Inference for Knowledge Graph Construction

The task of knowledge graph construction presents a confounding challenge for statistical relational models. While the uncertainty of extractions from NLP tools and the ontological structure of knowledge are a perfect match for the strengths of statistical relational techniques, the vast and continually growing evidence from which knowledge graphs are constructed can make such models prohibitively expensive. We address this challenge by presenting two lines of research that provide a foundation for online knowledge graph construction. The first is work on knowledge graph identification, a scalable probabilistic model for combining statistical features from uncertain extractions and ontological constraints to efficiently construct a knowledge graph. The second is the necessary theory and accompanying algorithms for partially updating an inferred knowledge graph. We illustrate how combining these components presents the opportunity to apply sophisticated statistical relational models to complex domains, such as knowledge graph construction, without sacrificing quality or efficiency. The Challenge of Lifelong Learning Statistical relational models have a key strength: they capture and represent the myriad relationships found in realworld data while incorporating probabilistic semantics to quantify uncertainty. Unfortunately, the sophisticated models our community builds are accompanied by a hefty price tag. For many practical applications, models will contain millions of statistical dependencies, making inference slow or even intractable. This obstacle is compounded by “lifelong learning” settings where new evidence continually arrives – if repeating inference is costly, the resulting inferences can quickly become outdated and inaccurate. One task that fits the paradigm of lifelong learning is knowledge graph construction. Knowledge graphs are structured knowledge bases where nodes represent entities and edges represent relationships between these entities. A growing number of projects have built vast knowledge graphs from Web data sources, including (among many others) YAGO (Suchanek, Kasneci, and Weikum 2007), OLLIE (Etzioni et al. 2008), DeepDive (Niu et al. 2012), and the Knowledge Vault (Dong et al. 2014). As new information is added to the Web – whether through news stories, user postings, or site updates – these knowledge bases must be updated to reflect the new information. As a case study, we consider a project that explicitly approaches knowledge graph construction as a lifelong learning task: NELL (Carlson et al. 2010), the Never-ending Language Learner. On a roughly daily basis, NELL employs a collection of information extraction techniques to discover candidate facts to add to its knowledge base. Using patterns of agreement and disagreement between extraction techniques, NELL selectively promotes candidate extractions that are likely to be true and uses these promotions as a feedback signal to train each of the extractors. Crucial to the success of NELL is deciding which candidate extractions are true using the rich dependencies between extracted facts. However, the tens to hundreds of millions of facts in NELL’s knowledge base make running inference to construct a complete knowledge graph impractical. Instead, the problem of promoting instances in NELL requires efficient updates to the knowledge graph using new extractions to bolster inferences on uncertain facts. In this paper, we summarize two existing lines of work that form the foundation for the online construction of massive knowledge graphs. The first component is knowledge graph identification (Pujara et al. 2013), a statistical relational model that combines ontological constraints and statistical features from uncertain extractions to infer a complete knowledge graph. The second component is recent work on online collective inference (Pujara, London, and Getoor 2015) which offers a theoretical bound on inference regret when making partial inference updates and algorithms for selectively updating the MAP state while maintaining low error. Finally, we illustrate how combining these two components can be applied to the large-scale lifelong learning problem of knowledge graph construction. Background: Knowledge Graph Identification In this section we provide background on knowledge graph identification, the task of transforming a noisy extraction graph to a consistent knowledge graph. Candidate facts from an information extraction system can be represented as an extraction graph where entities are nodes, categories are labels associated with each node, and relations are directed edges between the nodes. Unfortunately, the output from an information extraction system is often incorrect; the graph constructed from it has spurious and missing nodes and edges, and missing or inaccurate node labels. Our approach, country Kyrgyzstan Kyrgyz Republic bird Bishkek SameEnt