227 research outputs found

    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

    Methodological approaches and techniques for designing ontologies in information systems requirements engineering

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    Programa doutoral em Information Systems and TechnologyThe way we interact with the world around us is changing as new challenges arise, embracing innovative business models, rethinking the organization and processes to maximize results, and evolving change management. Currently, and considering the projects executed, the methodologies used do not fully respond to the companies' needs. On the one hand, organizations are not familiar with the languages used in Information Systems, and on the other hand, they are often unable to validate requirements or business models. These are some of the difficulties encountered that lead us to think about formulating a new approach. Thus, the state of the art presented in this paper includes a study of the models involved in the software development process, where traditional methods and the rivalry of agile methods are present. In addition, a survey is made about Ontologies and what methods exist to conceive, transform, and represent them. Thus, after analyzing some of the various possibilities currently available, we began the process of evolving a method and developing an approach that would allow us to design ontologies. The method we evolved and adapted will allow us to derive terminologies from a specific domain, aggregating them in order to facilitate the construction of a catalog of terminologies. Next, the definition of an approach to designing ontologies will allow the construction of a domain-specific ontology. This approach allows in the first instance to integrate and store the data from different information systems of a given organization. In a second instance, the rules for mapping and building the ontology database are defined. Finally, a technological architecture is also proposed that will allow the mapping of an ontology through the construction of complex networks, allowing mapping and relating terminologies. This doctoral work encompasses numerous Research & Development (R&D) projects belonging to different domains such as Software Industry, Textile Industry, Robotic Industry and Smart Cities. Finally, a critical and descriptive analysis of the work done is performed, and we also point out perspectives for possible future work.A forma como interagimos com o mundo à nossa volta está a mudar à medida que novos desafios surgem, abraçando modelos empresariais inovadores, repensando a organização e os processos para maximizar os resultados, e evoluindo a gestão da mudança. Atualmente, e considerando os projetos executados, as metodologias utilizadas não respondem na totalidade às necessidades das empresas. Por um lado, as organizações não estão familiarizadas com as linguagens utilizadas nos Sistemas de Informação, por outro lado, são muitas vezes incapazes de validar requisitos ou modelos de negócio. Estas são algumas das dificuldades encontradas que nos levam a pensar na formulação de uma nova abordagem. Assim, o estado da arte apresentado neste documento inclui um estudo dos modelos envolvidos no processo de desenvolvimento de software, onde os métodos tradicionais e a rivalidade de métodos ágeis estão presentes. Além disso, é efetuado um levantamento sobre Ontologias e quais os métodos existentes para as conceber, transformar e representar. Assim, e após analisarmos algumas das várias possibilidades atualmente disponíveis, iniciou-se o processo de evolução de um método e desenvolvimento de uma abordagem que nos permitisse conceber ontologias. O método que evoluímos e adaptamos permitirá derivar terminologias de um domínio específico, agregando-as de forma a facilitar a construção de um catálogo de terminologias. Em seguida, a definição de uma abordagem para conceber ontologias permitirá a construção de uma ontologia de um domínio específico. Esta abordagem permite em primeira instância, integrar e armazenar os dados de diferentes sistemas de informação de uma determinada organização. Num segundo momento, são definidas as regras para o mapeamento e construção da base de dados ontológica. Finalmente, é também proposta uma arquitetura tecnológica que permitirá efetuar o mapeamento de uma ontologia através da construção de redes complexas, permitindo mapear e relacionar terminologias. Este trabalho de doutoramento engloba inúmeros projetos de Investigação & Desenvolvimento (I&D) pertencentes a diferentes domínios como por exemplo Indústria de Software, Indústria Têxtil, Indústria Robótica e Smart Cities. Finalmente, é realizada uma análise critica e descritiva do trabalho realizado, sendo que apontamos ainda perspetivas de possíveis trabalhos futuros

    Semiring Provenance for Lightweight Description Logics

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    We investigate semiring provenance--a successful framework originally defined in the relational database setting--for description logics. In this context, the ontology axioms are annotated with elements of a commutative semiring and these annotations are propagated to the ontology consequences in a way that reflects how they are derived. We define a provenance semantics for a language that encompasses several lightweight description logics and show its relationships with semantics that have been defined for ontologies annotated with a specific kind of annotation (such as fuzzy degrees). We show that under some restrictions on the semiring, the semantics satisfies desirable properties (such as extending the semiring provenance defined for databases). We then focus on the well-known why-provenance, which allows to compute the semiring provenance for every additively and multiplicatively idempotent commutative semiring, and for which we study the complexity of problems related to the provenance of an axiom or a conjunctive query answer. Finally, we consider two more restricted cases which correspond to the so-called positive Boolean provenance and lineage in the database setting. For these cases, we exhibit relationships with well-known notions related to explanations in description logics and complete our complexity analysis. As a side contribution, we provide conditions on an ELHI_bot ontology that guarantee tractable reasoning.Comment: Paper currently under review. 102 page

    Enriching open-world knowledge graphs with expressive negative statements

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    Machine knowledge about entities and their relationships has been a long-standing goal for AI researchers. Over the last 15 years, thousands of public knowledge graphs have been automatically constructed from various web sources. They are crucial for use cases such as search engines. Yet, existing web-scale knowledge graphs focus on collecting positive statements, and store very little to no negatives. Due to their incompleteness, the truth of absent information remains unknown, which compromises the usability of the knowledge graph. In this dissertation: First, I make the case for selective materialization of salient negative statements in open-world knowledge graphs. Second, I present our methods to automatically infer them from encyclopedic and commonsense knowledge graphs, by locally inferring closed-world topics from reference comparable entities. I then discuss our evaluation fin-dings on metrics such as correctness and salience. Finally, I conclude with open challenges and future opportunities.Machine knowledge about entities and their relationships has been a long-standing goal for AI researchers. Over the last 15 years, thousands of public knowledge graphs have been automatically constructed from various web sources. They are crucial for use cases such as search engines. Yet, existing web-scale knowledge graphs focus on collecting positive statements, and store very little to no negatives. Due to their incompleteness, the truth of absent information remains unknown, which compromises the usability of the knowledge graph. In this dissertation: First, I make the case for selective materialization of salient negative statements in open-world knowledge graphs. Second, I present our methods to automatically infer them from encyclopedic and commonsense knowledge graphs, by locally inferring closed-world topics from reference comparable entities. I then discuss our evaluation fin-dings on metrics such as correctness and salience. Finally, I conclude with open challenges and future opportunities.Wissensgraphen über Entitäten und ihre Attribute sind eine wichtige Komponente vieler KI-Anwendungen. Wissensgraphen im Webmaßstab speichern fast nur positive Aussagen und übersehen negative Aussagen. Aufgrund der Unvollständigkeit von Open-World-Wissensgraphen werden fehlende Aussagen als unbekannt und nicht als falsch betrachtet. Diese Dissertation plädiert dafür, Wissensgraphen mit informativen Aussagen anzureichern, die nicht gelten, und so ihren Mehrwert für Anwendungen wie die Beantwortung von Fragen und die Zusammenfassung von Entitäten zu verbessern. Mit potenziell Milliarden negativer Aussagen von Kandidaten bewältigen wir vier Hauptherausforderungen. 1. Korrektheit (oder Plausibilität) negativer Aussagen: Unter der Open-World-Annahme (OWA) reicht es nicht aus, zu prüfen, ob ein negativer Kandidat im Wissensgraphen nicht explizit als positiv angegeben ist, da es sich möglicherweise um eine fehlende Aussage handeln kann. Von entscheidender Bedeutung sind Methoden zur Prüfung großer Kandidatengruppen, und zur Beseitigung falsch positiver Ergebnisse. 2. Bedeutung negativer Aussagen: Die Menge korrekter negativer Aussagen ist sehr groß, aber voller trivialer oder unsinniger Aussagen, z. B. “Eine Katze kann keine Daten speichern.”. Es sind Methoden zur Quantifizierung der Aussagekraft von Negativen erforderlich. 3. Abdeckung der Themen: Abhängig von der Datenquelle und den Methoden zum Abrufen von Kandidaten erhalten einige Themen oder Entitäten in demWissensgraphen möglicherweise keine negativen Kandidaten. Methoden müssen die Fähigkeit gewährleisten, Negative über fast jede bestehende Entität zu entdecken. 4. Komplexe negative Aussagen: In manchen Fällen erfordert das Ausdrücken einer Negation mehr als ein Wissensgraphen-Tripel. Beispielsweise ist “Einstein hat keine Ausbildung erhalten” eine inkorrekte Negation, aber “Einstein hat keine Ausbildung an einer US-amerikanischen Universität erhalten” ist korrekt. Es werden Methoden zur Erzeugung komplexer Negationen benötigt. Diese Dissertation geht diese Herausforderungen wie folgt an. 1. Wir plädieren zunächst für die selektive Materialisierung negativer Aussagen über Entitäten in enzyklopädischen (gut kanonisierten) Open-World-Wissensgraphen, und definieren formal drei Arten negativer Aussagen: fundiert, universell abwesend und konditionierte negative Aussagen. Wir stellen die Peer-basierte Negationsinferenz-Methode vor, um Listen hervorstechender Negationen über Entitäten zu erstellen. Die Methode berechnet relevante Peers für eine bestimmte Eingabeentität und verwendet ihre positiven Eigenschaften, um Erwartungen für die Eingabeentität festzulegen. Eine Erwartung, die nicht erfüllt ist, ist ein unmittelbar negativer Kandidat und wird dann anhand von Häufigkeits-, Wichtigkeits- und Unerwartetheitsmetriken bewertet. 2. Wir schlagen die Methode musterbasierte Abfrageprotokollextraktion vor, um hervorstechende Negationen aus umfangreichen Textquellen zu extrahieren. Diese Methode extrahiert hervorstechende Negationen über eine Entität, indem sie große Korpora, z.B., die Anfrageprotokolle von Suchmaschinen, unter Verwendung einiger handgefertigter Muster mit negativen Schlüsselwörtern sammelt. 3. Wir führen die UnCommonsense-Methode ein, um hervorstechende negative Phrasen über alltägliche Konzepte in weniger kanonisierten commonsense-KGs zu generieren. Diese Methode ist für die Negationsinferenz, Prüfung und Einstufung kurzer Phrasen in natürlicher Sprache konzipiert. Sie berechnet vergleichbare Konzepte für ein bestimmtes Zielkonzept, leitet aus dem Vergleich ihrer positiven Kandidaten Negationen ab, und prüft diese Kandidaten im Vergleich zum Wissensgraphen selbst, sowie mit Sprachmodellen (LMs) als externer Wissensquelle. Schließlich werden die Kandidaten mithilfe semantischer Ähnlichkeitserkennungshäufigkeitsmaßen eingestuft. 4. Um die Exploration unserer Methoden und ihrer Ergebnisse zu erleichtern, implementieren wir zwei Prototypensysteme. In Wikinegata wird ein System zur Präsentation der Peer-basierten Methode entwickelt, mit dem Benutzer negative Aussagen über 500K Entitäten aus 11 Klassen untersuchen und verschiedene Parameter der Peer-basierten Inferenzmethode anpassen können. Sie können den Wissensgraphen auch mithilfe einer Suchmaske mit negierten Prädikaten befragen. Im UnCommonsense-System können Benutzer genau prüfen, was die Methode bei jedem Schritt hervorbringt, sowie Negationen zu 8K alltäglichen Konzepten durchsuchen. Darüber hinaus erstellen wir mithilfe der Peer-basierten Negationsinferenzmethode den ersten groß angelegten Datensatz zu Demografie und Ausreißern in Interessengemeinschaften und zeigen dessen Nützlichkeit in Anwendungsfällen wie der Identifizierung unterrepräsentierter Gruppen. 5. Wir veröffentlichen alle in diesen Projekten erstellten Datensätze und Quellcodes unter https://www.mpi-inf.mpg.de/negation-in-kbs und https://www.mpi-inf.mpg.de/Uncommonsense

    D7.5 FIRST consolidated project results

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    The FIRST project commenced in January 2017 and concluded in December 2022, including a 24-month suspension period due to the COVID-19 pandemic. Throughout the project, we successfully delivered seven technical reports, conducted three workshops on Key Enabling Technologies for Digital Factories in conjunction with CAiSE (in 2019, 2020, and 2022), produced a number of PhD theses, and published over 56 papers (and numbers of summitted journal papers). The purpose of this deliverable is to provide an updated account of the findings from our previous deliverables and publications. It involves compiling the original deliverables with necessary revisions to accurately reflect the final scientific outcomes of the project

    A Design Science Research Approach to Architecting and Developing Information Systems for Collaborative Manufacturing : A Case for Human-Robot Collaboration

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    Konseptointi- ja suunnitteluvaiheessa sekä valmistuksen, käytön ja kehitysprosessin aikana syntyy tietoa, jonka hyödyntämisessä on valtavaa potentiaalia liike-elämän ja tuotantoprosessien muuttamiseen. Neljännen teollisen vallankumouksen ytimessä oleva digitaalinen muutos tunnistaa tämän painottaen erityisesti tämän tiedon yhdistämistä toimintojen ja järjestelmien tukemiseksi läpi tuotteen elinkaareen, mitä kutsutaan digitaaliseksi säikeen kehykseksi (digital thread framework). Tämän väitöskirjan tavoitteena on kehittää ja käyttää yhtä tällaista viitekehystä ihmisen ja robotin yhteistoiminnan asiayhteydessä. Tämä kehys pyrkii vastaamaan merkittävään ongelmaan, joka liittyy mukautuvuuden ja joustavuuden abstrakteihin ominaisuuksiin. Nykyiset ihmisen ja robotin yhteistyöjärjestelmät (human-robot collaboration (HRC)) on rakennettu pääasiassa pysyviksi järjestelmiksi, jotka sivuuttavat ihmisten intuitiivisen toiminnan asettamalla heidän roolinsa yhteistyötehtävissä etukäteen määritellyiksi. Lisäksi järjestelmien kyky vaihtaa tuotteesta toiseen on rajoittunutta. Tämä on erityisen ongelmallista nykyisellä laajan tuotevalikoiman aikakaudella, joka johtuu asiakkaiden räätälöidyistä vaatimuksista. Tähän taustaan vastaten, tämä väitöskirja käyttää design science research methodology -menetelmää suunnitellakseen, kehittääkseen ja ottaakseen käyttöön kolme pääasiallista artefaktia ihmisen ja robotin yhteistyösolussa laboratorioympäristössä. Ensimmäinen on digitaalisen säikeen kehys (digital thread framework), joka integroi tuotesuunnitteluympäristön toimijaksi monitoimijajärjestelmään käyttäen uusimpia tietoon perustuvia suunnittelujärjestelmiä, mikä tarjoaa prosessin toimijoille pääsyn tuotesuunnittelumalleihin reaaliajassa. Toinen on lisätyn todellisuuden malli, joka tarjoaa rajapinnan kokoonpanotehtävässä yhteistyöhön osallistuvan ihmisoperaattorin ja edellä mainitun kehyksen välille. Kolmas on tukitietomalli, jota yhteistyötä tekevät toimijat käyttävät tietopohjanaan täyttääkseen yhteistyössä tapahtuvan kokoonpanon tavoitteet mukautuvasti. Näitä kehitettyjä artefakteja käytettiin kokonaisuutena tapaustutkimuksissa, jotka liittyivät aidon dieselmoottorin kokoonpanoon, ja joissa todennettiin niiden hyödyllisyys ja että ne lisäävät joustavuutta, jota varten kehys (framework) suunniteltiin. Rajauslaatikoiden näyttäminen skaalautuvana informaationa, joka hahmottaa alikokoonpanon osien geometriaa, demostroi kehitettyjen artefaktien käytettävyyttä yhteistyötä tekevien toimijoiden aikomuksia heijastavien laajennetun todellisuuden projektioiden tuottamiseksi. Yhteenvetona tämän väitöskirjan tuloksena syntyi lähestymistapa älykkään ja mukautuvan robotiikan toteuttamiseksi hyödyntäen tietovirtoja ja mallinnusta ihmisen ja robotin yhteistoiminnan kontekstissa. Teollisuuden raportoima älykkäästi mukautuvien HRC-järjestelmien puute taas toimi osaltaan motivaationa tähän väitöskirjassa tehtyyn työhön. Kun tulevaisuuden tuotteet ja tuotantojärjestelmät muuttuvat monimutkaisemmiksi, tietojärjestelmiltä odotetaan suurempaa vastuuta korvaamaan ihmisen työmuistin luontaiset rajat ja mahdollistamaan siirtyminen kohti ihmiskeskeistä valmistusta, joihin viitataan termeillä Operator 4.0 ja Industry 5.0. Näin ollen on odotettavissa, että tietojärjestelmien tutkimus, kuten tämä väitöskirja, voi auttaa ottamaan merkittäviä askeleita tähän suuntaan.Information generated from the conceptualization, design, manufacturing, and use of a product has immense potential in transforming both the business and manufacturing processes of the manufacturing enterprise. The digital transformation at the heart of the fourth industrial revolution has acknowledged this with a special emphasis on weaving a thread of this information to support functions and systems throughout the life cycle of the product with what is known as a digital thread framework. This dissertation aims to develop and use one such framework in the context of human-robot collaborative assembly. The overarching problem that the framework aims to solve can be attributed to the abstract qualities of adaptability and flexibility. The human-robot collaboration (HRC) systems of today are built predominantly as static systems and ignore the intuitive role of humans by having their roles in collaborative tasks pre-defined. Furthermore, their ability to switch between products during product changeovers is also limited. This is especially problematic in the current era of product variety, stemming from the customised requirements of customers. To this end, this dissertation employs the design science research methodology to design, develop, and deploy predominantly three artefacts in a human-robot work cell in a laboratory setting. The first is the digital thread framework that integrates the product design environment using state-of-the-art knowledge-based engineering systems, as an agent of a multi-agent system, which provide the collaborative human-robot agents with access to product design models at run time. The second is a constituent mixed-reality model that provides an interface for the foregoing framework for the human operator engaged in collaborative assembly. The third is a supporting information model that the agents use as their knowledge base to fulfil adaptively the goals of collaborative assembly. Together, these developed artefacts were employed in case studies involving a real diesel engine assembly during which they were observed to provide utility and support the cause of adaptability for which the framework was designed. The identification of bounding boxes as a scalable information construct, that approximates the part geometry of the sub-assembly components, demonstrates the utility of the developed artefacts for spatially augmenting them as projections as intentions of collaborating agents. In summary, this dissertation contributes with an approach towards realising intelligent and adaptive robotics within the realms of information flows and modelling in the context of human-robot collaboration. The lack of intelligently adaptable HRC systems reported by the industry in part motivated the work undertaken in this dissertation. As future products and production systems become more complex, information systems are expected to assume greater responsibility to compensate for the inherent limits of the human working memory and enable transition towards a human-centred manufacturing, the current likes of which are labelled as Operator 4.0 and Industry 5.0. Thus, the expectation is that information systems research, such as this dissertation, can help take significant strides forward in this direction

    CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship

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    This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
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