1,393 research outputs found

    The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

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    Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch\u27s APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch\u27s data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch\u27s analytic tools by developing a customized plugin for OpenAI\u27s ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app

    Semantic models and knowledge graphs as manufacturing system reconfiguration enablers

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    Reconfigurable Manufacturing System (RMS) provides a cost-effective approach for manufacturers to adapt to fluctuating market demands by reconfiguring assets through automated analysis of asset utilization and resource allocation. Achieving this automation necessitates a clear understanding, formalization, and documentation of asset capabilities and capacity utilization. This paper introduces a unified model employing semantic modeling to delineate the manufacturing sector's capabilities, capacity, and reconfiguration potential. The model illustrates the integration of these three components to facilitate efficient system reconfiguration. Additionally, semantic modeling allows for the capture of historical experiences, thus enhancing long-term system reconfiguration through a knowledge graph. Two use cases are presented: capability matching and reconfiguration solution recommendation based on the proposed model. A thorough explication of the methodology and outcomes is provided, underscoring the advantages of this approach in terms of heightened efficiency, diminished costs, and augmented productivity

    Current and Future Challenges in Knowledge Representation and Reasoning

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    Knowledge Representation and Reasoning is a central, longstanding, and active area of Artificial Intelligence. Over the years it has evolved significantly; more recently it has been challenged and complemented by research in areas such as machine learning and reasoning under uncertainty. In July 2022 a Dagstuhl Perspectives workshop was held on Knowledge Representation and Reasoning. The goal of the workshop was to describe the state of the art in the field, including its relation with other areas, its shortcomings and strengths, together with recommendations for future progress. We developed this manifesto based on the presentations, panels, working groups, and discussions that took place at the Dagstuhl Workshop. It is a declaration of our views on Knowledge Representation: its origins, goals, milestones, and current foci; its relation to other disciplines, especially to Artificial Intelligence; and on its challenges, along with key priorities for the next decade

    A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases

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    In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance

    Towards Semantically Enriched Embeddings for Knowledge Graph Completion

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    Embedding based Knowledge Graph (KG) Completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. In a separate development, a vast amount of information has been captured within the Large Language Models (LLMs) which has revolutionized the field of Artificial Intelligence. KGs could benefit from these LLMs and vice versa. This vision paper discusses the existing algorithms for KG completion based on the variations for generating KG embeddings. It starts with discussing various KG completion algorithms such as transductive and inductive link prediction and entity type prediction algorithms. It then moves on to the algorithms utilizing type information within the KGs, LLMs, and finally to algorithms capturing the semantics represented in different description logic axioms. We conclude the paper with a critical reflection on the current state of work in the community and give recommendations for future directions

    Predicate Matrix: an interoperable lexical knowledge base for predicates

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    183 p.La Matriz de Predicados (Predicate Matrix en inglés) es un nuevo recurso léxico-semántico resultado de la integración de múltiples fuentes de conocimiento, entre las cuales se encuentran FrameNet, VerbNet, PropBank y WordNet. La Matriz de Predicados proporciona un léxico extenso y robusto que permite mejorar la interoperabilidad entre los recursos semánticos mencionados anteriormente. La creación de la Matriz de Predicados se basa en la integración de Semlink y nuevos mappings obtenidos utilizando métodos automáticos que enlazan el conocimiento semántico a nivel léxico y de roles. Asimismo, hemos ampliado la Predicate Matrix para cubrir los predicados nominales (inglés, español) y predicados en otros idiomas (castellano, catalán y vasco). Como resultado, la Matriz de predicados proporciona un léxico multilingüe que permite el análisis semántico interoperable en múltiples idiomas

    On the Significance of Distance in Machine Learning

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    Avstandsbegrepet er grunnleggende i maskinlæring. Hvordan vi velger å måle avstand har betydning, men det er ofte utfordrende å finne et passende avstandsmål. Metrisk læring kan brukes til å lære funksjoner som implementerer avstand eller avstandslignende mål. Vanlige dyplæringsmodeller er sårbare for modifikasjoner av input som har til hensikt å lure modellen (adversarial examples, motstridende eksempler). Konstruksjon av modeller som er robuste mot denne typen angrep er av stor betydning for å kunne utnytte maskinlæringsmodeller i større skala, og et passende avstandsmål kan brukes til å studere slik motstandsdyktighet. Ofte eksisterer det hierarkiske relasjoner blant klasser, og disse relasjonene kan da representeres av den hierarkiske avstanden til klasser. I klassifiseringsproblemer som må ta i betraktning disse klasserelasjonene, kan hierarkiinformert klassifisering brukes. Jeg har utviklet en metode kalt /distance-ratio/-basert (DR) metrisk læring. I motsetning til den formuleringen som normalt anvendes har DR-formuleringen to gunstige egenskaper. For det første er det skala-invariant med hensyn til rommet det projiseres til. For det andre har optimale klassekonfidensverdier på klasserepresentantene. Dersom rommet for å konstruere modifikasjoner er tilstrekklig stort, vil man med standard adversarial accuracy (SAA, standard motstridende nøyaktighet) risikere at naturlige datapunkter blir betraktet som motstridende eksempler. Dette kan være en årsak til SAA ofte går på bekostning av nøyaktighet. For å løse dette problemet har jeg utviklet en ny definisjon på motstridende nøyaktighet kalt Voronoi-epsilon adversarial accuracy (VAA, Voronoi-epsilon motstridende nøyaktighet). VAA utvider studiet av lokal robusthet til global robusthet. Klassehierarkisk informasjon er ikke tilgjengelig for alle datasett. For å håndtere denne utfordringen har jeg undersøkt om klassifikasjonsbaserte metriske læringsmodeller kan brukes til å utlede klassehierarkiet. Videre har jeg undersøkt de mulige effektene av robusthet på feature space (egenskapsrom). Jeg fant da at avstandsstrukturen til et egenskapsrom trent for robusthet har større likhet med avstandsstrukturen i rådata enn et egenskapsrom trent uten robusthet.The notion of distance is fundamental in machine learning. The choice of distance matters, but it is often challenging to find an appropriate distance. Metric learning can be used for learning distance(-like) functions. Common deep learning models are vulnerable to the adversarial modification of inputs. Devising adversarially robust models is of immense importance for the wide deployment of machine learning models, and distance can be used for the study of adversarial robustness. Often, hierarchical relationships exist among classes, and these relationships can be represented by the hierarchical distance of classes. For classification problems that must take these class relationships into account, hierarchy-informed classification can be used. I propose distance-ratio-based (DR) formulation for metric learning. In contrast to the commonly used formulation, DR formulation has two favorable properties. First, it is invariant of the scale of an embedding. Secondly, it has optimal class confidence values on class representatives. For a large perturbation budget, standard adversarial accuracy (SAA) allows natural data points to be considered as adversarial examples. This could be a reason for the tradeoff between accuracy and SAA. To resolve the issue, I proposed a new definition of adversarial accuracy named Voronoi-epsilon adversarial accuracy (VAA). VAA extends the study of local robustness to global robustness. Class hierarchical information is not available for all datasets. To handle this challenge, I investigated whether classification-based metric learning models can be used to infer class hierarchy. Furthermore, I explored the possible effects of adversarial robustness on feature space. I found that the distance structure of robustly trained feature space resembles that of input space to a greater extent than does standard trained feature space.Doktorgradsavhandlin

    Automatic Generation of Personalized Recommendations in eCoaching

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    Denne avhandlingen omhandler eCoaching for personlig livsstilsstøtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er å designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede løsningen er fokusert på forbedring av fysisk aktivitet. Prototypen bruker bærbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for å utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen på teknologisk verifisering snarere enn klinisk evaluering.publishedVersio

    Workshop Proceedings of the 12th edition of the KONVENS conference

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    The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut für Informationswissenschaft und Sprachtechnologie of Universität Hildesheim: it has long been one of the institute’s research topics, and it has received even more attention over the last few years
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