Alsayed Algergawy

• Dr. Eng.
• PostDoc Position at Friedrich Schiller University Jena

Ontology learning in complex domains, such as life sciences, poses significant challenges for current Large Language Models (LLMs). Existing LLMs struggle to generate ontologies with multiple hierarchical levels, rich interconnections, and comprehensive class coverage due to constraints on the number of tokens they can generate and inadequate domai...

Hypotheses are critical components of scientific argumentation. Knowing established hypotheses is often a prerequisite for following and contributing to scientific arguments in a research field. In scientific publications, hypotheses are usually presented for specific empirical settings, whereas the related general claim is assumed to be known. Pre...

Machine learning is getting more and more available for researchers outside the domain of computer sciences. For example, statistical methods for data mining that build on machine learning (e.g. random forests) are increasingly applied in ecology. These methods help with analyzing the increasing amounts of available data, thus allowing to identify...

This paper introduces DREIFLUSS, an innovative, minimalist approach designed to tackle the Column Type Annotation (CTA) and Column Property Annotation (CPA) tasks in the SemTab challenge. DREIFLUSS efficiently employs semantic information from well-established knowledge graphs, DBpedia, and Schema. org, to improve the annotation process. Experiment...

Computing the semantic similarity between pairs of terms plays a vital role within a myriad of shared data applications, such as data integration and ontology evolution. A first step towards building such applications is to determine which terms are semantically similar to each other. One feasible way to compute the similarity of two terms is to as...

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2023 campaign offered 15 tracks and was attended by 16 participants. This paper is an overall...

Biodiversity is the assortment of life on earth covering evolutionary, ecological, biological, and social forms. To preserve life in all its variety and richness, it is imperative to monitor the current state of biodiversity and its change over time and to understand the forces driving it. This need has resulted in numerous works being published in...

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities. The OAEI 2022 campaign offered 14 tracks and was attended by 18 participants. This paper is an overall...

Developing a precise argument is not an easy task. In real-world argumentation scenarios, arguments presented in texts (e.g. scientific publications) often constitute the end result of a long and tedious process. A lot of work on computational argumentation has focused on analyzing and aggregating these products of argumentation processes, i.e. arg...

Obtaining fit-to-use data associated with diverse aspects of biodiversity, ecology and environment is challenging since often it is fragmented, sub-optimally managed and available in heterogeneous formats. Recently, with the universal acceptance of the FAIR data principles, the requirements and standards of data publications have changed substantia...

Reproducibility is one of the fundamental characteristics of science. To reproduce scientific results, scientists need to manage and describe the provenance of end-to-end experimental pipelines. To understand , query, and reason how the results are derived, the provenance of the entire study needs to be described in an interoperable manner. Ontolog...

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consensus). The OAEI 2021 campaign offered 13 tracks and w...

One of the added values of long running and large scale collaborative projects is the ability to answer complex research questions based on the comprehensive set of data provided by their central repositories. In practice, however, finding data in such a repository to answer a specific question often proves to be a demanding task even for project s...

Biodiversity is the variety of life on earth which covers the evolutionary, ecological, and cultural processes that sustain life. Therefore, it is important to understand where biodiversity is, how it is changing over space and time, the driving factors of these changes and the resulting consequences on the diversity of life. To do so, it is necess...

With a rapidly growing body of knowledge, it becomes more and more difficult to keep track of the state of the art in a research field. A formal representation of the hypotheses in the field, their relations, the studies that support or question them based on which evidence, would greatly ease this task and help direct future research efforts. We p...

Ontologies are the backbone of the Semantic Web. As a result, the number of existing ontologies and the number of topics covered by them has increased considerably. With this, reusing these ontologies becomes preferable to constructing new ontologies from scratch. However, a user might be interested in a part and/or a set of parts of a given ontolo...

With a rapidly growing body of knowledge, it becomes more and more difficult to keep track of the state of the art in a research field. A formal representation of the hypotheses in the field, their relations, the studies that support or question them based on which evidence, would greatly ease this task and help direct future research efforts. We p...

Biodiversity research studies the variability and diversity of organisms, including variability within and between species with particular focus on the functional diversity of traits and their relationship to environment. Managing biodiversity data implies dealing with its heterogeneous nature using semantics and tailored ontologies. These are them...

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consen...

Multimedia resources are rapidly growing with a huge increase of visual contents. Thus, searching these images accurately and efficiently for all types of datasets becomes one of the most challenging tasks. Content-based image retrieval (CBIR) is the technique that retrieves images based on their visual contents. So that, selecting appropriate feat...

The Ontology Alignment Evaluation Initiative (OAEI) aims at comparing ontology matching systems on precisely defined test cases. These test cases can be based on ontologies of different levels of complexity (from simple thesauri to expressive OWL ontologies) and use different evaluation modalities (e.g., blind evaluation, open evaluation, or consen...

As the main way for knowledge representation for the purpose of machine understanding, ontologies are widely used in different application domains. This requires more and more domain specific information to be inserted into ontologies, making them harder to be easily understood by a human and there is a growing need to develop ontology visualizatio...

Despite the success achieved in the metadata models matching area, large-scale matching does not preserve high match quality and efficiency at the same time. To deal with these challenges, we introduce a generic matching framework, called MetMat, to identify and discover corresponding entities across XML schemas and/or ontologies (metadata models)....

As the main way for knowledge representation for the purpose of completely machine understanding, ontologies are widely used in different application domains. This full machine understanding makes them harder to be easily understood by a human. This necessitates the need to develop ontology visualization tools, which results in the existence of a l...

In an age where science is often interdisciplinary, it is frequently necessary to combine scientific data from different (sub-)disciplines and thus from different sources. Ontologies can play an important role in this integration process. However, existing ontologies will either cover just a part of the domain of interest or competing ontologies mo...

We introduce ADOnIS, an information system which coherently integrates two important, yet mostly disparate data sources, namely structured, tabular data, and unstructured data in terms of publications. The integration is achieved by providing the underlying background knowledge of the domains involved in terms of adequately tailored ontologies. Onc...

Semantic similarity plays a vital role within a myriad of shared data applications, such as data and information integration. A first step towards building such applications is to determine concepts, which are semantically similar to each other. One way to compute this similarity of two concepts is to assess their word similarity by exploiting diff...

We present Joyce, a scalable tool for identifying and assembling relevant (pieces of) ontologies from a repository of source ontologies, thus enabling the effective and efficient reuse of formalized domain knowledge. Joyce includes a conceptual filter to identify relevant classes, minimizes unintended redundancies, i.e. concept duplicates, and excl...

The main objective of the work is to improve the clustering efficiency and performance when we deal with very big datasets. This paper aims to improve the quality of XML data clustering by exploiting more features extracted from source schemas. In particular, it proposes clustering approach that gathers both content and structure of XML documents t...

Ontology matching plays a crucial role to resolve semantic heterogeneities within knowledge-based systems. However, ontologies contain a massive number of concepts, resulting in performance impediments during the ontology matching process. With the increasing number of ontology concepts, there is a growing need to focus more on largescale matching...

Bio-ontologies are characterized by large sizes, and there is a large number of smaller ontologies derived from them. Determining semantic correspondences across these smaller ones can be based on this “upper” ontology. To this end, we introduce a new fuzzy inference-based ontology matching approach exploiting upper ontologies as semantic bridges i...

A database system includes a set of different hardware and software resources with a large number of configuration parameters that affect and control the performance of database systems. Tuning these parameters within their diverse and complex environments requires a lot of expertise and it is a time-consuming, and often a misdirected process. Furt...

There is a proliferation of datasets generated by various scientists of different scientific disciplines. Therefore, there is a growing need to construct and develop platforms that enable scientists to capture, exchange, process, and interpret data for immediate use, as well as to store and manage data to support future reuse. Modeling and organizi...

The increasing size and the widespread use of XML data and different types of ontologies result in the big challenge of how to integrate these data. A critical step towards building this integration is to identify and discover semantically corresponding elements across heterogeneous data sets. This identification process becomes more and more chall...

Schema matching represents a critical step to integrate heterogeneous e-Business and shared-data applications. Most existing schema matching approaches rely heavily on similarity-based techniques, which attempt to discover correspondences based on various element similarity measures, each computed by an individual base matcher. It has been accepted...

Schema matching plays a central role in identifying the semantic correspondences across shared-data applications, such as data integration. Due to the increasing size and the widespread use of XML schemas and different kinds of ontologies, it becomes toughly challenging to cope with large-scale schema matching. Clustering-based matching is a great...

Database Management Systems (DBMSs) are the cores of most information systems. Database administrators (DBAs) face increasingly more challenges due to the systems growing complexity and must be proficient in areas, such as capacity planning, physical database design, DBMS tuning and DBMS management. Furthermore, DBAs need to implement policies for...

The numbers of web data sources grow significantly, and as a sequence, crucial data management issues should be addressed. Clustering is one of the issues that many researchers have focused on. Clustering has been proposed to improve the information availability. To this end, in this paper, we propose a feature-based clustering approach for cluster...

In the last few years we have observed a proliferation of approaches for clustering XML documents and schemas based on their structure and content. The presence of such a huge amount of approaches is due to the different applications requiring the clustering of XML data. These applications need data in the form of similar contents, tags, paths, str...

Schema matching plays a cenral role in identifying the semantic correspondences across shared-data applications, such as data integration etc. One of the great challenges in the field of XML schema matching is the definition, adoption, and utilization of element similarity measures. We classify these measures either as internal or external. The int...

Schema and ontology matching have attracted a great deal of interest among researchers. Despite the advances achieved, the large matching problem still presents a real challenge, such as it is a time-consuming and memory-intensive process. We therefore propose a scalable, clustering-based matching approach that breaks up the large matching problem...

Schema matching plays a central role in a myriad of XML-based applications. There has been a growing need for developing high-performance matching systems in order to identify and discover semantic correspondences across XML data. XML schema matching methods face several challenges in the form of definition, adoption, utilization, and combination o...

Due to the availability of huge number of Web services (WSs), finding an appropriate WS according to the requirement of a service consumer is still a challenge. In this paper, we present a new and flexible approach, called SeqDisc, that assesses the similarity between WSs. In particular, the approach exploits the Prüfer encoding method to represent...

The eXtensible Markup Language (XML) has emerged as a de facto standard to represent and exchange information among various applications on the Web and within organizations due to XML's inherent data self-describing capability and flexibility of organizing data. As a result, the number of available (heterogeneous) XML data is rapidly increasing, an...

In this paper, we classify, review, and experimentally compare major methods that are exploited in the definition, adoption, and utilization of element similarity measures in the context of XML schema matching. We aim at presenting a unified view which is useful when developing a new element similarity measure, when implementing an XML schema match...

The recent growing of the Semantic Web requires the need to cope with highly semantic heterogeneities among available ontologies. Ontology matching techniques aim to tackle this prob- lem by establishing correspondences between ontologies' elements. An intricate obstacle faces the ontology matching problem is its scal- ability against large number...

Schema matching is a critical step for discovering semantic correspondences among elements in many data-shared applications. Most of existing schema matching algorithms produce scores between schema elements resulting in discovering only simple matches. Such results partially solve the problem. Identifying and discovering complex matches is conside...

Locating desired Web services has become a challenging research problem due to the vast number of available Web services within an organization and on the Web. This necessitates the need for developing flexible, effective, and efficient Web service discovery frameworks. To this purpose, both the semantic description and the structure information of...

The relationship between XML data clustering and schema matching is bidirectional. On one side, clustering techniques have been adopted to improve matching performance, and on the other side schema matching is the backbone of the clustering technique. This paper presents a new approach for clustering XML schema based on schema matching. In particul...

The deep Web has many challenges to be solved. Among them is schema matching. In this paper, we build a conceptual connec- tion between the schema matching problem SMP and the fuzzy con- straint optimization problem FCOP. In particular, we propose the use of the fuzzy constraint optimization problem as a framework to model and formalize the schema...

Motivated by the fact that the most prominent feature in an XML schema is its hierarchical structure, we propose a new structure matching approach for XML schema matching. Our approach is based on the node context, which is reflected by its ancestor and its descendants including both the immediate children and the leaves of subtrees rooted at the e...

Schema matching plays a central role in many applications that require interoperability among heterogeneous data sources. A good evaluation for different capabilities of schema matching systems has become vital as the complexity of such systems arises. The capabilities of matching systems incorporate different (possibly conflicting) aspects among t...

The proliferation of applications dealing with shared data radically increases the need to iden- tify and discover the semantically corresponding elements. To cope with the di-culties of the necessary schema matching, we propose a unifled framework. The framework tries to collect the most well-known work concerning schema matching in a generalized...

Schema matching plays the central role in many applications that require interoperability between het-erogeneous data sources. The best way to attain comprehensive understanding of the schema matching problem is to construct a complete, if possible, problem formulation. Schema matching has been intensively researched and many matching systems have...