arXiv subjects

Trond Linjordet

Publications and source records attributed to Trond Linjordet.

7 recordsLinked to original sources

arXiv2026

tmQM-RDF Dataset: a Knowledge Graph Representing Transition Metal Complexes

Transition Metal Complexes (TMCs) have wide-ranging practical utility in chemistry, with possible applications that range from catalysis to medicinal chemistry. The study of TMCs and their properties is thus a field rich with potential, one in which machine learning and computational approaches can offer a substantial aid. For this reason, appropriate and accessible datasets, collecting a wide range of information, are required in order to facilitate the effective analysis and investigation of such compounds. This paper contributes to the data modelling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) dataset, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 50k TMCs. These descriptions are both qualitative and quantitative in nature, encompassing the compositional nature of TMCs in terms of their constituting ligands, as well as the entirety of their molecular graphs. An example of the power of the proposed representation is presented, showcasing how the information available in tmQM-RDF can be exploited for TMC manipulation tasks, achieving promising performance even with relatively simple probabilistic models.

physics.chem-ph↗

arXiv2023

An Ecosystem for Personal Knowledge Graphs: A Survey and Research Roadmap

This paper presents an ecosystem for personal knowledge graphs (PKGs), commonly defined as resources of structured information about entities related to an individual, their attributes, and the relations between them. PKGs are a key enabler of secure and sophisticated personal data management and personalized services. However, there are challenges that need to be addressed before PKGs can achieve widespread adoption. One of the fundamental challenges is the very definition of what constitutes a PKG, as there are multiple interpretations of the term. We propose our own definition of a PKG, emphasizing the aspects of (1) data ownership by a single individual and (2) the delivery of personalized services as the primary purpose. We further argue that a holistic view of PKGs is needed to unlock their full potential, and propose a unified framework for PKGs, where the PKG is a part of a larger ecosystem with clear interfaces towards data services and data sources. A comprehensive survey and synthesis of existing work is conducted, with a mapping of the surveyed work into the proposed unified ecosystem. Finally, we identify open challenges and research opportunities for the ecosystem as a whole, as well as for the specific aspects of PKGs, which include population, representation and management, and utilization.

cs.AI↗

arXiv2022

Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering

Knowledge graph question answering (KGQA) facilitates information access by leveraging structured data without requiring formal query language expertise from the user. Instead, users can express their information needs by simply asking their questions in natural language (NL). Datasets used to train KGQA models that would provide such a service are expensive to construct, both in terms of expert and crowdsourced labor. Typically, crowdsourced labor is used to improve template-based pseudo-natural questions generated from formal queries. However, the resulting datasets often fall short of representing genuinely natural and fluent language. In the present work, we investigate ways to characterize and remedy these shortcomings. We create the IQN-KGQA test collection by sampling questions from existing KGQA datasets and evaluating them with regards to five different aspects of naturalness. Then, the questions are rewritten to improve their fluency. Finally, the performance of existing KGQA models is compared on the original and rewritten versions of the NL questions. We find that some KGQA systems fare worse when presented with more realistic formulations of NL questions. The IQN-KGQA test collection is a resource to help evaluate KGQA systems in a more realistic setting. The construction of this test collection also sheds light on the challenges of constructing large-scale KGQA datasets with genuinely NL questions.

cs.IR↗

arXiv2020

Sanitizing Synthetic Training Data Generation for Question Answering over Knowledge Graphs

Synthetic data generation is important to training and evaluating neural models for question answering over knowledge graphs. The quality of the data and the partitioning of the datasets into training, validation and test splits impact the performance of the models trained on this data. If the synthetic data generation depends on templates, as is the predominant approach for this task, there may be a leakage of information via a shared basis of templates across data splits if the partitioning is not performed hygienically. This paper investigates the extent of such information leakage across data splits, and the ability of trained models to generalize to test data when the leakage is controlled. We find that information leakage indeed occurs and that it affects performance. At the same time, the trained models do generalize to test data under the sanitized partitioning presented here. Importantly, these findings extend beyond the particular flavor of question answering task we studied and raise a series of difficult questions around template-based synthetic data generation that will necessitate additional research.

cs.IR↗

arXiv2019

Impact of Training Dataset Size on Neural Answer Selection Models

It is held as a truism that deep neural networks require large datasets to train effective models. However, large datasets, especially with high-quality labels, can be expensive to obtain. This study sets out to investigate (i) how large a dataset must be to train well-performing models, and (ii) what impact can be shown from fractional changes to the dataset size. A practical method to investigate these questions is to train a collection of deep neural answer selection models using fractional subsets of varying sizes of an initial dataset. We observe that dataset size has a conspicuous lack of effect on the training of some of these models, bringing the underlying algorithms into question.

cs.IR↗

arXiv2013

Detecting Topological Entanglement Entropy in a Lattice of Quantum Harmonic Oscillators

The Kitaev surface-code model is the most studied example of a topologically ordered phase and typically involves four-spin interactions on a two-dimensional surface. A universal signature of this phase is topological entanglement entropy (TEE), but due to low signal to noise, it is extremely difficult to observe in these systems, and one usually resorts to measuring anyonic statistics of excitations or non-local string operators to reveal the order. We describe a continuous-variable analog to the surface code using quantum harmonic oscillators on a two-dimensional lattice, which has the distinctive property of needing only two-body nearest-neighbor interactions for its creation. Though such a model is gapless, satisfies an area law, and the ground state can be simply prepared by measurements on a finitely squeezed and gapped two-dimensional cluster state, which does not have topological order. Asymptotically, the TEE grows linearly with the squeezing parameter, and we show that its mixed-state generalization, the topological mutual information, is robust to some forms of state preparation error and can be detected simply using single-mode quadrature measurements. Finally, we discuss scalable implementation of these methods using optical and circuit-QED technology.

quant-ph↗

arXiv2010

Integrated photonic 3D waveguide arrays for quantum random walks on a circle

Quantum random walks (QRWs) can be used to perform both quantum simulations and quantum algorithms. In order to exploit this potential, quantum walks on different types of graphs must be physically implemented. To this end we design, model and experimentally fabricate, using the femtosecond laser direct-write technique, a 3D tubular waveguide array within glass to implement a photonic quantum walk on a circle. The boundary conditions of a QRW on a circle naturally suggests a 3D waveguide implementation - allowing much simpler device design than what could be achieved using a 2D waveguide architecture. We show that, in some cases, three-dimensional photonic circuits can be more suited to the simulation of complex quantum phenomena.

quant-ph↗