Jorge Martinez-Gil

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.

Tags: matching human resources

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

Tags: semantic similarity measurement

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods 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.

Tags: automated knowledge knowledge base

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.

Nowadays many techniques and tools are available for addressing the ontology matching problem, however, the complex nature of this problem causes existing solutions to be unsatisfactory. This work aims to shed some light on a more flexible way of matching ontologies. Ontology meta-matching, which is a set of techniques to configure optimum ontology matching functions. In this sense, we propose two approaches to automatically solve the ontology meta-matching problem. The first one is called maximum similarity measure, which is based on a greedy strategy to compute efficiently the parameters which configure a composite matching algorithm. The second approach is called genetics for ontology alignments and is based on a genetic algorithm which scales better for a large number of atomic matching algorithms in the composite algorithm and is able to optimize the results of the matching process.

Tags: evaluation ontology problem

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.

Tags: semantic similarity measures

Semantic similarity measures

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.

Computing the semantic similarity between terms (or short text expressions) that have the same meaning but which are not lexicographically similar is an important challenge in the information integration field. The problem is that techniques for textual semantic similarity measurement often fail to deal with words not covered by synonym dictionaries. In this paper, we try to solve this problem by determining the semantic similarity for terms using the knowledge inherent in the search history logs from the Google search engine. To do this, we have designed and evaluated four algorithmic methods for measuring the semantic similarity between terms using their associated history search patterns. These algorithmic methods are: a) frequent co-occurrence of terms in search patterns, b) computation of the relationship between search patterns, c) outlier coincidence on search patterns, and d) forecasting comparisons. We have shown experimentally that some of these methods correlate well with respect to human judgment when evaluating general purpose benchmark datasets, and significantly outperform existing methods when evaluating datasets containing terms that do not usually appear in dictionaries.

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.

Nowadays, there are a lot of techniques and tools for addressing the ontology matching problem; however, the complex nature of this problem means that the existing solutions are unsatisfactory. This work intends to shed some light on a more flexible way of matching ontologies using ontology meta-matching. This emerging technique selects appropriate algorithms and their associated weights and thresholds in scenarios where accurate ontology matching is necessary. We think that an overview of the problem and an analysis of the existing state-of-the-art solutions will help researchers and practitioners to identify the most appropriate specific features and global strategies in order to build more accurate and dynamic systems following this paradigm.

A fundamental challenge in the intersection of Artificial Intelligence and Databases consists of developing methods 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.

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.

This paper presents two main contributions. The authors propose a novel technique that beats classic probabilistic techniques for measuring semantic similarity between terms. This new technique consists of using not only a search engine for computing web page counts, but a smart combination of several popular web search engines. The approach is evaluated on the Miller and Charles and Gracia and Mena benchmark datasets and compared with existing probabilistic web extraction techniques.

The problem of matching schemas or ontologies consists of providing corresponding entities in two or more knowledge models that belong to a same domain but have been developed separately. Nowadays there are a lot of techniques and tools for addressing this problem, however, the complex nature of the matching problem make existing solutions for real situations not fully satisfactory. The Google Similarity Distance has appeared recently. Its purpose is to mine knowledge from the Web using the Google search engine in order to semantically compare text expressions. Our work consists of developing a software application for validating results discovered by schema and ontology matching tools using the philosophy behind this distance. Moreover, we are interested in using not only Google, but other popular search engines with this similarity distance. The results reveal three main facts. Firstly, some web search engines can help us to validate semantic correspondences satisfactorily. Secondly there are significant differences among the web search engines. And thirdly the best results are obtained when using combinations of the web search engines that we have studied.

In this work, we present our experience when developing the Matching Framework (MaF), a framework for matching ontologies that allows users to configure their own ontology matching algorithms and it allows developers to perform research on new complex algorithms. MaF provides numerical results instead of logic results provided by other kinds of algorithms. The framework can be configured by selecting the simple algorithms which will be used from a set of 136 basic algorithms, indicating exactly how many and how these algorithms will be composed and selecting the thresholds for retrieving the most promising mappings. Output results are provided in a standard format so that they can be used in many existing tools (evaluators, mediators, viewers, and so on) which follow this standard. The main goal of our work is not to better the existing solutions for ontology matching, but to help research new ways of combining algorithms in order to meet specific needs. In fact, the system can test more than 6 * 136! possible combinations of algorithms, but the graphical interface is designed to simplify the matching process.

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.