Drawing TimeML Relations with T-BOX

T-BOX is a new way of visualizing the temporal relations in TimeML graphs. Currently, TimeML\u27s temporal relations are usually presented as rows in a table or as directed labeled edges in a graph. I will argue that neither mode of representation scales up nicely when bigger documents are considered and that both make it harder than necessary to get a quick picture of what the temporal structure of a document is. T-BOX is an alternative way of visualizing TimeML graphs that uses left-to-right arrows, box-inclusions and stacking as three distinct ways to visualize precedence, inclusion and simultaneity

Chronoscopes: A theory of underspecified temporal representations

Representation and reasoning about time and events is a fundamental aspect of our cognitive abilities and intrinsic to our construal of the structure of our personal and historical lives and recall of past experiences. This talk describes an abstract device called a Chronoscope, that allows a temporal representation (a set of events and their temporal relations) to be viewed based on temporal abstractions. The temporal representation is augmented with abstract events called episodes that stand for discourse segments. The temporal abstractions allow one to collapse temporal relations, or view the representation at different time granularities (hour, day, month, year, etc.), with corresponding changes in event characterization and temporal relations at those granularities. A temporal representation can also be filtered to specify temporal trajectories of particular participants. Trajectories, in turn, can be intersected at various levels of granularity. Chronoscopes can be used to compare temporal representations (e.g., for aggregation, summarization, or evaluation purposes), as well as help in the visualization of temporal narrative

ForecastTKGQuestions: A Benchmark for Temporal Question Answering and Forecasting over Temporal Knowledge Graphs

Question answering over temporal knowledge graphs (TKGQA) has recently found increasing interest. TKGQA requires temporal reasoning techniques to extract the relevant information from temporal knowledge bases. The only existing TKGQA dataset, i.e., CronQuestions, consists of temporal questions based on the facts from a fixed time period, where a temporal knowledge graph (TKG) spanning the same period can be fully used for answer inference, allowing the TKGQA models to use even the future knowledge to answer the questions based on the past facts. In real-world scenarios, however, it is also common that given the knowledge until now, we wish the TKGQA systems to answer the questions asking about the future. As humans constantly seek plans for the future, building TKGQA systems for answering such forecasting questions is important. Nevertheless, this has still been unexplored in previous research. In this paper, we propose a novel task: forecasting question answering over temporal knowledge graphs. We also propose a large-scale TKGQA benchmark dataset, i.e., ForecastTKGQuestions, for this task. It includes three types of questions, i.e., entity prediction, yes-no, and fact reasoning questions. For every forecasting question in our dataset, QA models can only have access to the TKG information before the timestamp annotated in the given question for answer inference. We find that the state-of-the-art TKGQA methods perform poorly on forecasting questions, and they are unable to answer yes-no questions and fact reasoning questions. To this end, we propose ForecastTKGQA, a TKGQA model that employs a TKG forecasting module for future inference, to answer all three types of questions. Experimental results show that ForecastTKGQA outperforms recent TKGQA methods on the entity prediction questions, and it also shows great effectiveness in answering the other two types of questions.Comment: Accepted to ISWC 202

Annotation des informations temporelles dans des textes en français.

National audienceLe traitement des informations temporelles est crucial pour la compréhension de textes en langue naturelle. Le langage de spécification TimeML a été conçu afin de permettre le repérage et la normalisation des expressions temporelles et des événements dans des textes écrits en anglais. L'objectif des divers projets TimeML a été de formuler un schéma d'annotation pouvant s'appliquer à du texte libre, comme ce que l'on trouve sur le Web, par exemple. Des efforts ont été faits pour l'application de TimeML à d'autres langues que l'anglais, notamment le chinois, le coréen, l'italien, l'espagnol et l'allemand. Pour le français, il y a eu des efforts allant dans ce sens, mais ils sont encore un peu éparpillés. Dans cet article, nous détaillons nos travaux actuels qui visent à élaborer des ressources complètes pour l'annotation de textes en français selon TimeML - notamment un guide d'annotation, un corpus de référence (Gold Standard) et des modules d'annotation automatique