256 research outputs found
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
Terminology and ontology development for semantic annotation : A use case on sepsis and adverse events
publishedVersio
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
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Trust, Accountability, and Autonomy in Knowledge Graph-Based AI for Self-Determination
Knowledge Graphs (KGs) have emerged as fundamental platforms for powering intelligent decision-making and a wide range of Artificial Intelligence (AI) services across major corporations such as Google, Walmart, and AirBnb. KGs complement Machine Learning (ML) algorithms by providing data context and semantics, thereby enabling further inference and question-answering capabilities. The integration of KGs with neuronal learning (e.g., Large Language Models (LLMs)) is currently a topic of active research, commonly named neuro-symbolic AI. Despite the numerous benefits that can be accomplished with KG-based AI, its growing ubiquity within online services may result in the loss of self-determination for citizens as a fundamental societal issue. The more we rely on these technologies, which are often centralised, the less citizens will be able to determine their own destinies. To counter this threat, AI regulation, such as the European Union (EU) AI Act, is being proposed in certain regions. The regulation sets what technologists need to do, leading to questions concerning How the output of AI systems can be trusted? What is needed to ensure that the data fuelling and the inner workings of these artefacts are transparent? How can AI be made accountable for its decision-making? This paper conceptualises the foundational topics and research pillars to support KG-based AI for self-determination. Drawing upon this conceptual framework, challenges and opportunities for citizen self-determination are illustrated and analysed in a real-world scenario. As a result, we propose a research agenda aimed at accomplishing the recommended objectives
Named Entity Resolution in Personal Knowledge Graphs
Entity Resolution (ER) is the problem of determining when two entities refer
to the same underlying entity. The problem has been studied for over 50 years,
and most recently, has taken on new importance in an era of large,
heterogeneous 'knowledge graphs' published on the Web and used widely in
domains as wide ranging as social media, e-commerce and search. This chapter
will discuss the specific problem of named ER in the context of personal
knowledge graphs (PKGs). We begin with a formal definition of the problem, and
the components necessary for doing high-quality and efficient ER. We also
discuss some challenges that are expected to arise for Web-scale data. Next, we
provide a brief literature review, with a special focus on how existing
techniques can potentially apply to PKGs. We conclude the chapter by covering
some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct.
2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and
applications' edited by Tiwari et a
Characteristic sets profile features: Estimation and application to SPARQL query planning
RDF dataset profiling is the task of extracting a formal representation of a dataset’s features. Such features may cover various aspects of the RDF dataset ranging from information on licensing and provenance to statistical descriptors of the data distribution and its semantics. In this work, we focus on the characteristics sets profile features that capture both structural and semantic information of an RDF dataset, making them a valuable resource for different downstream applications. While previous research demonstrated the benefits of characteristic sets in centralized and federated query processing, access to these fine-grained statistics is taken for granted. However, especially in federated query processing, computing this profile feature is challenging as it can be difficult and/or costly to access and process the entire data from all federation members. We address this shortcoming by introducing the concept of a profile feature estimation and propose a sampling-based approach to generate estimations for the characteristic sets profile feature. In addition, we showcase the applicability of these feature estimations in federated querying by proposing a query planning approach that is specifically designed to leverage these feature estimations. In our first experimental study, we intrinsically evaluate our approach on the representativeness of the feature estimation. The results show that even small samples of just 0.5% of the original graph’s entities allow for estimating both structural and statistical properties of the characteristic sets profile features. Our second experimental study extrinsically evaluates the estimations by investigating their applicability in our query planner using the well-known FedBench benchmark. The results of the experiments show that the estimated profile features allow for obtaining efficient query plans
Design Patterns for Situated Visualization in Augmented Reality
Situated visualization has become an increasingly popular research area in
the visualization community, fueled by advancements in augmented reality (AR)
technology and immersive analytics. Visualizing data in spatial proximity to
their physical referents affords new design opportunities and considerations
not present in traditional visualization, which researchers are now beginning
to explore. However, the AR research community has an extensive history of
designing graphics that are displayed in highly physical contexts. In this
work, we leverage the richness of AR research and apply it to situated
visualization. We derive design patterns which summarize common approaches of
visualizing data in situ. The design patterns are based on a survey of 293
papers published in the AR and visualization communities, as well as our own
expertise. We discuss design dimensions that help to describe both our patterns
and previous work in the literature. This discussion is accompanied by several
guidelines which explain how to apply the patterns given the constraints
imposed by the real world. We conclude by discussing future research directions
that will help establish a complete understanding of the design of situated
visualization, including the role of interactivity, tasks, and workflows.Comment: To appear in IEEE VIS 202
Machine Learning Algorithm for the Scansion of Old Saxon Poetry
Several scholars designed tools to perform the automatic scansion of poetry in many languages, but none of these tools
deal with Old Saxon or Old English. This project aims to be a first attempt to create a tool for these languages. We
implemented a Bidirectional Long Short-Term Memory (BiLSTM) model to perform the automatic scansion of Old Saxon
and Old English poems. Since this model uses supervised learning, we manually annotated the Heliand manuscript, and
we used the resulting corpus as labeled dataset to train the model. The evaluation of the performance of the algorithm
reached a 97% for the accuracy and a 99% of weighted average for precision, recall and F1 Score. In addition, we tested
the model with some verses from the Old Saxon Genesis and some from The Battle of Brunanburh, and we observed that
the model predicted almost all Old Saxon metrical patterns correctly misclassified the majority of the Old English input
verses
A Real-Time Approach for Smart Building Operations Prediction Using Rule-Based Complex Event Processing and SPARQL Query
Due to intelligent, adaptive nature towards various operations and their
ability to provide maximum comfort to the occupants residing in them, smart
buildings are becoming a pioneering area of research. Since these architectures
leverage the Internet of Things (IoT), there is a need for monitoring different
operations (Occupancy, Humidity, Temperature, CO2, etc.) to provide sustainable
comfort to the occupants. This paper proposes a novel approach for intelligent
building operations monitoring using rule-based complex event processing and
query-based approaches for dynamically monitoring the different operations.
Siddhi is a complex event processing engine designed for handling multiple
sources of event data in real time and processing it according to predefined
rules using a decision tree. Since streaming data is dynamic in nature, to keep
track of different operations, we have converted the IoT data into an RDF
dataset. The RDF dataset is ingested to Apache Kafka for streaming purposes and
for stored data we have used the GraphDB tool that extracts information with
the help of SPARQL query. Consequently, the proposed approach is also evaluated
by deploying the large number of events through the Siddhi CEP engine and how
efficiently they are processed in terms of time. Apart from that, a risk
estimation scenario is also designed to generate alerts for end users in case
any of the smart building operations need immediate attention. The output is
visualized and monitored for the end user through a tableau dashboard
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