An unsupervised approach to disjointness learning based on terminological cluster trees

In the context of the Semantic Web regarded as a Web of Data, research efforts have been devoted to improving the quality of the ontologies that are used as vocabularies to enable complex services based on automated reasoning. From various surveys it emerges that many domains would require better ontologies that include non-negligible constraints for properly conveying the intended semantics. In this respect, disjointness axioms are representative of this general problem: these axioms are essential for making the negative knowledge about the domain of interest explicit yet they are often overlooked during the modeling process (thus affecting the efficacy of the reasoning services). To tackle this problem, automated methods for discovering these axioms can be used as a tool for supporting knowledge engineers in modeling new ontologies or evolving existing ones. The current solutions, either based on statistical correlations or relying on external corpora, often do not fully exploit the terminology. Stemming from this consideration, we have been investigating on alternative methods to elicit disjointness axioms from existing ontologies based on the induction of terminological cluster trees, which are logic trees in which each node stands for a cluster of individuals which emerges as a sub-concept. The growth of such trees relies on a divide-and-conquer procedure that assigns, for the cluster representing the root node, one of the concept descriptions generated via a refinement operator and selected according to a heuristic based on the minimization of the risk of overlap between the candidate sub-clusters (quantified in terms of the distance between two prototypical individuals). Preliminary works have showed some shortcomings that are tackled in this paper. To tackle the task of disjointness axioms discovery we have extended the terminological cluster tree induction framework with various contributions: 1) the adoption of different distance measures for clustering the individuals of a knowledge base; 2) the adoption of different heuristics for selecting the most promising concept descriptions; 3) a modified version of the refinement operator to prevent the introduction of inconsistency during the elicitation of the new axioms. A wide empirical evaluation showed the feasibility of the proposed extensions and the improvement with respect to alternative approaches

Using distributional similarity to organise biomedical terminology

We investigate an application of distributional similarity techniques to the problem of structural organisation of biomedical terminology. Our application domain is the relatively small GENIA corpus. Using terms that have been accurately marked-up by hand within the corpus, we consider the problem of automatically determining semantic proximity. Terminological units are dened for our purposes as normalised classes of individual terms. Syntactic analysis of the corpus data is carried out using the Pro3Gres parser and provides the data required to calculate distributional similarity using a variety of dierent measures. Evaluation is performed against a hand-crafted gold standard for this domain in the form of the GENIA ontology. We show that distributional similarity can be used to predict semantic type with a good degree of accuracy

Improving the translation environment for professional translators

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project

Axiomatization of General Concept Inclusions from Finite Interpretations

Description logic knowledge bases can be used to represent knowledge about a particular domain in a formal and unambiguous manner. Their practical relevance has been shown in many research areas, especially in biology and the semantic web. However, the tasks of constructing knowledge bases itself, often performed by human experts, is difficult, time-consuming and expensive. In particular the synthesis of terminological knowledge is a challenge every expert has to face. Because human experts cannot be omitted completely from the construction of knowledge bases, it would therefore be desirable to at least get some support from machines during this process. To this end, we shall investigate in this work an approach which shall allow us to extract terminological knowledge in the form of general concept inclusions from factual data, where the data is given in the form of vertex and edge labeled graphs. As such graphs appear naturally within the scope of the Semantic Web in the form of sets of RDF triples, the presented approach opens up the possibility to extract terminological knowledge from the Linked Open Data Cloud. We shall also present first experimental results showing that our approach has the potential to be useful for practical applications

Cluster algebras II: Finite type classification

This paper continues the study of cluster algebras initiated in math.RT/0104151. Its main result is the complete classification of the cluster algebras of finite type, i.e., those with finitely many clusters. This classification turns out to be identical to the Cartan-Killing classification of semisimple Lie algebras and finite root systems, which is intriguing since in most cases, the symmetry exhibited by the Cartan-Killing type of a cluster algebra is not at all apparent from its geometric origin. The combinatorial structure behind a cluster algebra of finite type is captured by its cluster complex. We identify this complex as the normal fan of a generalized associahedron introduced and studied in hep-th/0111053 and math.CO/0202004. Another essential combinatorial ingredient of our arguments is a new characterization of the Dynkin diagrams.Comment: 50 pages, 18 figures. Version 2: new introduction; final version, to appear in Invent. Mat

Integrating Concepts of Artificial Intelligence in the EO4GEO Body of Knowledge

Ponència del XXIV ISPRS Congress (2022 edition), 6–11 June 2022, Nice, FranceThe EO4GEO Body of Knowledge (BoK) forms a structure of concepts and relationships between them, describing the domain of Earth Observation and Geo-Information (EO/GI). Each concept carries a short description, a list of key literature references and a set of associated skills which are used for job profiling and curriculum building. As the EO/GI domain is evolving continuously, the BoK needs regular updates with new concepts embodying new trends, and deprecating concepts which are not relevant anymore. This paper presents the inclusion of BoK concepts related to Artificial Intelligence. This broad field of knowledge has links to several applications in EO/GI. Its connection to concepts, already existing in the BoK, needs special attention. To perform a clean and structural integration of the cross-cutting domain of AI, first a separate cluster of AI concepts was created, which was then merged with the existing BoK. The paper provides examples of this integration with specific concepts and examples of training resources in which AI-related concepts are used. Although the presented structure already provides a good starting point, the positioning of AI within the EO/GI-focussed BoK needs to be further enhanced with the help of expert calls as part of the BoK update cycle

Combining machine learning and semantic web: A systematic mapping study

In line with the general trend in artificial intelligence research to create intelligent systems that combine learning and symbolic components, a new sub-area has emerged that focuses on combining Machine Learning components with techniques developed by the Semantic Web community - Semantic Web Machine Learning (SWeML). Due to its rapid growth and impact on several communities in thepast two decades, there is a need to better understand the space of these SWeML Systems, their characteristics, and trends. Yet, surveys that adopt principled and unbiased approaches are missing. To fill this gap, we performed a systematic study and analyzed nearly 500 papers published in the past decade in this area, where we focused on evaluating architectural and application-specific features. Our analysis identified a rapidly growing interest in SWeML Systems, with a high impact on several application domains and tasks. Catalysts for this rapid growth are the increased application of deep learning and knowledge graph technologies. By leveraging the in-depth understanding of this area acquired through this study, a further key contribution of this article is a classification system for SWeML Systems that we publish as ontology.</p

Developmental biology of wood formation

The wood-forming vascular cambium is responsible for the production of a large part of the biomass on this planet. Yet, there is only limited knowledge on how cell proliferation and differentiation in the cambial meristem are regulated. In this thesis the wood-forming tissues of aspen were used as a model system to identify and characterize molecular factors related to cambial meristem activity. An important regulator of cambial meristem activity is the plant hormone auxin. As polar transport is crucial for the delivery of auxin to the cambial zone, we identified homologues of known regulators of polar auxin transport and described their regulation by environmental and developmental factors. Translating changes in auxin concentration into changes in gene expression involves members of the Aux/IAA gene family. Aspen homologues of Aux/IAA genes were cloned and found to be expressed in a highly tissue-specific fashion, which is further influenced by developmental events and changes in the environment. A major response of trees to environmental changes is the suspension of meristematic growth during winter dormancy. A comparison of gene expression in active and dormant cambia revealed dramatic changes in the transcriptome including the expression of many cold and stress related genes during winter. During the process of wood formation, cells originating in the vascular cambium go through an elaborate process of cell division, cell expansion, secondary wall formation and programmed cell death. Large-scale analysis of gene expression was used to create transcriptional maps of the differentiation process. This extensive dataset allowed us to confirm the proposed functions of various genes involved in wood formation, assign other known genes to specific stages along the developmental gradient and identify a large number of novel potential regulators of wood formation. The data further suggest that the cambial meristem shares regulatory mechanisms with other meristems in addition to its own, specific factors