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Temporal reasoning is a crucial natural language processing (NLP) task, providing a nuanced understanding of time-sensitive contexts within textual data. Although recent advancements in Large Language Models (LLMs) have demonstrated their potential in ...

Many facts change over time, Time-sensitive Question Answering(TSQA) answers questions about time evolution facts to test the model’s ability in the dimension of the time. The existing methods obtain the representations of questions and documents and ...

Multi-modal information outbreak is consistently increasing in social media. Classification of tweet sentiments using various information modalities will help the recommender systems to achieve success in digital marketing. Moreover, aspect-level ...

The use of ontologies to model human behaviours that affect health is challenging since this process involves data from multiple inter-related domains that unfold and evolve over time. However, while current ontology development methodologies are generic ...

A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain, including urban planning and management, ride-sharing services, and intelligent transportation systems. Individuals' preferences and ...

Predicting future events in dynamic knowledge graphs has attracted significant attention. Existing work models the historical information in a holistic way, which achieves satisfactory performance. However, in real-world scenarios, the influence of ...

We present a system for online, incremental composite event recognition. In streaming environments, the usual case is for data to arrive with a (variable) delay from, and to be revised by, the underlying sources. We propose RTECinc, an incremental version ...

The model checking problem for multi-agent systems against specifications in the alternating-time temporal logic ATL, hence ATL∗, under perfect recall and imperfect information is known to be undecidable. To tackle this problem, in this paper we ...

In ontology-based applications, the authoring of complex concepts or queries written in a description logic (DL) is a difficult task. An established approach to generate complex expressions from examples provided a user, is the bottom-up approach. This ...

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined, parametric probability distributions. Under the operational contexts of both strong and dynamic control, this paper addresses robustness measure ...

This paper reports on an ongoing development of a tool for extracting structured information on events a given target entity participated in from massive collections of textual documents and anchoring these events on a timescale. An overview of the ...

Early studies about user modeling already noted that user models need to continuously adapt, as new information about the user becomes available, keeping the user model updated. Although temporal aspects are inherent characteristics of user modeling, so ...

Clinical practice guidelines are assuming a major role in the medical area, to provide physicians with evidence-based recommendations for the treatment of single pathologies. The treatment of comorbid patients (i.e., patients affected by multiple ...

Temporal reasoning, in the form of propagation of temporal constraints, is an important topic in Artificial Intelligence. The current literature in the area is moving from the treatment of "crisp" temporal constraints to fuzzy or probabilistic ...

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework ...

Aspects of time, change, evolution, temporal reasoning and decision-making remain in the focus of metadata and ontology engineering because many related challenges are yet to be fully addressed. In this paper, we present ontology ALLEN+ as a tool to ...

In the context of human action recognition from video sequences in the medical environment, a Temporal Belief-based Hidden Markov Model (HMM) is presented. It allows to cope with human action temporality and enables to manage the data uncertainty and ...

On the Web, where resources such as documents and data are published, shared, transformed, and republished, provenance is a crucial piece of metadata that would allow users to place their trust in the resources they access. The open provenance model (...

Temporal reasoning (i.e., reasoning about relationships across time) is complex and difficult, particularly when engaged through complex media such as online Geographic Information System (GIS) applications. Partnering with Social Explorer (SE), a Web-...

One of the most important challenges of the creation of intelligent environments is the specifications of what intelligent behaviours the system will exhibit. The processing of these situations can be computationally demanding. We report on the advances ...