Marco Moreno-Ibarra

An ontology-based approach for representing the interaction process between user profile and its context for collaborative learning environments

This work is focused on defining user profiles based on ontologies.The research proposes the evolution operation that interacts with ontologies.This work defines an approach to represent the interaction process between user profiles and their context.The application of this work is oriented toward establishing adequate user profiles for recommender systems. Recent researches about the personalized content generation have focused their efforts on two main topics: the first topic is the user model definition, i.e. the dimensions to be taken into account to represent the user, and the second topic is about the techniques used by recommender systems to provide recommendations according to the user requirements, such as adaptive approaches for context-aware systems, collaborative learning, and recommender systems for mobile environments. In this work, an approach based on ontologies to represent the interaction process between user profile and its context for collaborative learning is presented. We also analyzed the role assignments, permissions, restrictions and the definition of rules that are applied to the user, particularly in the collaborative learning context where the subject is involved. A case study related to the context of a school as well as the defined roles by the occupations in the context of locations is proposed.

GEONTO-MET: an approach to conceptualizing the geographic domain

To date, there are different ontologies for many domains and applications. Users can access them to share information, reuse knowledge, and integrate data sources for several purposes and applications such as semantic web, data warehousing, e-learning, e-commerce, knowledge representation, and so on. Ontology engineering is rapidly becoming a mature discipline, having produced tools and methodologies for building and managing ontologies. However, even with a clearly defined engineering methodology, building an ontology remains a challenging, time-consuming, and error-prone task, because it forces ontology builders to conceptualize their expert knowledge explicitly and to re-organize it in typical ontological categories such as concepts, properties, and axioms. In this article, an approach to conceptualizing the geographic domain is described. It is oriented toward formalizing a geographic domain conceptualization according to specifications from the Mexican Institute of Statistics, Geography and Informatics. The main goal is to provide semantic and ontological descriptions, which represent the properties and relationships that describe the behavior of geographic objects by means of concepts. GEONTO-MET is focused on developing geographic application ontologies for sharing and integrating geospatial information.

A collaborative learning approach for geographic information retrieval based on social networks

Method for geographic information retrieval based on matching-query layers is defined.The approach builds an ontology that defines the time, space and social features.The work defines particular cases in GIR for GIScience collaborative learning.Application of the work establishes adequate user profiles for information retrieval. Nowadays, spatial and temporal data play an important role in social networks. These data are distributed and dispersed in several heterogeneous data sources. These peculiarities make that geographic information retrieval being a non-trivial task, considering that the spatial data are often unstructured and built by different collaborative communities from social networks. The problem arises when user queries are performed with different levels of semantic granularity. This fact is very typical in social communities, where users have different levels of expertise. In this paper, a novelty approach based on three matching-query layers driven by ontologies on the heterogeneous data sources is presented. A technique of query contextualization is proposed for addressing to available heterogeneous data sources including social networks. It consists of contextualizing a query in which whether a data source does not contain a relevant result, other sources either provide an answer or in the best case, each one adds a relevant answer to the set of results. This approach is a collaborative learning system based on experience level of users in different domains. The retrieval process is achieved from three domains: temporal, geographical and social, which are involved in the user-content context. The work is oriented towards defining a GIScience collaborative learning for geographic information retrieval, using social networks, web and geodatabases.

Semantic similarity applied to generalization of geospatial data

The paper presents an approach to verifying the consistency of generalized geospatial data at a conceptual level. The principal stages of the proposed methodology are Analysis, Synthesis, and Verification. Analysis is focused on extracting the peculiarities of spatial relations by means of quantitative measures. Synthesis is used to generate a conceptual representation (ontology) that explicitly and qualitatively represents the relations between geospatial objects, resulting in tuples called herein semantic descriptions. Verification consists of a comparison between two semantic descriptions (description of source and generalized data): we measure the semantic distance (confusion) between ontology local concepts, generating three global concepts Equal, Unequal, and Equivalent. They measure the (in) consistency of generalized data: Equal and Equivalent - their consistency, while Unequal - an inconsistency. The method does not depend on coordinates, scales, units of mea-sure, cartographic projection, representation format, geometric primitives, and so on. The approach is applied and tested on the generalization of two topographic layers: rivers and elevation contour lines (case of study).

A cross-domain framework for designing healthcare mobile applications mining social networks to generate recommendations of training and nutrition planning

Abstract Nowadays, people are practicing physical exercise in order to maintain good health conditions. Such physical workouts are required by a plan, which should be designed and supervised by sport specialists and medical assistants. Thus, the exercise sessions shall start with consultation of a coach, doctor and dietician; however, many times this scenario is not presented. In typical activities such as running, cycling and fitness, people use health mobile apps with their smartphones, which offer support for these activities. Nevertheless, the functionality and operation of these applications are isolated, because many and long questionnaires are performed. Additionally, the physical and health state of a user is not considered. These issues would be taken into account for determining recommendations about the time for doing exercise and the kind of activity for each person. In this work, a social semantic mobile framework to generate recommendations where a mobile application allows sensing the physical performance, taking into consideration medical criteria with smartphones is proposed. The approach includes a semantic cross-information that comes from social network and official data as well as sport activities and medical knowledge. This knowledge is translated into application ontologies related directly to health, nutrition and training domains. The methodology also covers physical fitness tests and a monitoring tool for evaluating the nutrition plan and the correct execution of the training. As case study, the mobile application offers to evaluate the physical and health conditions of a runner, automatically generate a nutrition plan and training, monitor plans and recomputed them if users make changes in their routines. The data provided from the social network are used as feedback in the application, in order to make the training and nutrition plans more flexible by applying spatio-temporal analysis based on machine learning. Finally, the generated training and nutrition plans were validated by specialists, they have demonstrated 82% of effectiveness rate in exercise training routines and 86% in nutrition plans. In addition, the results were compared with isolated approaches and manual recommendations made by specialists, the obtained overall performance was 81%.

Geocoding Tweets Approach Based on Conceptual Representations in the Context of the Knowledge Society

In this paper, an approach to geocode tweets published in Spanish is proposed. The tweets are related to traffic events within an urban context of the Mexico City. They are generated by a particular phenomenon for knowing the behavior of the involved geographic entities. In order to disambiguate and verify the consistency of information, an application ontology was defined. Thus, the core goal is to identify location as well as spatial relationships between entities presented in the events, using semantic and spatial analysis of the collected dataset. In consequence, a visualization method for presenting the results was also proposed. The paper describes the methodology for enabling the discovery of spatial patterns within traffic tweets and provides useful information to make timely decisions and contribute in the context of Knowledge Society.

A Mobile Information System Based on Crowd-Sensed and Official Crime Data for Finding Safe Routes: A Case Study of Mexico City

Mobile information systems agendas are increasingly becoming an essential part of human life and they play an important role in several daily activities. These have been developed for different contexts such as public facilities in smart cities, health care, traffic congestions, e-commerce, financial security, user-generated content, and crowdsourcing. In GIScience, problems related to routing systems have been deeply explored by using several techniques, but they are not focused on security or crime rates. In this paper, an approach to provide estimations defined by crime rates for generating safe routes in mobile devices is proposed. It consists of integrating crowd-sensed and official crime data with a mobile application. Thus, data are semantically processed by an ontology and classified by the Bayes algorithm. A geospatial repository was used to store tweets related to crime events of Mexico City and official reports that were geocoded for obtaining safe routes. A forecast related to crime events that can occur in a certain place with the collected information was performed. The novelty is a hybrid approach based on semantic processing to retrieve relevant data from unstructured data sources and a classifier algorithm to collect relevant crime data from official government reports with a mobile application.

Traffic Analysis Based on Short Texts from Social Media

Social networks provide information about activities of humans and social events. Thus, with the help of social networks, we can extract the traffic events that occur in a city. In the context of an urban area, this kind of data allows to obtaining contextual real-time information shared among citizens that will be useful to address social, environmental and economic issues. In this paper, the authors describe a methodology to obtain information related to traffic events such as accidents or congestion, from Twitter messages and RSS services. A text mining process is applied on the messages to acquire the relevant data, then data are classified by using a machine learning algorithm. The events are geocoded and transformed into geometric points to be represented on a map. The final repository lets data to be available for further works related to the traffic events on the study area. As a case of study we consider Mexico City.

Environmental Noise Sensing Approach Based on Volunteered Geographic Information and Spatio-Temporal Analysis with Machine Learning

In this paper a methodology for analyzing the behavior of the environmental noise pollution is proposed. It consists of a mobile application called ‘NoiseMonitor’, which senses the environmental noise with the microphone sensor available in the mobile device. The georeferenced noise data constitute Volunteered Geographic Information that compose a large geospatial database of urban information of the Mexico City. In addition, a Web-GIS is proposed in order to make spatio-temporal analysis based on a prediction model, applying Machine Learning techniques to generate acoustic noise mapping with contextual information.According to the obtained results, a comparison between support vector machines and artificial neural networks were performed in order to evaluate the model and the behavior of the sensed data.

A recommender system to generate museum itineraries applying augmented reality and social-sensor mining techniques

Nowadays, museums offer technological and digital options to enrich the user experience in a visit. However, questions arise like which exhibition/museum could I visit? How to tour it and get the best experience? These questions are not easy to answer, because they do not represent tasks straightforward. Considering that the experiences of visiting a museum are now available in social networks, in which users describe, rate, and disseminate a work of art/exhibition of a museum, this information can be mined to generate tour recommendations in museums. Such recommendations could be improved by combining and applying data mining obtained from Internet of Things sensors installed in museums. In this paper, a hybrid approach to make recommendations for museum visits is proposed. It includes an Internet of Things architecture of beacons, incorporating some technologies based on semantic analysis, data mining, and machine learning. This approach integrates and combines data sources for generating and recommending indoor and outdoor itineraries for museums, which are visualized with augmented reality. The itinerary is built, taking into consideration opinions and assessments from social networks, the semantic classification of museums, and cultural activities, as well as data measured by beacon sensors in museum exhibitions. The result is a customized tour with augmented reality that contains a set of recommendations of how to visit a set of museums and obtain a better experience of the visit. A prototype of mobile application is available in the Google Play, called the “Historic Center,” with almost 500 downloads and an acceptable evaluation.