{"type":"ScholarlyArticle","title":"Summary Markov Models for Event Sequences","authors":[{"name":"Debarun Bhattacharjya","slug":"debarun-bhattacharjya","given":"Debarun","family":"Bhattacharjya","affiliation":"IBM Research"},{"name":"Saurabh Sihag","slug":"saurabh-sihag","given":"Saurabh","family":"Sihag","affiliation":"University of Pennsylvania"},{"name":"Oktie Hassanzadeh","slug":"oktie-hassanzadeh","given":"Oktie","family":"Hassanzadeh","affiliation":"IBM Research"},{"name":"Liza Bialik","slug":"liza-bialik","given":"Liza","family":"Bialik","affiliation":"University of Massachusetts , Amherst"}],"abstract":"Datasets involving sequences of different types of events without meaningful time stamps are prevalent in many applications, for instance when extracted from textual corpora. We propose a family of models for such event sequences -- summary Markov models -- where the probability of observing an event type  depends only on a summary of historical occurrences of its influencing set of event types. This Markov model family is motivated by Granger causal models for time series, with the important distinction that only one event can occur in a position in an event sequence. We show that a unique minimal influencing set exists for any set of event types of interest and choice of summary function, formulate two novel models from the general family that represent specific sequence dynamics, and propose a greedy search algorithm for learning them from event sequence data. We conduct an experimental investigation comparing the proposed models with relevant baselines, and illustrate their knowledge acquisition and discovery capabilities through case studies involving sequences from text.","doi":"10.24963/ijcai.2022/670","url":"https://ijcai.org/proceedings/2022/670","pdf":"https://ijcai.org/proceedings/2022/0670.pdf","markdown":"https://ijcai.org/proceedings/2022/670.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/670","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Uncertainty in AI","track":"Main Track","number":670,"pages":{"first":4836,"last":4842},"keywords":["Uncertainty in AI: Tractable Probabilistic Models","Data Mining: Mining Spatial and/or Temporal Data","Knowledge Representation and Reasoning: Causality","Machine Learning: Time-series; Data Streams","Uncertainty in AI: Graphical Models"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987067","length_sec":73,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985210","length_sec":697,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Aalen et al., 2008] O. 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