Building Dynamic Knowledge Graphs

One of the fundamental challenges in constructing knowledge bases is that the knowledge we seek to capture comes from an uncertain and changing world. Constructing a KB necessitates dealing with a continual flow of evidence that requires updating and revising the KB. A key question for the AKBC community is how to construct and update KBs using a steadily growing stream of noisy extractions from large text corpora, like the Web. In this vision paper, we (1) formalize the problem of building a dynamic knowledge graph, (2) define a probabilistic model for using new evidence to update the knowledge graph, and (3) present an efficient algorithm for knowledge graph construction on streaming extractions. 1 The Challenge of the Dynamic Knowledge Graph Consider a knowledge base (KB) constructed in 2008: it might accurately capture that Barack Obama is a senator living in Illinois. However, a year later the correct KB must be revised: Barack Obama is no longer a senator, but a president and his residence is Washington, DC. As this tiny example motivates, a crucial challenge in updating the KB is that the facts it contains are not independent; updating one fact, such as employment, may require revising many others, such as residence. Humans have grappled with updating beliefs in response to changing conditions for millennia and the problem has been studied in disciplines ranging from philosophy to computer science. Research over the last few decades has studied many aspects relevant to KBs, spanning areas such as belief revision[1], non-monotonic reasoning[2], and temporal knowledge bases[3, 4, 5]. However, this existing research does not address the practical problems facing AKBC, where information extraction systems are applied to an infinite stream of textual data (such as the Web) and produce millions of extractions with varying degrees of confidence[6, 7]. In this paradigm, we must identify the important statistical dependencies between facts and determine which facts are likely to require revision. To capture statistical dependencies and uncertain information, we adopt a probabilistic approach and formulate the problem in terms of knowledge graph construction. Knowledge graphs represent entities as nodes and the relationships between them as edges, but the structure of the graph is often unknown requiring entity resolution, node labeling and link prediction. This problem is known as knowledge graph identification . In KGI, a probabilistic model specifies a probability distribution over possible knowledge graphs, and inference finds the most probable knowledge graph. Here we propose three of the fundamental challenges to applying KGI in a dynamic setting. (1) How do probability distributions change as new evidence is introduced? What characterizes a desirable model for updating the knowledge graph? (2) How can we specify probabilistic models for dynamic data? How can modeling languages easily express relationships between new extractions and existing knowledge? (3) What considerations are important for inferring dynamic knowledge graphs? How can we improve the speed of inference without sacrificing the quality of the inferred knowledge graph?