You will be shown an excerpt from a computer science scientific research paper, followed by a question about the paper. Please answer the question. Do not include any text in your response other than the answer.

Context: •Challenge 1. Existing knowledge graph are not constructed for re-ranking task. They usually contain trivial factual triples, which can hardly bring information gain. The inappropriate selection of external knowledge could even jeopardize the re-ranker performance. How to utilize existing knowledge graph to re-ranking task is remain a challenge.•Challenge 2.The explicit knowledge and implicit knowledge are highly heterogeneous due to the different sources, which makes the aggregation of the two difficult.How to mutually refine each other and effectively aggregate explicit knowledge into implicit knowledge to alleviate the semantic gap between query and passage is still a challenge. Fig. 2 shows a real case of our global graph pruning method on ConceptNet, i.e., a general knowledge graph. In this case, the entity hepatitis has various relations to disease, infectious disease, adult, etc. From the distance of nodes in Fig. 2, we can clearly observe that the knowledge hepatitis is an infectious disease is more reliable and informative than hepatitis is located at adult. To hepatitis, the concept adult is more general than infectious disease. This indicates that our pruning method can effectively eliminate less informative knowledge.

Question: What is the example of unreliable relations in knowledge graph for passage re-ranking scenario?
Unreliable relations in a knowledge graph involve trivial factual triplets that do not bring substantial information gain. For example, in ConceptNet, the entity “hepatitis” has relations with both “infectious disease” and “adult”. To the concept “hepatitis”,  the concept “adults” is more general than “infectious disease” and thus the relationship between “hepatitis” and “infectious disease” is more reliable and informative.