Dividing the Ontology Alignment Task with Semantic Embeddings and Logic-based Modules

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In this paper we present an approach that combines a neural embedding model and logic-based modules to accurately divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed method is adequate in practice and can be integrated within the workflow of systems unable to cope with very large ontologies

Breaking-down the Ontology Alignment Task with a Lexical Index and Neural Embeddings

Large ontologies still pose serious challenges to state-of-the-art ontology alignment systems. In the paper we present an approach that combines a lexical index, a neural embedding model and locality modules to effectively divide an input ontology matching task into smaller and more tractable matching (sub)tasks. We have conducted a comprehensive evaluation using the datasets of the Ontology Alignment Evaluation Initiative. The results are encouraging and suggest that the proposed methods are adequate in practice and can be integrated within the workflow of state-of-the-art systems

Unveiling Relations in the Industry 4.0 Standards Landscape based on Knowledge Graph Embeddings

Industry~4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of \emph{empowering interoperability} in smart factories. These standards enable the description and interaction of the main components, systems, and processes inside of a smart factory. Due to the growing number of frameworks and standards, there is an increasing need for approaches that automatically analyze the landscape of I4.0 standards. Standardization frameworks classify standards according to their functions into layers and dimensions. However, similar standards can be classified differently across the frameworks, producing, thus, interoperability conflicts among them. Semantic-based approaches that rely on ontologies and knowledge graphs, have been proposed to represent standards, known relations among them, as well as their classification according to existing frameworks. Albeit informative, the structured modeling of the I4.0 landscape only provides the foundations for detecting interoperability issues. Thus, graph-based analytical methods able to exploit knowledge encoded by these approaches, are required to uncover alignments among standards. We study the relatedness among standards and frameworks based on community analysis to discover knowledge that helps to cope with interoperability conflicts between standards. We use knowledge graph embeddings to automatically create these communities exploiting the meaning of the existing relationships. In particular, we focus on the identification of similar standards, i.e., communities of standards, and analyze their properties to detect unknown relations. We empirically evaluate our approach on a knowledge graph of I4.0 standards using the Trans$^*$ family of embedding models for knowledge graph entities. Our results are promising and suggest that relations among standards can be detected accurately.Comment: 15 pages, 7 figures, DEXA2020 Conferenc

LogMap family participation in the OAEI 2020

We present the participation of LogMap and its variants in the OAEI 2020 campaign. The LogMap project started in January 2011 with the objective of developing a scalable and logic-based ontology matching system. This is the ninth participation in the OAEI and the experience has so far been very positive. LogMap is one of the few systems that participates in (almost) all OAEI tracks

Knowledge Extraction from Textual Resources through Semantic Web Tools and Advanced Machine Learning Algorithms for Applications in Various Domains

Nowadays there is a tremendous amount of unstructured data, often represented by texts, which is created and stored in variety of forms in many domains such as patients' health records, social networks comments, scientific publications, and so on. This volume of data represents an invaluable source of knowledge, but unfortunately it is challenging its mining for machines. At the same time, novel tools as well as advanced methodologies have been introduced in several domains, improving the efficacy and the efficiency of data-based services. Following this trend, this thesis shows how to parse data from text with Semantic Web based tools, feed data into Machine Learning methodologies, and produce services or resources to facilitate the execution of some tasks. More precisely, the use of Semantic Web technologies powered by Machine Learning algorithms has been investigated in the Healthcare and E-Learning domains through not yet experimented methodologies. Furthermore, this thesis investigates the use of some state-of-the-art tools to move data from texts to graphs for representing the knowledge contained in scientific literature. Finally, the use of a Semantic Web ontology and novel heuristics to detect insights from biological data in form of graph are presented. The thesis contributes to the scientific literature in terms of results and resources. Most of the material presented in this thesis derives from research papers published in international journals or conference proceedings

Knowledge graph embedding enhancement using ontological knowledge in the biomedical domain

The biomedical field is a critical area for natural language processing (NLP) applications because it involves a vast amount of unstructured data, including clinical notes, medical publications, and electronic health records. NLP techniques can help extract valuable information from these documents, such as disease symptoms, medication usage, and treatment outcomes, which can improve patient care and clinical decision-making. MAPS S.p.A. currently produces Clinika, a software that extracts knowledge from clinical corpora. Clinika performs the task of Named Entity Recognition (NER) by linking entities to medical concepts from an established knowledge base, in this case, the Unified Medical Language System (UMLS). This dissertation details how we approached designing and implementing a component for the new version of Clinika, specifically a mention embedder that uses embeddings to perform entity linking with UMLS concepts. We focused on enhancing existing dense contextual embeddings by injecting ontological knowledge, using two parallel approaches: (1) taking the embeddings as a by-product of an entity alignment model aided by an ontology, and (2) fine-tuning a contextual language model with custom sampling strategies. We evaluated both approaches with suitable experiments from the relevant literature. After testing, we discontinued the first approach but found more significant results using the second. The results on the tasks chosen to evaluate the embeddings were not promising, we address the causes in the Error Analysis section, and discuss further work on this topic

Knowledge Graph Alignment as a Service

Researchers and practitioners in the field of knowledge graphs rely on a variety of tools to perform knowledge graph alignment. In order to broaden the domain of a knowledge scientist, the process of aligning knowledge graphs must be accessible to a broader range of users, and in a way that can scale to meet the needs of a growing userbase. This paper presents a cloud-based, language agnostic approach that aims at providing knowledge graph alignment tooling at scale