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Frank van Harmelen

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34 papers
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34

JBHI Journal 2025 Journal Article

KG4NH: A Comprehensive Knowledge Graph for Question Answering in Dietary Nutrition and Human Health

  • Chengcheng Fu
  • Xueli Pan
  • Jieyu Wu
  • Junkai Cai
  • Zhisheng Huang
  • Frank Van Harmelen
  • Weizhong Zhao
  • Xingpeng Jiang

It is commonly known that food nutrition is closely related to human health. The complex interactions between food nutrients and diseases, influenced by gut microbial metabolism, present challenges in systematizing and practically applying knowledge. To address this, we propose a method for extracting triples from a vast amount of literature, which is used to construct a comprehensive knowledge graph on nutrition and human health. Concurrently, we develop a query-based question answering system over our knowledge graph, proficiently addressing three types of questions. The results show that our proposed model outperforms other state-of-art methods, achieving a precision of 0. 92, a recall of 0. 81, and an F1 score of 0. 86 in the nutrition and disease relation extraction task. Meanwhile, our question answering system achieves an accuracy of 0. 68 and an F1 score of 0. 61 on our benchmark dataset, showcasing competitiveness in practical scenarios. Furthermore, we design five independent experiments to assess the quality of the data structure in the knowledge graph, ensuring results characterized by high accuracy and interpretability. In conclusion, the construction of our knowledge graph shows significant promise in facilitating diet recommendations, enhancing patient care applications, and informing decision-making in clinical research.

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NAI Journal 2025 Journal Article

Towards semantically enriched embeddings for knowledge graph completion

  • Mehwish Alam
  • Frank Van Harmelen
  • Maribel Acosta

Embedding based Knowledge Graph (KG) completion has gained much attention over the past few years. Most of the current algorithms consider a KG as a multidirectional labeled graph and lack the ability to capture the semantics underlying the schematic information. This position paper revises the state of the art and discusses several variations of the existing algorithms for KG completion, which are discussed progressively based on the level of expressivity of the semantics utilized. The paper begins with analysing various KG completion algorithms considering only factual information such as transductive and inductive link prediction and entity type prediction algorithms. It then revises the algorithms utilizing Large Language Models as background knowledge. Afterwards, it discusses the algorithms progressively utilizing semantic information such as class hierarchy information within the KGs and semantics represented in different description logic axioms. The paper concludes with a critical reflection on the current state of work in the community, where we argue that the aspects of semantics, rigorous evaluation protocols, and bias against external sources have not been sufficiently addressed in the literature, which hampers a more thorough understanding of advantages and limitations of existing approaches. Lastly, we provide recommendations for future directions.

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AIIM Journal 2024 Journal Article

Guideline-informed reinforcement learning for mechanical ventilation in critical care

  • Floris den Hengst
  • Martijn Otten
  • Paul Elbers
  • Frank Van Harmelen
  • Vincent François-Lavet
  • Mark Hoogendoorn

Reinforcement Learning (RL) has recently found many applications in the healthcare domain thanks to its natural fit to clinical decision-making and ability to learn optimal decisions from observational data. A key challenge in adopting RL-based solution in clinical practice, however, is the inclusion of existing knowledge in learning a suitable solution. Existing knowledge from e. g. medical guidelines may improve the safety of solutions, produce a better balance between short- and long-term outcomes for patients and increase trust and adoption by clinicians. We present a framework for including knowledge available from medical guidelines in RL. The framework includes components for enforcing safety constraints and an approach that alters the learning signal to better balance short- and long-term outcomes based on these guidelines. We evaluate the framework by extending an existing RL-based mechanical ventilation (MV) approach with clinically established ventilation guidelines. Results from off-policy policy evaluation indicate that our approach has the potential to decrease 90-day mortality while ensuring lung protective ventilation. This framework provides an important stepping stone towards implementations of RL in clinical practice and opens up several avenues for further research.

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NeurIPS Conference 2023 Conference Paper

A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference

  • Emile van Krieken
  • Thiviyan Thanapalasingam
  • Jakub Tomczak
  • Frank Van Harmelen
  • Annette Ten Teije

We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perform exponential-time exact inference, limiting the scalability of PNL solutions. We introduce Approximate Neurosymbolic Inference (A-NeSI): a new framework for PNL that uses neural networks for scalable approximate inference. A-NeSI 1) performs approximate inference in polynomial time without changing the semantics of probabilistic logics; 2) is trained using data generated by the background knowledge; 3) can generate symbolic explanations of predictions; and 4) can guarantee the satisfaction of logical constraints at test time, which is vital in safety-critical applications. Our experiments show that A-NeSI is the first end-to-end method to solve three neurosymbolic tasks with exponential combinatorial scaling. Finally, our experiments show that A-NeSI achieves explainability and safety without a penalty in performance.

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AIIM Journal 2023 Journal Article

Food4healthKG: Knowledge graphs for food recommendations based on gut microbiota and mental health

  • Chengcheng Fu
  • Zhisheng Huang
  • Frank Van Harmelen
  • Tingting He
  • Xingpeng Jiang

Food is increasingly acknowledged as a powerful means to promote and maintain mental health. The introduction of the gut-brain axis has been instrumental in understanding the impact of food on mental health. It is widely reported that food can significantly influence gut microbiota metabolism, thereby playing a pivotal role in maintaining mental health. However, the vast amount of heterogeneous data published in recent research lacks systematic integration and application development. To remedy this, we construct a comprehensive knowledge graph, named Food4healthKG, focusing on food, gut microbiota, and mental diseases. The constructed workflow includes the integration of numerous heterogeneous data, entity linking to a normalized format, and the well-designed representation of the acquired knowledge. To illustrate the availability of Food4healthKG, we design two case studies: the knowledge query and the food recommendation based on Food4healthKG. Furthermore, we propose two evaluation methods to validate the quality of the results obtained from Food4healthKG. The results demonstrate the system’s effectiveness in practical applications, particularly in providing convincing food recommendations based on gut microbiota and mental health. Food4healthKG is accessible at https: //github. com/ccszbd/Food4healthKG.

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AIJ Journal 2022 Journal Article

Analyzing Differentiable Fuzzy Logic Operators

  • Emile van Krieken
  • Erman Acar
  • Frank Van Harmelen

The AI community is increasingly putting its attention towards combining symbolic and neural approaches, as it is often argued that the strengths and weaknesses of these approaches are complementary. One recent trend in the literature is weakly supervised learning techniques that employ operators from fuzzy logics. In particular, these use prior background knowledge described in such logics to help the training of a neural network from unlabeled and noisy data. By interpreting logical symbols using neural networks, this background knowledge can be added to regular loss functions, hence making reasoning a part of learning. We study, both formally and empirically, how a large collection of logical operators from the fuzzy logic literature behave in a differentiable learning setting. We find that many of these operators, including some of the most well-known, are highly unsuitable in this setting. A further finding concerns the treatment of implication in these fuzzy logics, and shows a strong imbalance between gradients driven by the antecedent and the consequent of the implication. Furthermore, we introduce a new family of fuzzy implications (called sigmoidal implications) to tackle this phenomenon. Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and compare how different operators behave in practice. We find that, to achieve the largest performance improvement over a supervised baseline, we have to resort to non-standard combinations of logical operators which perform well in learning, but no longer satisfy the usual logical laws.

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IJCAI Conference 2022 Conference Paper

Reinforcement Learning with Option Machines

  • Floris den Hengst
  • Vincent Francois-Lavet
  • Mark Hoogendoorn
  • Frank Van Harmelen

Reinforcement learning (RL) is a powerful framework for learning complex behaviors, but lacks adoption in many settings due to sample size requirements. We introduce a framework for increasing sample efficiency of RL algorithms. Our approach focuses on optimizing environment rewards with high-level instructions. These are modeled as a high-level controller over temporally extended actions known as options. These options can be looped, interleaved and partially ordered with a rich language for high-level instructions. Crucially, the instructions may be underspecified in the sense that following them does not guarantee high reward in the environment. We present an algorithm for control with these so-called option machines (OMs), discuss option selection for the partially ordered case and describe an algorithm for learning with OMs. We compare our approach in zero-shot, single- and multi-task settings in an environment with fully specified and underspecified instructions. We find that OMs perform significantly better than or comparable to the state-of-art in all environments and learning settings.

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KR Conference 2020 Conference Paper

Analyzing Differentiable Fuzzy Implications

  • Emile van Krieken
  • Erman Acar
  • Frank Van Harmelen

Combining symbolic and neural approaches has gained considerable attention in the AI community, as it is argued that their strengths and weaknesses are complementary. One trend in the literature are weakly supervised learning techniques that employ operators from fuzzy logics. They use prior background knowledge described in such logics to help training neural networks from unlabeled and noisy data. By interpreting logical symbols using neural networks (or grounding them), this background knowledge can be added to regular loss functions, hence making reasoning a part of learning. We investigate how implications from the fuzzy logic literature behave in a differentiable setting. In this setting, we analyze the differences between the formal properties of these fuzzy implications. It turns out that various fuzzy implications, including some of the most well-known, are highly unsuitable for use in a differentiable learning setting. A further finding shows a strong imbalance between gradients driven by the antecedent and the consequent of the implication. Furthermore, we introduce a new family of fuzzy implications (called sigmoidal implications) to tackle this phenomenon. Finally, we empirically show that it is possible to use Differentiable Fuzzy Logics for semi-supervised learning, and show that sigmoidal implications outperform other choices of fuzzy implications.

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FLAP Journal 2019 Journal Article

Semi-supervised Learning using Differentiable Reasoning.

  • Emile van Krieken
  • Erman Acar
  • Frank Van Harmelen

We introduce Differentiable Reasoning (DR), a novel semi-supervised learning technique which uses relational background knowledge to benefit from unlabeled data. We apply it to the Semantic Image Interpretation (SII) task and show that background knowledge provides significant improvement. We find that there is a strong but interesting imbalance between the contributions of updates from Modus Ponens (MP) and its logical equivalent Modus Tollens (MT) to the learning process, suggesting that our approach is very sensitive to a phenomenon called the Raven Paradox [10]. We propose a solution to overcome this situation.

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AIIM Journal 2017 Journal Article

Analyzing interactions on combining multiple clinical guidelines

  • Veruska Zamborlini
  • Marcos Da Silveira
  • Cedric Pruski
  • Annette Ten Teije
  • Edwin Geleijn
  • Marike van der Leeden
  • Martijn Stuiver
  • Frank Van Harmelen

Accounting for patients with multiple health conditions is a complex task that requires analysing potential interactions among recommendations meant to address each condition. Although some approaches have been proposed to address this issue, important features still require more investigation, such as (re)usability and scalability. To this end, this paper presents an approach that relies on reusable rules for detecting interactions among recommendations coming from various guidelines. It extends a previously proposed knowledge representation model (TMR) to enhance the detection of interactions and it provides a systematic analysis of relevant interactions in the context of multimorbidity. The approach is evaluated in a case study on rehabilitation of breast cancer patients, developed in collaboration with experts. The results are considered promising to support the experts in this task.

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IS Journal 2009 Journal Article

It's a Streaming World! Reasoning upon Rapidly Changing Information

  • Emanuele Della Valle
  • Stefano Ceri
  • Frank Van Harmelen
  • Dieter Fensel

Data streams occur in modern applications such as sensor network monitoring, traffic engineering, RFID tags applications, telecom call recording, medical record management, financial applications, and clickstreams. On the Web, many sites distribute and present information in real-time streams. In many of these application areas, the ability to perform complex reasoning tasks that combine streaming data with evolving knowledge would be of great benefit. Stream reasoning—an unexplored, yet high-impact research area—is a new multidisciplinary approach that will build on the Semantic Web and provide the abstractions, foundations, methods, and tools required to integrate data streams and reasoning systems.

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AIIM Journal 2009 Journal Article

Using model checking for critiquing based on clinical guidelines

  • Perry Groot
  • Arjen Hommersom
  • Peter J.F. Lucas
  • Robbert-Jan Merk
  • Annette Ten Teije
  • Frank Van Harmelen
  • Radu Serban

Objective Medical critiquing systems compare clinical actions performed by a physician with a predefined set of actions. In order to provide useful feedback, an important task is to find differences between the actual actions and a set of ‘ideal’ actions as described by a clinical guideline. In case differences exist, the critiquing system provides insight into the extent to which they are compatible. Methods and material We propose a computational method for such critiquing, where the ideal actions are given by a formal model of a clinical guideline, and where the actual actions are derived from real world patient data. We employ model checking to investigate whether a part of the actual treatment is consistent with the guideline. Results We show how critiquing can be cast in terms of temporal logic, and what can be achieved by using model checking. Furthermore, a method is introduced for off-line computing relevant information which can be exploited during critiquing. The method has been applied to a clinical guideline of breast cancer in conjunction with breast cancer patient data.

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AIIM Journal 2007 Journal Article

Extraction and use of linguistic patterns for modelling medical guidelines

  • Radu Serban
  • Annette Ten Teije
  • Frank Van Harmelen
  • Mar Marcos
  • Cristina Polo-Conde

Objective The quality of knowledge updates in evidence-based medical guidelines can be improved and the effort spent for updating can be reduced if the knowledge underlying the guideline text is explicitly modelled using the so-called linguistic guideline patterns, mappings between a text fragment and a formal representation of its corresponding medical knowledge. Methods and material Ontology-driven extraction of linguistic patterns is a method to automatically reconstruct the control knowledge captured in guidelines, which facilitates a more effective modelling and authoring of medical guidelines. We illustrate by examples the use of this method for generating and instantiating linguistic patterns in the text of a guideline for treatment of breast cancer, and evaluate the usefulness of these patterns in the modelling of this guideline. Results We developed a methodology for extracting and using linguistic patterns in guideline formalization, to aid the human modellers in guideline formalization and reduce the human modelling effort. Using automatic transformation rules for simple linguistic patterns, a good recall (between 72% and 80%) is obtained in selecting the procedural knowledge relevant for the guideline model, even though the precision of the guideline model generated automatically covers only between 20% and 35% of the human-generated guideline model. These results indicate the suitability of our method as a pre-processing step in medical guideline formalization. Conclusions Modelling and authoring of medical texts can benefit from our proposed method. As pre-requisites for generating automatically a skeleton of the guideline model from the procedural part of the guideline text, to aid the human modeller, the medical terminology used by the guideline must have a good overlap with existing medical thesauri and its procedural knowledge must obey linguistic regularities that can be mapped into the control constructs of the target guideline modelling language.

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AIIM Journal 2006 Journal Article

Improving medical protocols by formal methods

  • Annette Ten Teije
  • Mar Marcos
  • Michel Balser
  • Joyce van Croonenborg
  • Christoph Duelli
  • Frank Van Harmelen
  • Peter Lucas
  • Silvia Miksch

Objectives During the last decade, evidence-based medicine has given rise to an increasing number of medical practice guidelines and protocols. However, the work done on developing and distributing protocols outweighs the efforts on guaranteeing their quality. Indeed, anomalies like ambiguity and incompleteness are frequent in medical protocols. Recent efforts have tried to address the problem of protocol improvement, but they are not sufficient since they rely on informal processes and notations. Our objective is to improve the quality of medical protocols. Approach The solution we suggest to the problem of quality improvement of protocols consists in the utilisation of formal methods. It requires the definition of an adequate protocol representation language, the development of techniques for the formal analysis of protocols described in that language and, more importantly, the evaluation of the feasibility of the approach based on the formalisation and verification of real-life medical protocols. For the first two aspects we rely on earlier work from the fields of knowledge representation and formal methods. The third aspect, i. e. the evaluation of the use of formal methods in the quality improvement of protocols, constitutes our main objective. The steps with which we have carried out this evaluation are the following: (1) take two real-life reference protocols which cover a wide variety of protocol characteristics; (2) formalise these reference protocols; (3) check the formalisation for the verification of interesting protocol properties; and (4) determine how many errors can be uncovered in this way. Results Our main results are: a consolidated formal language to model medical practice protocols; two protocols, each both modelled and formalised; a list of properties that medical protocols should satisfy; verification proofs for these protocols and properties; and perspectives of the potentials of this approach. Our results have been evaluated by a panel of medical experts, who judged that the problems we detected in the protocols with the help of formal methods were serious and should be avoided. Conclusions We have succeeded in demonstrating the feasibility of formal methods for improving medical protocols.

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IJCAI Conference 2005 Conference Paper

Reasoning with Inconsistent Ontologies

  • Zhisheng Huang
  • Frank Van Harmelen
  • Annette ten

In this paper we present a framework of reasoning with inconsistent ontologies, in which pre-defined selection functions are used to deal with concept relevance. We examine how the notion of ”concept relevance” can be used for reasoning with inconsistent ontologies. We have implemented a prototype called PION (Processing Inconsistent ONtologies), which is based on a syntactic relevance-based selection function. In this paper, we also report the experiments with PION.

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KER Journal 2002 Journal Article

How the semantic web will change KR: challenges and opportunities for a new research agenda

  • Frank Van Harmelen

Currently the Web is the largest available environment for the deployment of agents, and much work in agent research is driven by Web-based applications (Luke et al. (1997), Joachims et al. (1997), Bollacker et al. (1998), Doorenbos et al. (1997) are just some examples; see also the May 2000 special issue of the Artificial Intelligence Journal on intelligent internet systems, 118 (1–2)). However, such applications of agent technology are hampered by the fact that the Web is not geared towards agent use, but is rather designed for human use. Current Web resources are lacking in explicit, machine-accessible descriptions of their contents; they are only fully accessible to agents with a competent grasp of English (i.e. limited to human agents only).

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AIIM Journal 1999 Journal Article

A study of PROforma, a development methodology for clinical procedures

  • Arjen Vollebregt
  • Annette Ten Teije
  • Frank Van Harmelen
  • Johan van der Lei
  • Mees Mosseveld

Knowledge engineering has shown that besides the general methodologies from software engineering it is useful to develop special purpose methodologies for knowledge based systems (KBS). PROforma is a newly developed methodology for a specific type of knowledge based systems. PROforma is intended for decision support systems and in particular for clinical procedures in the medical domain. This paper reports on an evaluation study of PROforma, and on the trade-off that is involved between general purpose and special purpose development methods in Knowledge Engineering and Medical AI. Our method for evaluating PROforma is based on re-engineering a realistic system in two methodologies: the new and special purpose KBS methodology PROforma and the widely accepted, and more general KBS methodology CommonKADS. The four most important results from our study are as follows. Firstly, PROforma has some strong points which are also strong related to requirements of medical reasoning. Secondly, PROforma has some weak points, but none of them are in any way related to the special purpose nature of PROforma. Thirdly, a more general method like CommonKADS works better in the analysis phase than the more special purpose method PROforma. Finally, to support a complementary use of the methodologies, we propose a mapping between their respective languages.

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AIIM Journal 1997 Journal Article

Formalisation for decision support in anaesthesiology

  • Gerard R.Renardel de Lavalette
  • Rix Groenboom
  • Ernest Rotterdam
  • Frank Van Harmelen
  • Annette Ten Teije
  • Fred de Geus

This paper reports on research for decision support for anaesthesiologists at the University Hospital in Groningen, the Netherlands. Based on Carola, an existing automated Operation documentation system, we designed a support environment that will assist in real-time diagnosis. The core of the work presented here consists of a knowledge base (containing anaesthesiological knowledge) and a diagnosis system. The knowledge base is specified in the logic-based formal specification language AFSL. This leads to a powerful and precise treatment of knowledge structuring and data abstraction.

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KER Journal 1995 Journal Article

Formal methods in knowledge engineering

  • Frank Van Harmelen
  • Dieter Fensel

Abstract This paper presents a general discussion of the role of formal methods in knowledge engineering. We give an historical account of the development of the field of knowledge engineering towards the use of formal methods. Subsequently, we discuss the pros and cons of formal methods. We do this by summarising the proclaimed advantages, and by arguing against some of the commonly heard objections against formal methods. We briefly summarise the current state of the art and discuss the most important directions that future research in this field should take. This paper presents a general setting for the other contributions in this issue of the journal, which each deal with a specific issue in more detail.

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KER Journal 1994 Journal Article

A comparison of languages which operationalize and formalize KADS models of expertise

  • Dieter Fensel
  • Frank Van Harmelen

Abstract In the field of knowledge engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the contruction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. To enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications, Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area.

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AIJ Journal 1993 Journal Article

Rippling: A heuristic for guiding inductive proofs

  • Alan Bundy
  • Andrew Stevens
  • Frank Van Harmelen
  • Andrew Ireland
  • Alan Smaill

We describe rippling: a tactic for the heuristic control of the key part of proofs by mathematical induction. This tactic significantly reduces the search for a proof of a wide variety of inductive theorems. We first present a basic version of rippling, followed by various extensions which are necessary to capture larger classes of inductive proofs. Finally, we present a generalised form of rippling which embodies these extensions as special cases. We prove that generalised rippling always terminates, and we discuss the implementation of the tactic and its relation with other inductive proof search heuristics.

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IJCAI Conference 1989 Conference Paper

A Rational Reconstruction and Extension of Recursion Analysis

  • Alan Bundy
  • Frank Van Harmelen
  • JANE HESKETH
  • Alan Smaill
  • Andrew Stevens

The focus of this paper is the technique of recur8\on analysis. Recursion analysis is used by the Boyer-Moore Theorem Prover to choose an appropriate induction schema and variable to prove theorems by mathematical induction. A rational reconstruction of recursion analysis is outlined, using the technique of proof plans. This rational reconstruction suggests an extension of recursion analysis which frees the induction suggestion from the forms of recursion found in the conjecture. Preliminary results are reported of the automation of this rational reconstruction and extension using the CLAM- Oyster system.

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AIJ Journal 1988 Journal Article

Explanation-based generalisation = partial evaluation

  • Frank Van Harmelen
  • Alan Bundy

We argue that explanation-based generalisation as recently proposed in the machine learning literature is essentially equivalent to partial evaluation, a well-known technique in the functional and logic programming literature. We show this equivalence by analysing the definitions and underlying algorithms of both techniques, and by giving a PROLOG program which can be interpreted as doing either explanation-based generalisation or partial evaluation.

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KER Journal 1984 Journal Article

Criteria for Choosing Representation Languages and Control Regimes for Expert Systems

  • Han Reichgelt
  • Frank Van Harmelen

Abstract Shells and high-level programming language environments suffer from a number of shortcomings as knowledge engineering tools. We conclude that a variety of knowledge representation formalisms and a variety of controls regimes are needed. In addition guidelines should be provided about when to choose which knowledge representation formalism and which control regime. The guidelines should be based on properties of the task and the domain of the expert system. In order to arrive at these guidelines we first critically review some of the classifications of expert systems in the literature. We then give our own list of criteria. We test this list applying our criteria to a number of existing expert systems. As a caveat, we have not yet made a systematic attempt at correlating the criteria and different knowledge representations formalisms and control regimes, although we make some preliminary remarks throughout the paper.

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