435 research outputs found
Documenting Knowledge Graph Embedding and Link Prediction using Knowledge Graphs
In recent years, sub-symbolic learning, i.e., Knowledge Graph Embedding (KGE) incorporated with Knowledge Graphs (KGs) has gained significant attention in various downstream tasks (e.g., Link Prediction (LP)). These techniques learn a latent vector representation of KG's semantical structure to infer missing links. Nonetheless, the KGE models remain a black box, and the decision-making process behind them is not clear. Thus, the trustability and reliability of the model's outcomes have been challenged. While many state-of-the-art approaches provide data-driven frameworks to address these issues, they do not always provide a complete understanding, and the interpretations are not machine-readable. That is why, in this work, we extend a hybrid interpretable framework, InterpretME, in the field of the KGE models, especially for translation distance models, which include TransE, TransH, TransR, and TransD. The experimental evaluation on various benchmark KGs supports the validity of this approach, which we term Trace KGE. Trace KGE, in particular, contributes to increased interpretability and understanding of the perplexing KGE model's behavior
Current and Future Challenges in Knowledge Representation and Reasoning
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
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstΓΈtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er Γ₯ designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede lΓΈsningen er fokusert pΓ₯ forbedring av fysisk aktivitet. Prototypen bruker bΓ¦rbare 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 Γ₯ utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen pΓ₯ teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Semiring Provenance for Lightweight Description Logics
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
Integration of heterogeneous data sources and automated reasoning in healthcare and domotic IoT systems
In recent years, IoT technology has radically transformed many crucial industrial and service sectors such as healthcare. The multi-facets heterogeneity of the devices and the collected information provides important opportunities to develop innovative systems and services. However, the ubiquitous presence of data silos and the poor semantic interoperability in the IoT landscape constitute a significant obstacle in the pursuit of this goal. Moreover, achieving actionable knowledge from the collected data requires IoT information sources to be analysed using appropriate artificial intelligence techniques such as automated reasoning. In this thesis work, Semantic Web technologies have been investigated as an approach to address both the data integration and reasoning aspect in modern IoT systems. In particular, the contributions presented in this thesis are the following: (1) the IoT Fitness Ontology, an OWL ontology that has been developed in order to overcome the issue of data silos and enable semantic interoperability in the IoT fitness domain; (2) a Linked Open Data web portal for collecting and sharing IoT health datasets with the research community; (3) a novel methodology for embedding knowledge in rule-defined IoT smart home scenarios; and (4) a knowledge-based IoT home automation system that supports a seamless integration of heterogeneous devices and data sources
Π’Π΅Ρ Π½ΠΎΠ»ΠΎΠ³ΠΈΡ ΠΊΠΎΠΌΠΏΠ»Π΅ΠΊΡΠ½ΠΎΠΉ ΠΏΠΎΠ΄Π΄Π΅ΡΠΆΠΊΠΈ ΠΆΠΈΠ·Π½Π΅Π½Π½ΠΎΠ³ΠΎ ΡΠΈΠΊΠ»Π° ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΡΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ ΡΠΈΡΡΠ΅ΠΌ Π½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΡ
Π ΠΈΠ·Π΄Π°Π½ΠΈΠΈ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½ΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΠ΅ ΡΠ΅ΠΊΡΡΠ΅ΠΉ Π²Π΅ΡΡΠΈΠΈ ΠΎΡΠΊΡΡΡΠΎΠΉ ΡΠ΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ ΠΎΠ½ΡΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΡΡΠ²Π° ΠΈ ΡΠΊΡΠΏΠ»ΡΠ°ΡΠ°ΡΠΈΠΈ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΡΡ
Π³ΠΈΠ±ΡΠΈΠ΄Π½ΡΡ
ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ (Π’Π΅Ρ
Π½ΠΎΠ»ΠΎΠ³ΠΈΠΈ OSTIS). ΠΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π° ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΡΠ°Π½Π΄Π°ΡΡΠΈΠ·Π°ΡΠΈΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² ΠΈ
ΡΡΠ΅Π΄ΡΡΠ² ΠΈΡ
ΠΏΡΠΎΠ΅ΠΊΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΡΡΠΎ ΡΠ²Π»ΡΠ΅ΡΡΡ Π²Π°ΠΆΠ½Π΅ΠΉΡΠΈΠΌ ΡΠ°ΠΊΡΠΎΡΠΎΠΌ, ΠΎΠ±Π΅ΡΠΏΠ΅ΡΠΈΠ²Π°ΡΡΠΈΠΌ ΡΠ΅ΠΌΠ°Π½ΡΠΈΡΠ΅ΡΠΊΡΡ ΡΠΎΠ²ΠΌΠ΅ΡΡΠΈΠΌΠΎΡΡΡ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΈΡ
ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½ΡΠΎΠ², ΡΡΠΎ
ΡΡΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ΅ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ ΡΡΡΠ΄ΠΎΠ΅ΠΌΠΊΠΎΡΡΠΈ ΡΠ°Π·ΡΠ°Π±ΠΎΡΠΊΠΈ ΡΠ°ΠΊΠΈΡ
ΡΠΈΡΡΠ΅ΠΌ.
ΠΠ½ΠΈΠ³Π° ΠΏΡΠ΅Π΄Π½Π°Π·Π½Π°ΡΠ΅Π½Π° Π²ΡΠ΅ΠΌ, ΠΊΡΠΎ ΠΈΠ½ΡΠ΅ΡΠ΅ΡΡΠ΅ΡΡΡ ΠΏΡΠΎΠ±Π»Π΅ΠΌΠ°ΠΌΠΈ ΠΈΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΏΠ΅ΡΠΈΠ°Π»ΠΈΡΡΠ°ΠΌ Π² ΠΎΠ±Π»Π°ΡΡΠΈ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΡΠ°Π»ΡΠ½ΡΡ
ΠΊΠΎΠΌΠΏΡΡΡΠ΅ΡΠ½ΡΡ
ΡΠΈΡΡΠ΅ΠΌ ΠΈ ΠΈΠ½ΠΆΠ΅Π½Π΅ΡΠΈΠΈ Π·Π½Π°Π½ΠΈΠΉ. ΠΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½Π° ΡΡΡΠ΄Π΅Π½ΡΠ°ΠΌΠΈ, ΠΌΠ°Π³ΠΈΡΡΡΠ°Π½ΡΠ°ΠΌΠΈ ΠΈ Π°ΡΠΏΠΈΡΠ°Π½ΡΠ°ΠΌΠΈ ΡΠΏΠ΅ΡΠΈΠ°Π»ΡΠ½ΠΎΡΡΠΈ Β«ΠΡΠΊΡΡΡΡΠ²Π΅Π½Π½ΡΠΉ ΠΈΠ½ΡΠ΅Π»Π»Π΅ΠΊΡΒ».
Π’Π°Π±Π». 8. ΠΠ». 223. ΠΠΈΠ±Π»ΠΈΠΎΠ³Ρ.: 665 Π½Π°Π·Π²
Trustworthy machine learning through the lens of privacy and security
Nowadays, machine learning (ML) becomes ubiquitous and it is transforming society. However, there are still many incidents caused by ML-based systems when ML is deployed in real-world scenarios. Therefore, to allow wide adoption of ML in the real world, especially in critical applications such as healthcare, finance, etc., it is crucial to develop ML models that are not only accurate but also trustworthy (e.g., explainable, privacy-preserving, secure, and robust). Achieving trustworthy ML with different machine learning paradigms (e.g., deep learning, centralized learning, federated learning, etc.), and application domains (e.g., computer vision, natural language, human study, malware systems, etc.) is challenging, given the complicated trade-off among utility, scalability, privacy, explainability, and security. To bring trustworthy ML to real-world adoption with the trust of communities, this study makes a contribution of introducing a series of novel privacy-preserving mechanisms in which the trade-off between model utility and trustworthiness is optimized in different application domains, including natural language models, federated learning with human and mobile sensing applications, image classification, and explainable AI. The proposed mechanisms reach deployment levels of commercialized systems in real-world trials while providing trustworthiness with marginal utility drops and rigorous theoretical guarantees. The developed solutions enable safe, efficient, and practical analyses of rich and diverse user-generated data in many application domains
Combining Query Rewriting and Knowledge Graph Embeddings for Complex Query Answering
The field of complex query answering using Knowledge Graphs (KGs) has seen substantial advancements in recent years, primarily through the utilization of Knowledge Graph Embeddings (KGEs). However, these methodologies often stumble when faced with intricate query structures that involve multiple entities and relationships. This thesis primarily investigates the potential of integrating query rewriting techniques into the KGE query answering process to improve performance in such situations. Guided by a TBox, a schema that describes the concepts and relationships in the data from Description Logics, query rewriting translates a query into a union of rewritten queries that can potentially widen the prediction scope for KGEs. The thesis uses the PerfectRef algorithm for facilitating query rewriting, aiming to maximize the scope of query response and enhance prediction capabilities. Two distinct datasets were employed in the study: The Family Dataset, a subset of Wikidata, and DBPedia15k, a subset of DBPedia. The effectiveness of the proposed methodology was evaluated against these datasets using different KGE models, in our case TransE, DistMult, BoxE, RotatE, and CompGCN. The results demonstrate a notable improvement in complex query answering when query rewriting is used for both The Family dataset and DBPedia15k. Furthermore, the amalgamation of query rewriting and KGE predictions yielded a performance boost for The Family dataset. However, the same was not observed for DBPedia15k, likely due to discrepancies and errors present within DBPedia15k compared to the Full DBPedia KG used for validation in our framework. This research suggests that query rewriting, as a pre-processing step for KGE prediction, can enhance the performance of complex query answering, mainly when the dataset is not fully entailed. This study provides important insights into the potential and limitations of integrating query rewriting with KGEs. It may serve as a guidepost for future research to improve the complex query answering when a TBox is available.Masteroppgave i informatikkINF399MAMN-PROGMAMN-IN
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