Dissemination and geovisualization of territorial entities\u27 history

This paper describes an innovative solution for geovisualization of the demographic and administrative history of French municipalities named communes in French. This solution allows for the open dissemination of such data. The challenge is to provide a web interface for unskilled users in order to help them understand complex information about the demographic evolution of French territories. Our approach combines interactive thematic spatial and temporal views. We describe our architecture based on open-source technologies and the organization of this imperfect geo-historical information in our spatiotemporal database. Our second contribution concerns the concept of an acquaintance graph that has been used to obtain an efficient design with good performance in our geovisualization website

Neurosymbolic AI for Reasoning on Graph Structures: A Survey

Neurosymbolic AI is an increasingly active area of research which aims to combine symbolic reasoning methods with deep learning to generate models with both high predictive performance and some degree of human-level comprehensibility. As knowledge graphs are becoming a popular way to represent heterogeneous and multi-relational data, methods for reasoning on graph structures have attempted to follow this neurosymbolic paradigm. Traditionally, such approaches have utilized either rule-based inference or generated representative numerical embeddings from which patterns could be extracted. However, several recent studies have attempted to bridge this dichotomy in ways that facilitate interpretability, maintain performance, and integrate expert knowledge. Within this article, we survey a breadth of methods that perform neurosymbolic reasoning tasks on graph structures. To better compare the various methods, we propose a novel taxonomy by which we can classify them. Specifically, we propose three major categories: (1) logically-informed embedding approaches, (2) embedding approaches with logical constraints, and (3) rule-learning approaches. Alongside the taxonomy, we provide a tabular overview of the approaches and links to their source code, if available, for more direct comparison. Finally, we discuss the applications on which these methods were primarily used and propose several prospective directions toward which this new field of research could evolve.Comment: 21 pages, 8 figures, 1 table, currently under review. Corresponding GitHub page here: https://github.com/NeSymGraph

Accommodating Complex Chained Prepositional Phrases in Natural Language Query Interface to an Event-Based Triplestore

Building Natural language query interfaces (NLI) to databases is one the most interesting and challenging fields of study for computer scientists and researchers. There have been many advancements and achievements in this area that enables NLIs to operate more efficiently and have wide NL coverage. However, there exists some shortcomings in query interface to semantic web triplestores. Some researchers have attempted to extend the range of queries that can be answered. However, only a few techniques can handle queries containing complex chained prepositional phrases. This thesis involves extending an existing method that can accommodate prepositional phrases to also be able to handle when..., where..., and with what... type queries. The approach developed is implemented in the Miranda programing environment

Tracking Data Provenance of Archaeological Temporal Information in Presence of Uncertainty

The interpretation process is one of the main tasks performed by archaeologists who, starting from ground data about evidences and findings, incrementally derive knowledge about ancient objects or events. Very often more than one archaeologist contributes in different time instants to discover details about the same finding and thus, it is important to keep track of history and provenance of the overall knowledge discovery process. To this aim, we propose a model and a set of derivation rules for tracking and refining data provenance during the archaeological interpretation process. In particular, among all the possible interpretation activities, we concentrate on the one concerning the dating that archaeologists perform to assign one or more time intervals to a finding to define its lifespan on the temporal axis. In this context, we propose a framework to represent and derive updated provenance data about temporal information after the mentioned derivation process. Archaeological data, and in particular their temporal dimension, are typically vague, since many different interpretations can coexist, thus, we will use Fuzzy Logic to assign a degree of confidence to values and Fuzzy Temporal Constraint Networks to model relationships between dating of different findings represented as a graph-based dataset. The derivation rules used to infer more precise temporal intervals are enriched to manage also provenance information and their following updates after a derivation step. A MapReduce version of the path consistency algorithm is also proposed to improve the efficiency of the refining process on big graph-based datasets