ilievski | 1 | 12 | 7.63 |
| Piek Vossen | 2 | 387 | 61.59 |
| Marieke van Erp | 3 | 284 | 24.19 |