Pre-meeting (Dong Nguyen).
Related paper: Semantic taxonomy induction from heterogenous evidence
Focus paper: Unsupervised Ontology Induction from Text
The related paper focuses on taxonomy induction. Their novel contributions are jointly taking evidence of multiple relationships into account and handling polysemy. They view a taxonomy as a set of relations. In this paper they focus on two relations: hyponyms and cousinhood (i and j are mn-cousins if their closest least common subsumer is within exactly m and n links). They add two constraints related to transitivity and cousinhood to the taxonomy structure.
They define the probability of a taxonomy as the joint probability of its relations. Relations have prior and posterior probability given evidence (for example lexical and syntactic patterns). A greedy local search is used to find a taxonomy.
Experiments are done by extending Wordnet. Humans judges evaluated a random sample of generated links. They had good performance with 84% precision and a 70% improvement over non-joint algorithms.
For the focus paper, it would be nice if they had evaluated the extracted relations directly instead of using a task based approach (for example manual judges). I'm also not sure if ontology is the right term to use for the kind of structures they extracted.