Time is deeply woven into how people perceive, and communicate about the world. Almost unconsciously, we provide our language utterances with temporal cues, like verb tenses, and we can hardly produce sentences without such cues. Extracting temporal cues from text, and constructing a global temporal view about the order of described events is a major challenge of automatic natural language understanding. Temporal reasoning, the process of combining different temporal cues into a coherent temporal view, plays a central role in temporal information extraction. This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on how combining symbolic reasoning with machine learning-based information extraction systems can improve performance. It gives a clear overview of the used methodologies for temporal reasoning, and explains how temporal reasoning can be, and has been successfully integrated into temporal information extraction systems. Based on the distillation of existing work, this survey also suggests currently unexplored research areas. We argue that the level of temporal reasoning that current systems use is still incomplete for the full task of temporal information extraction, and that a deeper understanding of how the various types of temporal information can be integrated into temporal reasoning is required to drive future research in this area.

Knowledge graphs (KGs) typically contain temporal facts indicating relationships among entities at different times. Due to their incompleteness, several approaches have been proposed to infer new facts for a KG based on the existing ones-a problem known as KG completion. KG embedding approaches have proved effective for KG completion, however, they have been developed mostly for static KGs. Developing temporal KG embedding models is an increasingly important problem. In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time. This is in contrast to the existing temporal KG embedding approaches where only static entity features are provided. The proposed embedding function is model-agnostic and can be potentially combined with any static model. We prove that combining it with SimplE, a recent model for static KG embedding, results in a fully expressive model for temporal KG completion. Our experiments indicate the superiority of our proposal compared to existing baselines.

Cyberbullying is a critical socio-technical problem that seriously limits the use of online interaction spaces by different individuals. Emerging literature identifies cyberbullying as a continuous temporal phenomena rather than one-off incidents. However, as of yet, little computational work has been done to model the temporal dynamics of cyberbullying in online sessions. In this work, we model the temporal dynamics of commenting behavior as point processes and validate it over a crowd-labeled cyberbullying data-set of Instagram media sessions. We define several temporal features to model the distinguishing characteristics between cyberbullying and regular media sessions. We find that our approach is successfully able to identify significant differences between cyberbullying and regular media sessions, and provide a performance increase in cyberbullying detection. This paves the way for more nuanced work on the use of temporal modeling to detect and mitigate the occurrence of cyberbullying.

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These two papers have nice overview of recent works. First one explain recent architectures and introduce a state of the art method for video recognition problem and the second one analyze datasets, algorithms and evaluation metrics in a different perspective.

Little is known about what causes anti-social behavior online. The paper at hand analyzes vandalism and damage in Wikipedia with regard to the time it is conducted and the country it originates from. First, we identify vandalism and damaging edits via ex post facto evidence by mining Wikipedia’s revert graph. Second, we geolocate the cohort of edits from anonymous Wikipedia editors using their associated IP addresses and edit times, showing the feasibility of reliable historic geolocation with respect to country and time zone, even under limited geolocation data. Third, we conduct the first spatio-temporal analysis of vandalism on Wikipedia. Our analysis reveals significant differences for vandalism activities during the day, and for different days of the week, seasons, countries of origin, as well as Wikipedia’s languages. For the analyzed countries, the ratio is typically highest at non-summer workday mornings, with additional peaks after break times. We hence assume that Wikipedia vandalism is linked to labor, perhaps serving as relief from stress or boredom, whereas cultural differences have a large effect. Our results open up avenues for new research on collaborative writing at scale, and advanced technologies to identify and handle antisocial behavior in online communities.

The Temporal Network with Uncertainty (TNU) modeling framework is used to represent temporal knowledge in presence of qualitative temporal uncertainty. Dynamic Controllability (DC) is the problem of deciding the existence of a strategy for scheduling the controllable time points of the network observing past happenings only. In this paper, we address the DC problem for a very general class of TNU, namely Disjunctive Temporal Network with Uncertainty. We make the following contributions. First, we define strategies in the form of an executable language; second, we propose the first decision procedure to check whether a given strategy is a solution for the DC problem; third we present an efficient algorithm for strategy synthesis based on techniques derived from Timed Games and Satisfiability Modulo Theory. The experimental evaluation shows that the approach is superior to the state-of-the-art.

We represent knowledge as integrity constraints in a formalization of probabilistic spatio-temporal knowledge bases. We start by defining the syntax and semantics of a formalization called PST knowledge bases. This definition generalizes an earlier version, called SPOT, which is a declarative framework for the representation and processing of probabilistic spatio-temporal data where probability is represented as an interval because the exact value is unknown. We augment the previous definition by adding a type of non-atomic formula that expresses integrity constraints. The result is a highly expressive formalism for knowledge representation dealing with probabilistic spatio-temporal data. We obtain complexity results both for checking the consistency of PST knowledge bases and for answering queries in PST knowledge bases, and also specify tractable cases. All the domains in the PST framework are finite, but we extend our results also to arbitrarily large finite domains.

Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate different properties in spatio-temporal data, such as spatial clustering and shared structure among variables. We demonstrate how the general framework can be applied to cokriging and forecasting tasks, and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee. We conduct experiments on both synthetic datasets and real application datasets to demonstrate that our method is not only significantly faster than existing methods but also achieves lower estimation error.

Solving challenging computational problems involving time has been a critical component in the development of artificial intelligence systems almost since the inception of the field. This book provides a concise introduction to the core computational elements of temporal reasoning for use in AI systems for planning and scheduling, as well as systems that extract temporal information from data. It presents a survey of temporal frameworks based on constraints, both qualitative and quantitative, as well as of major temporal consistency techniques. The book also introduces the reader to more recent extensions to the core model that allow AI systems to explicitly represent temporal preferences and temporal uncertainty. This book is intended for students and researchers interested in constraint-based temporal reasoning.