T emporal K nowledge B ase C ompletion: N ew A lgorithms and E valuation P rotocols (2020)

Abstract: Research on temporal knowledge bases, which associate a relational fact (s,r,o) with a validity time period (or time instant), is in its early days. Our work considers predicting missing entities (link prediction) and missing time intervals (time prediction) as joint Temporal Knowledge Base Completion (TKBC) tasks, and presents TIMEPLEX, a novel TKBC method, in which entities, relations and, time are all embedded in a uniform, compatible space. TIMEPLEX exploits the recurrent nature of some facts/events and temporal interactions between pairs of relations, yielding state-of-the-art results on both prediction tasks. We also find that existing TKBC models heavily overestimate link prediction performance due to imperfect evaluation mechanisms. In response, we propose improved TKBC evaluation protocols for both link and time prediction tasks, dealing with subtle issues that arise from the partial overlap of time intervals in gold instances and system predictions.

## introduction a knowledge base (kb) is a collection of triples (s, r, o), with a subject s, a relation type r and an object o. kbs are usually incomplete, necessitating completion (kbc), i.e., inferring facts not provided in the kb. a kbc model is often evaluated via link prediction: supplying missing arguments to queries of the form (s, r, ?) and (?, r, o). many relations are transient or impermanent. temporal kbs annotate each fact (event) with the time period (instant) in which it holds (occurs) @xcite . a person is born in a city in an instant, a politician can be a country's president for several years, and a marriage may last between years and decades. temporal kbs represent these by (s, r, o, t ) tuples, where t is a span of time. ## evaluation metrics and filtering the preceding discussion motivates why we need clearly-thought-out filtering and evaluation schemes, not only for time interval prediction queries, but also because time affects link prediction evaluation in subtle but fundamental ways. 3 tac's original score compares gold and predicted bounds on begin and end of an interval. this formula is its adaptation, where begin and end are each a specific time point. this section addresses both issues. ## the proposed timeplex framework similar to tnt-complex, timeplex learns complex-valued entity, relation, and time instant embeddings. however, it has several differences from tnt-complex. (1) its base scoring function φ t x (s, r, o, t) adds several products of three embeddings, instead of a single four-way product (section 4.1). (2) it has a fully automatic mechanism to introduce additional features to capture recurrent nature of a relation, as well as temporal interactions between pairs of relations (section 4.2). (3) it uses a two-phase training (section 4.3) curriculum that estimates first the embeddings and then novel additional parameters. (4) its testing protocol can output a missing time-interval t for time-interval prediction queries (section 4.4). ## experiments we investigate the following research questions. ## discussion timeplex cannot exploit the influence that an entity can have on time difference distributions. for example, the life expectancy of a person (mean difference between diedin and bornin events) would be around 85 in japan, but 54 in lesotho. extending our model to learn separate parameters for each rel, entity pair may be difficult due to sparsity. also, recurrent facts may admit exceptions: winter olympics are held every 4 years except for 1992 and 1994. however, we do not expect even humans to do well in such cases. exceptions like these are sparse and difficult to learn, except by rote. ## conclusion we presented timeplex, a new tkbc framework, which combines representations of time with representations of entities and relations. it also learns soft temporal consistency constraints, which allow knowledge of one temporal fact to influence belief in another fact. timeplex exceeds the performance of existing tkbc systems. time embeddings are temporally meaningful, and timeplex makes fewer temporal consistency and ordering mistakes. we also argue that current evaluation schemes for both link and time prediction have limitations, and propose more meaningful schemes. our method of sampling is efficient as the data size is unchanged. it also ensures each fact is sampled uniformly, not hurting link prediction performance by oversampling of long duration facts. hyte time prediction: hyte can only predict a bin for the test fact. to convert predicted bins to years (or days), we take a mean of all years seen with the predicted bin and then do greedy coalescing to output time interval in years.