We lifted 8 functions out of this paper's own repositories and ran 0 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| liu-yushan/PoLo | pwc_unofficial | 0 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| check_rule | Not yet run | liu-yushan/PoLo/mycode/model/rules.py code served (permissive licence) · get_code("ce7c9ee686c9c031") |
| create_output_and_model_dir | Not yet run | liu-yushan/PoLo/mycode/model/trainer.py code served (permissive licence) · get_code("49ae84b4e9adad02") |
| get_conf_length_1 | Not yet run | liu-yushan/PoLo/datasets/Hetionet/preprocessing/calculate_confidence.py code served (permissive licence) · get_code("235febb7d1bd55ef") |
| get_conf_length_2 | Not yet run | liu-yushan/PoLo/datasets/Hetionet/preprocessing/calculate_confidence.py code served (permissive licence) · get_code("26788190b1493200") |
| get_conf_length_3 | Not yet run | liu-yushan/PoLo/datasets/Hetionet/preprocessing/calculate_confidence.py code served (permissive licence) · get_code("776926e52bd63a83") |
| initialize_setting | Not yet run | liu-yushan/PoLo/mycode/model/trainer.py code served (permissive licence) · get_code("ba8093f2012bdfe9") |
| modify_rewards | Not yet run | liu-yushan/PoLo/mycode/model/rules.py code served (permissive licence) · get_code("32c81ffd6db50f7e") |
| prepare_argument | Not yet run | liu-yushan/PoLo/mycode/model/rules.py code served (permissive licence) · get_code("1852640726232918") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Knowledge bases (KB), both automatically and manually constructed, are often incomplete --- many valid facts can be inferred from the KB by synthesizing existing information. A popular approach to KB completion is to infer new relations by combinatory reasoning over the information found along other paths connecting a pair of entities. Given the enormous size of KBs and the exponential number of paths, previous path-based models have considered only the problem of predicting a missing relation given two entities or evaluating the truth of a proposed triple. Additionally, these methods have traditionally used random paths between fixed entity pairs or more recently learned to pick paths between them. We propose a new algorithm MINERVA, which addresses the much more difficult and practical task of answering questions where the relation is known, but only one entity. Since random walks are impractical in a setting with combinatorially many destinations from a start node, we present a neural reinforcement learning approach which learns how to navigate the graph conditioned on the input query to find predictive paths. Empirically, this approach obtains state-of-the-art results on several datasets, significantly outperforming prior methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1711.05851")
get_code_for_paper("1711.05851")
have("1711.05851")
Connect an agent — have() is free.