tronlinj

• Cibinel, Luca; Linjordet, Trond; Pensar, Johan; Badia, David Balcells; Bin, Riccardo De & Ell, Basil (2026). tmQM-RDF Data Set: A Knowledge Graph Representing Transition Metal Complexes. Journal of Chemical Information and Modeling. ISSN 1549-9596. 66(13), s. 7524–7538. doi: 10.1021/acs.jcim.6c01281. Fulltekst i vitenarkiv Vis sammendrag

  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 data sets, 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 modeling effort via the introduction of the transition metal quantum mechanics RDF (tmQM-RDF) data set, a knowledge graph constructed using the Resource Description Framework (RDF) vocabulary which collects rich and detailed descriptions of approximately 60k TMCs. These descriptions are both qualitative and quantitative, encompassing the compositional nature of TMCs as well as the entirety of their molecular graphs. The Python package tmqmrdfdata is released together with the knowledge graph to provide a user-friendly interface. 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.

• Strandgaard, Magnus; Linjordet, Trond; Kneiding, Hannes; Burnage, Arron Luc; Nova, Ainara & Jensen, Jan Halborg [Vis alle 7 forfattere av denne artikkelen] (2025). A Deep Generative Model for the Inverse Design of Transition Metal Ligands and Complexes. JACS Au. 5(5), s. 2294–2308. doi: 10.1021/jacsau.5c00242. Fulltekst i vitenarkiv Vis sammendrag

  Deep generative models yielding transition metal complexes (TMCs) remain scarce despite the key role of these compounds in industrial catalytic processes, anticancer therapies, and the energy transition. Compared to drug discovery within the chemical space of organic molecules, TMCs pose further challenges, including the encoding of chemical bonds of higher complexity and the need to optimize multiple properties. In this work, we developed a generative model for the inverse design of transition metal ligands and complexes, based on the junction tree variational autoencoder (JT-VAE). After implementing a SMILES-based encoding of the metal–ligand bonds, the model was trained with the tmQMg-L ligand library, allowing for the generation of thousands of novel, highly diverse monodentate (κ1) and bidentate (κ2) ligands, including imines, phosphines, and carbenes. Further, the generated ligands were labeled with two target properties reflecting the stability and electron density of the associated homoleptic iridium TMCs: the HOMO–LUMO gap (ϵ) and the charge of the metal center (qIr). This data was used to implement a conditional model that generated ligands from a prompt, with the single- or dual-objective of optimizing either or both the ϵ and qIr properties and allowing for chemical interpretation based on the optimization trajectories. The optimizations also had an impact on other chemical properties, including ligand dissociation energies and oxidative addition barriers. A similar model was implemented to condition ligand generation by solubility and steric bulk.

• Skjæveland, Martin G; Balog, Krisztian; Bernard, Nolwenn; Lajewska, Weronika & Linjordet, Trond (2024). An ecosystem for personal knowledge graphs: A survey and research roadmap. AI Open. 5, s. 55–69. doi: 10.1016/j.aiopen.2024.01.003. Fulltekst i vitenarkiv Vis sammendrag

  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.

• Linjordet, Trond; Balog, Krisztian & Setty, Vinay (2022). Towards Formally Grounded Evaluation Measures for Semantic Parsing-based Knowledge Graph Question Answering. I Crestani, Fabio; Pasi, Gabriella & Gaussier, Eric (Red.), Proceedings of the 2022 ACM SIGIR International Conference on Theory of Information Retrieval (ICTIR '22). Association for Computing Machinery (ACM). ISSN 9781450394123. s. 3–12. doi: 10.1145/3539813.3545146. Fulltekst i vitenarkiv
• Linjordet, Trond & Balog, Krisztian (2022). Would You Ask it that Way? Measuring and Improving Question Naturalness for Knowledge Graph Question Answering. I Amigó, Enrique; Castells, Pablo; Gonzalo, Julio; Carterette, Ben; Culpepper, J. Shane & Kazai, Gabriella (Red.), SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, July 11 - 15, 2022. Association for Computing Machinery (ACM). ISSN 9781450387323. s. 3090–3098. doi: 10.1145/3477495.3531739. Fulltekst i vitenarkiv
• Linjordet, Trond (2020). Neural (Knowledge Graph) Question Answering Using Synthetic Training Data. I d'Aquin, Mathieu & Dietze, Stefan (Red.), CIKM 20 : The 29th ACM international conference on information and knowledge management : Virtual Event Ireland, October 19-23, 2020.. ACM Publications. ISSN 9781450368599. s. 3245–3248. doi: 10.1145/3340531.3418505. Fulltekst i vitenarkiv
• Linjordet, Trond & Balog, Krisztian (2020). Sanitizing Synthetic Training Data Generation for Question Answering over Knowledge Graphs. I Balog, Krisztian; Setty, Vinay; Lioma, Christina; Liu, Yiqun; Zhang, Min & Berberich, Klaus (Red.), ICTIR '20: Proceedings of the 2020 ACM SIGIR on International Conference on Theory of Information Retrieval. Association for Computing Machinery (ACM). ISSN 9781450380676. s. 121–128. doi: 10.1145/3409256.3409836. Fulltekst i vitenarkiv
• Linjordet, Trond & Balog, Krisztian (2019). Impact of training dataset size on neural answer selection models. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. 11437 LNCS, s. 828–835. doi: 10.1007/978-3-030-15712-8_59. Fulltekst i vitenarkiv
• Martin, Christopher; Evans, James; Karvonen, Andrew; Paskaleva, Krassimira; Yang, Dujuan & Linjordet, Trond (2019). Smart-sustainability: A new urban fix? Sustainable Cities and Society (SCS). ISSN 2210-6707. 45, s. 640–648. doi: 10.1016/j.scs.2018.11.028. Fulltekst i vitenarkiv
• Paskaleva, Krassimira; Evans, James; Martin, Christopher; Linjordet, Trond; Yang, Dujuan & Karvonen, Andrew (2017). Data governance in the sustainable smart city. Informatics. 4(4), s. 1–19. doi: 10.3390/informatics4040041. Fulltekst i vitenarkiv

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• Linjordet, Trond (2015). Sannsynligvis eksamensklar : krasjkurs i grunnleggende statistikk for universitet og høyskole. Ideklekkeriet forlag. ISBN 9788293460008. 177 s. Fulltekst i vitenarkiv

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• Linjordet, Trond; Balog, Krisztian; Engan, Kjersti & Eftestøl, Trygve Christian (2022). A Critical Look at the Evaluation of Knowledge Graph Question Answering. Universitetet i Stavanger. ISSN 9788284391304. Fulltekst i vitenarkiv Vis sammendrag

  The field of information retrieval (IR) is concerned with systems that “make a given stored collection of information items available to a user population” [111]. The way in which information is made available to the user depends on the formulation of this broad concern of IR into specific tasks by which a system should address a user’s information need [85]. The specific IR task also dictates how the user may express their information need. The classic IR task is ad hoc retrieval, where the user issues a query to the system and gets in return a list of documents ranked by estimated relevance of each document to the query [85]. However, it has long been acknowledged that users are often looking for answers to questions, rather than an entire document or ranked list of documents [17, 141]. Question answering (QA) is thus another IR task; it comes in many flavors, but overall consists of taking in a user’s natural language (NL) question and returning an answer. This thesis describes work done within the scope of the QA task. The flavor of QA called knowledge graph question answering (KGQA) is taken as the primary focus, which enables QA with factual questions against structured data in the form of a knowledge graph (KG). This means the KGQA system addresses a structured representation of knowledge rather than—as in other QA flavors—an unstructured prose context. KGs have the benefit that given some identified entities or predicates, all associated properties are available and relationships can be utilized. KGQA then enables users to access structured data using only NL questions and without requiring formal query language expertise. Even so, the construction of satisfactory KGQA systems remains a challenge. Machine learning with deep neural networks (DNNs) is a far more promising approach than manually engineering retrieval models [29, 56, 130]. The current era dominated by DNNs began with seminal work on computer vision, where the deep learning paradigm demonstrated its first cases of “superhuman” performance [32, 71]. Subsequent work in other applications has also demonstrated “superhuman” performance with DNNs [58, 87]. As a result of its early position and hence longer history as a leading application of deep learning, computer vision with DNNs has been bolstered with much work on different approaches towards augmenting [120] or synthesizing [94] additional training data. The difficulty with machine learning approaches to KGQA appears to rest in large part with the limited volume, quality, and variety of available datasets for this task. Compared to labeled image data for computer vision, the problems of data collection, augmentation, and synthesis are only to a limited extent solved for QA, and especially for KGQA. There are few datasets for KGQA overall, and little previous work that has found unsupervised or semi-supervised learning approaches to address the sparsity of data. Instead, neural network approaches to KGQA rely on either fully or weakly supervised learning [29]. We are thus concerned with neural models trained in a supervised setting to perform QA tasks, especially of the KGQA flavor. Given a clear task to delegate to a computational system, it seems clear that we want the task performed as well as possible. However, what methodological elements are important to ensure good system performance within the chosen scope? How should the quality of system performance be assessed? This thesis describes work done to address these overarching questions through a number of more specific research questions. Altogether, we designate the topic of this thesis as KGQA evaluation, which we address in a broad sense, encompassing four subtopics from (1) the impact on performance due to volume of training data provided and (2) the information leakage between training and test splits due to unhygienic data partitioning, through (3) the naturalness of NL questions resulting from a common approach for generating KGQA datasets, to (4) the axiomatic analysis and development of evaluation measures for a specific flavor of the KGQA task. Each of the four subtopics is informed by previous work, but we aim in this thesis to critically examine the assumptions of previous work to uncover, verify, or address weaknesses in current practices surrounding KGQA evaluation.

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