Programming with Personalized PageRank: A Locally Groundable First-Order Probabilistic Logic

Many information-management tasks (including classification, retrieval, information extraction, and information integration) can be formalized as inference in an appropriate probabilistic first-order logic. However, most probabilistic first-order logics are not efficient enough for realisticallysized instances of these tasks. One key problem is that queries are typically answered by "grounding" the query - i.e., mapping it to a propositional representation, and then performing propositional inference-and with a large database of facts, groundings can be very large, making inference and learning computationally expensive. Here we present a first-order probabilistic language which is well-suited to approximate "local" grounding: in particular, every query Q can be approximately grounded with a small graph. The language is an extension of stochastic logic programs where inference is performed by a variant of personalized PageRank. Experimentally, we show that the approach performs well on an entity resolution task, a classification task, and a joint inference task; that the cost of inference is independent of database size; and that speedup in learning is possible by multi-threading.