Jorge Martinez-Gil

Software Competence Center Hagenberg | SCCH · Data Analysis Systems Group

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...

In this work we present GOAL (Genetics for Ontology Align- ments) a new approach to compute the optimal ontology alignment func- tion for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method,is expected to scale better for a high number,of measures. Our a...

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...

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 dictionarie...

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 alg...

Data catalogs play a crucial role in modern data-driven organizations by facilitating the discovery, understanding, and utilization of diverse data assets. However, ensuring their quality and reliability is complex, especially in open and large-scale data environments. This paper proposes a framework to automatically determine the quality of open d...

Semantic similarity measures are widely used in natural language processing to catalyze various computer-related tasks. However, no single semantic similarity measure is the most appropriate for all tasks, and researchers often use ensemble strategies to ensure performance. This research work proposes a method for automatically designing semantic s...

The issue of word sense ambiguity poses a significant challenge in natural language processing due to the scarcity of annotated data to feed machine learning models to face the challenge. Therefore, unsupervised word sense disambiguation methods have been developed to overcome that challenge without relying on annotated data. This research proposes...

Many legal professionals think the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and error-prone. The two main reasons are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overl...

Recently, transfer learning strategies have become ideal for reusing acquired knowledge through a training phase. The key idea is that reusing such knowledge brings advantages such as increased accuracy and considerable resource savings. In this work, we design a novel strategy for effective and efficient transfer learning in semantic similarity. O...

Technical standards help software architects to identify relevant requirements and to facilitate system certification, i.e., to systematically assess whether a system meets critical requirements in fields like security, safety, or interoperability. Despite their usefulness, standards typically remain vague on how requirements should be addressed vi...

Artificial intelligence (AI) is a crucial technology of industrial digitalization. Especially in the production industry, a great potential is present in optimizing existing processes, e.g., concerning resource consumption, emission reduction, process and product quality improvements, predictive maintenance, and so on. Some of this potential is add...

In manufacturing industry, product failure is costly, as it results in financial and time losses. Understanding the causes of product failure is critical for reducing the occurrence of failure and optimising the manufacturing process. As a result, a number of studies utilising data-driven approaches such as machine learning have been conducted to r...

This research presents ORUGA, a method that tries to automatically optimize the readability of any text in English. The core idea behind the method is that certain factors affect the readability of a text, some of which are quantifiable (number of words, syllables, presence or absence of adverbs, and so on). The nature of these factors allows us to...

This article presents a comprehensive review of stacking methods commonly used to address the challenge of automatic semantic similarity measurement in the literature. Since more than two decades of research have left various semantic similarity measures, scientists and practitioners often find many difficulties in choosing the best method to put i...

The challenge of assessing semantic similarity between pieces of text through computers has attracted considerable attention from industry and academia. New advances in neural computation have developed very sophisticated concepts, establishing a new state of the art in this respect. In this paper, we go one step further by proposing new techniques...

This research work presents a new augmentation model for knowledge graphs (KGs) that increases the accuracy of knowledge graph question answering (KGQA) systems. In the current situation, large KGs can represent millions of facts. However, the many nuances of human language mean that the answer to a given question cannot be found, or it is not poss...

The problem of ontology matching consists of finding the semantic correspondences between two ontologies that, although belonging to the same domain, have been developed separately. Ontology matching methods are of great importance today since they allow us to find the pivot points from which an automatic data integration process can be established...

Up to this day the practical economical application of automated driving stays behind the high expectations that were raised by the great successes of Deep Learning. There is no doubt that fully autonomous (level 5 automated) driving in all weather and road conditions is a much more challenging problem then initially expected. Yet there is another...

The automatic semantic similarity assessment field has attracted much attention due to its impact on multiple areas of study. In addition, it is also relevant that recent advances in neural computation have taken the solutions to a higher stage. However, some inherent problems persist. For example, large amounts of data are still needed to train so...

This work shows how we have designed and implemented a digital platform for Smart Villages, which can serve as a knowledge management and decision support system for rural stakeholders, including planners, administrative staff, and decision-makers. Our platform is intended to help pilot a smooth transition into a sustainable administration, help to...

This work is a companion reproducibility paper that presents a framework to reproduce our previous experiments and results reported in Werneck et al. (2021). In that previous paper, we introduced a systematic mapping process of points-of-interest (POI) recommendation methods and provided a uniform evaluation methodology based on metrics covering di...

Ontology meta-matching techniques have been consolidated as one of the best approaches to face the problem of discovering semantic relationships between knowledge models that belong to the same domain but have been developed independently. After more than a decade of research, the community has reached a stage of maturity characterized by increasin...

In the industrial domain, developing solutions that allow the identification, understanding, and correction of faults is essential due to the cost of handling such situations. However, to date, there are not many solutions capable of facilitating the human operator to discern the causes and possible solutions for a specific fault. In this work, we...

Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released cause...

In recent times, we have seen an explosion in the number of new solutions to address the problem of semantic similarity. In this context, solutions of a neuronal nature seem to obtain the best results. However, there are some problems related to their low interpretability as well as the large number of resources needed for their training. In this w...

In recent times, the use of knowledge graphs has been massively adopted so that many of these graphs can even be found publicly on the Web. This makes that solutions for solving interoperability problems among them might be in high demand. The reason is that unifying these knowledge graphs could impact a wide range of industrial and academic discip...

With advancements in technology and big data availability, industries are struggling with data interoperability and knowledge representation. Ontologies have a great potential to solve such problems. However, the lack of standardisation prevents the widespread adoption of ontologies in different manufacturing domains. We investigate the possibility...

The problem of identifying the degree of semantic similarity between two textual statements automatically has grown in importance in recent times. Its impact on various computer-related domains and recent breakthroughs in neural computation has increased the opportunities for better solutions to be developed. This research takes the research effort...

This study presents our ongoing research on designing new methods for changepoint detection in industrial environments using a CUSUM method variant. The changepoint detection refers to identifying the location of change of some aspect in a given time series. The significant difference concerning a state-of-the-art time series prediction technique (...

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applie...

In various industrial production environments a burn-in phase of a specific settling-length precedes a stable (i.e. steady-state, stationary) process mode. The identification of the corresponding physical parameters may be difficult to perform in the presence of strong noise. We propose a method using isotonic regression which circumvents the negat...

In various industrial production environments a burn-in phase of a specific settling-length precedes a stable (i.e. steady-state, stationary) process mode. The identification of the corresponding physical parameters may be difficult to perform in the presence of strong noise. We propose a method using isotonic regression which circumvents the negat...

This volume constitutes the refereed proceedings of the workshops held at the 32nd International Conference on Database and Expert Systems Applications, DEXA 2021, held in a virtual format in September 2021: The 12th International Workshop on Biological Knowledge Discovery from Data (BIOKDD 2021), the 5th International Workshop on Cyber-Security an...

p>With advancements in technology and big data availability, industries are struggling with data interoperability and knowledge representation. Ontologies have a great potential to solve such problems. However, the lack of standardisation prevents the widespread adoption of ontologies in different manufacturing domains. We investigate the possibili...

Knowledge graphs in manufacturing and production aim to make production lines more efficient and flexible with higher quality output. This makes knowledge graphs attractive for companies to reach Industry 4.0 goals. However, existing research in the field is quite preliminary, and more research effort on analyzing how knowledge graphs can be applie...

Automatic matching of job offers and job candidates is a major problem for a number of organizations and job applicants that if it were successfully addressed could have a positive impact in many countries around the world. In this context, it is widely accepted that semi-automatic matching algorithms between job and candidate profiles would provid...

In recent times, the digitalization of urban areas has got considerable attention from the public. As a side effect, there has also been great interest in the digitalization of the rural world or the so-called Smart Villages. Smart Villages refer to the improvement of infrastructure management and planning to fight against depopulation and low popu...

The problem of automatically measuring the degree of semantic similarity between textual expressions is a challenge that consists of calculating the degree of likeness between two text fragments that have none or few features in common according to human judgment. In recent times, several machine learning methods have been able to establish a new s...

One of the major problems in the manufacturing industry consists of the fact that, when manufacturing a product, many parts from different lots are supplied and mixed to a certain degree during an indeterminate number of stages, what makes it very difficult to trace each of these parts from its origin to its presence in a final product. In order to...

This volume constitutes the refereed proceedings of the three workshops held at the 31st International Conference on Database and Expert Systems Applications, DEXA 2020, held in September 2020: The 11th International Workshop on Biological Knowledge Discovery from Data, BIOKDD 2020, the 4th International Workshop on Cyber-Security and Functional Sa...

As a result of the continuously growing volume of information available, browsing and querying of textual information in search of specific facts is currently a tedious task exacerbated by a reality where data presentation very often does not meet the needs of users. To satisfy these ever-increasing needs, we have designed an solution to provide an...

The concept of smartness is an essential topic that was only recently extended to rural areas. Although smartness is already incorporated strongly into numerous urban environments, differences between cities and villages prevent direct transfer of the methods and tools used for the smart transformation. To increase the awareness of newly developed...

Semantic similarity measurement aims to automatically compute the degree of similarity between two textual expressions that use different representations for naming the same concepts. However, very short textual expressions cannot always follow the syntax of a written language and, in general, do not provide enough information to support proper ana...

In this work, we have developed the first version of a smartness assessment framework that allows the representatives from a village to make a self-evaluation of its current status based on smartness criteria identified by an international group of experts. The framework allows a detailed evaluation of six different aspects including Mobility, Gove...

One of the major problems in the manufacturing industry consists of the fact that many parts from different lots are supplied and mixed to a certain degree during an indeterminate number of stages, what makes it very difficult to trace each of these parts from its origin to its presence in a final product. In order to overcome this limitation, we h...

Nowadays, the volume of legal information available is continuously growing. As a result, browsing and querying this huge legal corpus in search of specific information is currently a tedious task exacerbated by the fact that data presentation does not usually meet the needs of professionals in the sector. To satisfy these ever-increasing needs, we...

The Social Insurance industry can be considered as a basic pillar of the welfare state in many countries around the world. However, there is not much public research work on how to prevent social fraud. And the few published works are oriented towards detecting fraud on the side of the employees or providers. In this work, our aim is to describe ou...

Recent advances in machine learning have been able to make improvements over the state-of-the-art regarding semantic similarity measurement techniques. In fact, we have all seen how classical techniques have given way to promising neural techniques. Nonetheless, these new techniques have a weak point: they are hardly interpretable. For this reason,...

Purpose The purpose of this study is to automatically provide suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Design/methodology/approach Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting...

The design of reliable DNA libraries that can be used for bio-molecular computing involves several heterogeneous conflicting design criteria that traditional optimization approaches do not fit properly. As it is well known, evolutionary algorithms are very appropriate for solving complex NP-hard optimization problems. However, these approaches take...

The challenge of automatically recommending job offers to appropriate job seekers is a topic that has attracted many research effort during the last times. However, it is generally assumed that there is a need of more user-friendly filtering methods so that the automated recommendation systems might be more widely used. We present here our research...

In this work we present GOAL (Genetics for Ontology Align- ments) a new approach to compute the optimal ontology alignment func- tion for a given ontology input set. Although this problem could be solved by an exhaustive search when the number of similarity measures is low, our method,is expected to scale better for a high number,of measures. Our a...

The current paper describes our research towards a cloud infrastructure for the universal access and interaction with a number of services implementing methods for enriching, matching and querying information about job offers and applicant profiles in the cloud. These methods exploit well-known recruitment knowledge bases in order to deliver valuab...

Automatically providing suggestions for predicting the likely status of a mechanical component is a key challenge in a wide variety of industrial domains. Existing solutions based on ontological models have proven to be appropriate for fault diagnosis, but they fail when suggesting activities leading to a successful prognosis of mechanical componen...

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 a...

Semantic similarity measurement aims to determine the likeness between two text expressions that use different lexicographies for representing the same real object or idea. In this work, we describe the way to exploit broad cultural trends for identifying semantic similarity. This is possible through the quantitative analysis of a vast digital book...

Ontology Matching aims to find the semantic correspondences between ontologies that belong to a single domain but that have been developed separately. However, there are still some problem areas to be solved, because experts are still needed to supervise the matching processes and an efficient way to reuse the alignments has not yet been found. We...

A profile describes a set of properties, e.g. a set of skills a person may have or a set of skills required for a particular job. Profile matching aims to determine how well a given profile fits to a requested profile. Profiles can be defined by filters in a lattice of concepts derived from a knowledge base that is grounded in description logic, an...

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...

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 dictionarie...

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...

Semantic similarity measures are very important in many computer‐related fields. Previous works on applications such as data integration, query expansion, tag refactoring or text clustering have used some semantic similarity measures in the past. Despite the usefulness of semantic similarity measures in these applications, the problem of measuring...

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...

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...

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...

The number of potential job candidates, and therefore costs associated to their hiring, has grown significantly in the recent years. This is mainly due to both the complicated situation of the labor market and the increased geographical flexibility of employees. Some initiatives for making the e-Recruitment processes more efficient have notably imp...

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...

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 a...

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...

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...

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...

A profile describes a set of properties, e.g. a set of skills a person may have, a set of skills required for a particular job, or a set of abilities a football player may have with respect to a particular team strategy. Profile matching aims to determine how well a given profile fits to a requested profile. The approach taken in this article is gr...

A profile describes a set of properties, e.g. a set of skills a person may have or a set of skills required for a particular job. Profile matching aims to determine how well a given profile fits to a requested profile. Profiles can be defined by filters in a lattice of concepts derived from a knowledge base that is grounded in description logic, an...

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...

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...

Semantic similarity measurement of biomedical nomenclature aims to determine the likeness between two biomedical expressions that use different lexicographies for representing the same real biomedical concept. There are many semantic similarity measures for trying to address this issue, many of them have represented an incremental improvement over...

In this work we present SIFT, a 3-step algorithm for the analysis of the structural information represented by means of a taxonomy. The major advantage of this algorithm is the capability to leverage the information inherent to the hierarchical structures of taxonomies to infer correspondences which can allow to merge them in a later step. This met...

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...

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 a...