Jorge Martinez-Gil

Evolutionary algorithm based on different semantic similarity functions for synonym recognition in the biomedical domain

One of the most challenging problems in the semantic web field consists of computing the semantic similarity between different terms. The problem here is the lack of accurate domain-specific dictionaries, such as biomedical, financial or any other particular and dynamic field. In this article we propose a new approach which uses different existing semantic similarity methods to obtain precise results in the biomedical domain. Specifically, we have developed an evolutionary algorithm which uses information provided by different semantic similarity metrics. Our results have been validated against a variety of biomedical datasets and different collections of similarity functions. The proposed system provides very high quality results when compared against similarity ratings provided by human experts (in terms of Pearson correlation coefficient) surpassing the results of other relevant works previously published in the literature.

An overview of textual semantic similarity measures based on web intelligence

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is a key challenge in many computer related fields. The problem is that traditional approaches to semantic similarity measurement are not suitable for all situations, for example, many of them often fail to deal with terms not covered by synonym dictionaries or are not able to cope with acronyms, abbreviations, buzzwords, brand names, proper nouns, and so on. In this paper, we present and evaluate a collection of emerging techniques developed to avoid this problem. These techniques use some kinds of web intelligence to determine the degree of similarity between text expressions. These techniques implement a variety of paradigms including the study of co-occurrence, text snippet comparison, frequent pattern finding, or search log analysis. The goal is to substitute the traditional techniques where necessary.

CoTO: A novel approach for fuzzy aggregation of semantic similarity measures

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. There are a lot of semantic similarity measures for addressing this problem. However, the best results have been achieved when aggregating a number of simple similarity measures. This means that after the various similarity values have been calculated, the overall similarity for a pair of text expressions is computed using an aggregation function of these individual semantic similarity values. This aggregation is often computed by means of statistical functions. In this work, we present CoTO (Consensus or Trade-Off) a solution based on fuzzy logic that is able to outperform these traditional approaches.

Modeling User Behavior through Electricity Consumption Patterns

Reducing energy consumption in buildings of all kinds is a key challenge for researchers since it can help to notably reduce the waste of energy and its associated costs. However, when dealing with residential environments, there is a major problem, people comfort should not be altered, so it is necessary to look for smart methods which take into account this circumstance. Traditional techniques have not considered the study of human behavior when providing solutions in this field, but new human-centric paradigms are emerging gradually. We present our research on user behavior concerning electricity consumption in office buildings and residential environments. Our goal consists of inspiring practitioners in this field for developing new human-aware solutions.

Automated knowledge base management: A survey

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developingmethods to automatically manage Knowledge Bases which can serve as a knowledge source for computer systems trying to replicate the decision-making ability of human experts. Despite of most of the tasks involved in the building, exploitation and maintenance of KBs are far from being trivial, and significant progress has been made during the last years. However, there are still a number of challenges that remain open. In fact, there are some issues to be addressed in order to empirically prove the technology for systems of this kind to be mature and reliable. c ⃝ 2015 Published by Elsevier Inc.

A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain

We face the complex problem of timely, accurate and mutually satisfactory mediation between job offers and suitable applicant profiles by means of semantic processing techniques. In fact, this problem has become a major challenge for all public and private recruitment agencies around the world as well as for employers and job seekers. It is widely agreed that smart algorithms for automatically matching, learning, and querying job offers and candidate profiles will provide a key technology of high importance and impact and will help to counter the lack of skilled labor and/or appropriate job positions for unemployed people. Additionally, such a framework can support global matching aiming at finding an optimal allocation of job seekers to available jobs, which is relevant for independent employment agencies, e.g. in order to reduce unemployment.

Extending Knowledge-Based Profile Matching in the Human Resources Domain

In the Human Resources domain the accurate matching between job positions and job applicants profiles is crucial for job seekers and recruiters. The use of recruitment taxonomies has proven to be of significant advantage in the area by enabling semantic matching and reasoning. Hence, the development of Knowledge Bases (KB) where curricula vitae and job offers can be uploaded and queried in order to obtain the best matches by both, applicants and recruiters is highly important. We introduce an approach to improve matching of profiles, starting by expressing jobs and applicants profiles by filters representing skills and competencies. Filters are used to calculate the similarity between concepts in the subsumption hierarchy of a KB. This is enhanced by adding weights and aggregates on filters. Moreover, we present an approach to evaluate over-qualification and introduce blow-up operators that transform certain role relations such that matching of filters can be applied.

Top-k Matching Queries for Filter-Based Profile Matching in Knowledge Bases

Finding the best matching job offers for a candidate profile or, the best candidates profiles for a particular job offer, respectively constitutes the most common and most relevant type of queries in the Human Resources (HR) sector. This technically requires to investigate top-k queries on top of knowledge bases and relational databases. We propose in this paper a top-k query algorithm on relational databases able to produce effective and efficient results. The approach is to consider the partial order of matching relations between jobs and candidates profiles together with an efficient design of the data involved. In particular, the focus on a single relation, the matching relation, is crucial to achieve the expectations.

Realistic User Behavior Modeling for Energy Saving in Residential Buildings

Due to the high costs of live research, performance simulation has become a widely accepted method of assessment for the quality of proposed solutions in this field. Additionally, being able to simulate the behavior of the future occupants of a residential building can be very useful since it can support both design-time and run-time decisions leading to reduced energy consumption through, e.g., the design of model predictive controllers that incorporate user behavior predictions. In this work, we provide a framework for simulating user behavior in residential buildings. In fact, we are interested in how to deal with all user behavior aspects so that these computer simulations can provide a realistic framework for testing alternative policies for energy saving.

An Overview of Knowledge Management Techniques for e-Recruitment

The number of potential job candidates and therefore, costs associated with their hiring, has grown signi ̄cantly in the recent years. This is mainly due to both the complicated situation of the labour market and the increased geographical °exibility of employees. Some initiatives for making the e-Recruitment processes more e±cient have notably improved the situation by developing automatic solutions. But there are still some challenges that remain open since traditional solutions do not consider semantic relations properly. This problem can be appropriately addressed by means of a sub discipline of knowledge management called semantic processing. Therefore, we overview the major techniques from this ̄eld that can play a key role in the design of a novel business model that is more attractive for job applicants and job providers.The number of potential job candidates and therefore, costs associated with their hiring, has grown significantly in the recent years. This is mainly due to both the complicated situation of the labour market and the increased geographical flexibility of employees. Some initiatives for making the e-Recruitment processes more efficient have notably improved the situation by developing automatic solutions. But there are still some challenges that remain open since traditional solutions do not consider semantic relations properly. This problem can be appropriately addressed by means of a sub discipline of knowledge management called semantic processing. Therefore, we overview the major techniques from this field that can play a key role in the design of a novel business model that is more attractive for job applicants and job providers.