Giovanni Guzmán

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.

Geospatial information integration based on the conceptualization of geographic domain

Geospatial information integration is not a trivial task. An integrated view must be able to describe various heterogeneous data sources and its interrelation to obtain shared conceptualizations. Up-to-date, there are different and public ontologies for many domains and applications. Ontology engineering is rapidly becoming a mature discipline, which has produced various tools and methodologies for building and managing ontologies. However, even with a clearly defined engineering methodology, building a large ontology remains a challenging, time-consuming and error-prone task, since 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 paper, an approach to conceptualize the geographic domain is described. As a result of this conceptualization, we propose a semantic method for geospatial information integration. This consists of providing semantic descriptions, which explicitly describe the properties and relations of geographic objects represented by concepts, while the behavior describes the objects semantics. Summing up, this work presents a methodology allowing integrate and share geospatial information. It provides feasible solutions towards these and other related issues such as compact data by alternative structures of knowledge representation and avoids the ambiguity of these terms, using a geographic domain conceptualization. The general vision of the paper is to establish the basis to implement semantic processing oriented to geospatial data. Future works are focused on designing intelligent geographic information systems (iGIS).

Object counting without conglomerate separation

Object counting is an important problem in image analysis. All the proposed techniques until now need to split someway the conglomerates of objects in the image before determining their number. The improved technique here proposed uses the skeleton of the image to accomplish the same task. The original technique was first introduced by H. Sossa and G. Guzman (2000). The improved technique also uses the number of terminal points, NTps (points with just one neighbor) and the number of three-edge-points, NTEps (points with just three neighbors) in the skeleton to compute the desired number of objects in the image. The approach is applicable to the case of objects such that when its skeleton is obtained the sum of (NTps+NTEps)=2. The technique can efficiently handle noisy branches in the skeleton and the undesired decomposition of crossing points into three-edge-points normally present when an image is skeletonized. This was not done for the original technique. Both the original and the improved technique are compared in several scenarios.

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.

Geospatial Information Integration Approach Based on Geographic Context Ontologies

Geospatial information integration is not a trivial task. An integrated view must be able to describe various heterogeneous data sources and its interrelation to obtain shared conceptualizations. In this work, an approach to geospatial information integration based on the conceptualization of the geographic domain is described. As a result of this conceptualization, we propose a semantic method for geospatial information integration. This consists of providing semantic descriptions, which explicitly describe the properties and relations of geographic objects represented by concepts, while the behavior depicts the objects, semantics. Also, this method allows us to compress and share geospatial information by means of alternative structures of knowledge representation. Thus, it avoids the ambiguity of the terms, using a geographic domain conceptualization. The general vision of the paper is to establish the basis to implement semantic processing oriented to geospatial data. Future work is focused on designing intelligent geographic information systems (iGIS).

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.

Semantic Supervised Clustering Approach to Classify Land Cover in Remotely Sensed Images

GIS applications involve applying classification algorithms to remotely sensed images to determine information about a specific region on the Earth’s surface. These images are very useful sources of geographical data commonly used to classify land cover, analyze crop conditions, assess mineral and petroleum deposits and quantify urban growth. In this paper, we propose a semantic supervised clustering approach to classify multispectral information in satellite images. We use the maximum likelihood method to generate the clustering. In addition, we complement the analysis applying spatial semantics to determine the training sites and refine the classification. The approach considers the a priori knowledge of the remotely sensed images to define the classes related to the geographic environment. In this case, the properties and relations that involve the geo-image define the spatial semantics; these features are used to determine the training data sites. The method attempts to improve the supervised clustering, adding the intrinsic semantics of multispectral satellite images in order to establish the classes that involve the analysis with more precision.