Unsupervised Sense-Aware Hypernymy Extraction

In this paper, we show how unsupervised sense representations can be used to improve hypernymy extraction. We present a method for extracting disambiguated hypernymy relationships that propagates hypernyms to sets of synonyms (synsets), constructs embeddings for these sets, and establishes sense-aware relationships between matching synsets. Evaluation on two gold standard datasets for English and Russian shows that the method successfully recognizes hypernymy relationships that cannot be found with standard Hearst patterns and Wiktionary datasets for the respective languages.Comment: In Proceedings of the 14th Conference on Natural Language Processing (KONVENS 2018). Vienna, Austri

Ontology Modeling 2.0: Next Steps

Semantic Web as a field of research and applications is concerned with methods and tools for data sharing, discovery, integration, and reuse, both on and off the World Wide Web. In the form of knowledge graphs and their underlying schemas, Semantic Web technologies are currently entering industrial mainstream. At the same time, the ever increasing prevalence of publicly available structured data on the Semantic Web enables new applications in a variety of domains, and as part of this presentation, we provide a conceptual approach that leverages such data in order to explain the input-output behavior of trained artificial neural networks. We apply existing Semantic Web technologies in order to provide an experimental proof of concept

KGrEaT: A Framework to Evaluate Knowledge Graphs via Downstream Tasks

In recent years, countless research papers have addressed the topics of knowledge graph creation, extension, or completion in order to create knowledge graphs that are larger, more correct, or more diverse. This research is typically motivated by the argumentation that using such enhanced knowledge graphs to solve downstream tasks will improve performance. Nonetheless, this is hardly ever evaluated. Instead, the predominant evaluation metrics - aiming at correctness and completeness - are undoubtedly valuable but fail to capture the complete picture, i.e., how useful the created or enhanced knowledge graph actually is. Further, the accessibility of such a knowledge graph is rarely considered (e.g., whether it contains expressive labels, descriptions, and sufficient context information to link textual mentions to the entities of the knowledge graph). To better judge how well knowledge graphs perform on actual tasks, we present KGrEaT - a framework to estimate the quality of knowledge graphs via actual downstream tasks like classification, clustering, or recommendation. Instead of comparing different methods of processing knowledge graphs with respect to a single task, the purpose of KGrEaT is to compare various knowledge graphs as such by evaluating them on a fixed task setup. The framework takes a knowledge graph as input, automatically maps it to the datasets to be evaluated on, and computes performance metrics for the defined tasks. It is built in a modular way to be easily extendable with additional tasks and datasets.Comment: Accepted for the Short Paper track of CIKM'23, October 21-25, 2023, Birmingham, United Kingdo

Biomedical ontology alignment: An approach based on representation learning

While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results

Improving Hypernymy Extraction with Distributional Semantic Classes

In this paper, we show how distributionally-induced semantic classes can be helpful for extracting hypernyms. We present methods for inducing sense-aware semantic classes using distributional semantics and using these induced semantic classes for filtering noisy hypernymy relations. Denoising of hypernyms is performed by labeling each semantic class with its hypernyms. On the one hand, this allows us to filter out wrong extractions using the global structure of distributionally similar senses. On the other hand, we infer missing hypernyms via label propagation to cluster terms. We conduct a large-scale crowdsourcing study showing that processing of automatically extracted hypernyms using our approach improves the quality of the hypernymy extraction in terms of both precision and recall. Furthermore, we show the utility of our method in the domain taxonomy induction task, achieving the state-of-the-art results on a SemEval'16 task on taxonomy induction.Comment: In Proceedings of the 11th Conference on Language Resources and Evaluation (LREC 2018). Miyazaki, Japa

Semantic Integration of MIR Datasets with the Polifonia Ontology Network

Integration between different data formats, and between data belonging to different collections, is an ongoing challenge in the MIR field. Semantic Web tools have proved to be promising resources for making different types of music information interoperable. However, the use of these technologies has so far been limited and scattered in the field. To address this, the Polifonia project is developing an ontological ecosystem that can cover a wide variety of musical aspects (musical features, instruments, emotions, performances). In this paper, we present the Polifonia Ontology Network, an ecosystem that enables and fosters the transition towards semantic MIR

“Eduinformatics”: A new education field promotion

Kobe Tokiwa University is currently performing university reform. To address many pressing and important problems of university reform, we had, over the past three years, determined research questions about these problems and resolved each of them when a problem presented itself, publishing our findings as reports or presenting in meetings. Before we began this research, we thought that there were no relationships between our studies. In this study, we reflect on our research during the last three years. Looking back, we discovered that we can classify our research into six groups by context analysis: group 1: university reform and collaboration between academic faculty and administrative staff, group 2: proposal of novel knowledge-creation models, group 3: novel visualization methods for education, group 4: information and communication technology for education, group 5: evidence-based education, group 6: first-year experience. More abstractly, our research can provide basis for a novel interdisciplinary concept, which we call “eduinformatics.” Based on this research, we propose changes to higher education that will result in increased quality of learning and teaching.| 神戸常盤大学は大学改革の真っ只中にある。われわれは過去3年間、本学における高等教育上の重要な課題を解決すべく、その都度リサーチクエスチョンを定めたうえでそれを解決し、その内容を論文や学会発表という形で公表してきた。今回、これらの一連の研究成果を俯瞰的に眺め、文脈解析を行った結果、われわれの研究が、①大学改革と教職協働、②新たな知の創造モデルの提案、③教育の新可視化法の開発、④ ICTを用いた教育方法の開発、⑤エビデンス・ベースドな教育の実践、⑥初年次教育、の6つの分野に分類できることを見出した。そしてこの6つの分野が、bioinformaticsという既存の概念に倣い、教育研究といういわゆる文系色の濃い分野に、いわゆる理系色を融合させた学際的分野として“eduinformatics”という新たな概念を提唱することで1つに括れることを発見した。この新たな概念を高等教育研究に敷衍することで、わが国の高等教育研究が飛躍的に発展していくことが期待できる