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Cost-Effective Conceptual Design Over Taxonomies
| Content Provider | ACM Digital Library |
|---|---|
| Author | Nayyeri, Amir Chodpathumwan, Yodsawalai Vakilian, Ali Termehchy, Arash |
| Abstract | It is known that annotating entities in unstructured and semistructured datasets by their concepts improves the effectiveness of answering queries over these datasets. Ideally, one would like to annotate entities of all relevant concepts in a dataset. However, it takes substantial time and computational resources to annotate concepts in large datasets and an organization may have sufficient resources to annotate only a subset of relevant concepts. Clearly, it would like to annotate a subset of concepts that provides the most effective answers to queries over the dataset. We propose a formal framework that quantifies the amount by which annotating entities of concepts from a taxonomy in a dataset improves the effectiveness of answering queries over the dataset. Because the problem is NP-hard, we propose an efficient approximation for the problem. Our extensive empirical studies validate our framework and show the accuracy and efficiency of our algorithm. |
| Starting Page | 35 |
| Ending Page | 40 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450349833 |
| DOI | 10.1145/3068839.3068841 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-05-14 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Content Type | Text |
| Resource Type | Article |
