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Stefan Borgwardt

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31 papers
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31

KR Conference 2025 Conference Paper

Automated Planning with Ontologies Under Coherence Update Semantics

  • Stefan Borgwardt
  • Duy Nhu
  • Gabriele Röger

Standard automated planning employs first-order formulas under closed-world semantics to achieve a goal with a given set of actions from an initial state. We follow a line of research that aims to incorporate background knowledge into automated planning problems, for example by means of ontologies, which are usually interpreted under open-world semantics. We present a new approach for planning with DL-Lite ontologies that combines the advantages of ontology-based action conditions provided by explicit-input knowledge and action bases (eKABs) and ontology-aware action effects under the coherence update semantics. We show that the complexity of the resulting formalism is not higher than that of previous approaches, and provide an implementation via a polynomial compilation into classical planning. An evaluation on existing and new benchmarks examines the performance of a planning system on different variants of our compilation.

YNIMG Journal 2025 Journal Article

Multimodal personalization of transcranial direct current stimulation for modulation of sensorimotor integration

  • Jan-Ole Radecke
  • Alexander Kühn
  • Tim Erdbrügger
  • Yvonne Buschermöhle
  • Sogand Rashidi
  • Hannah Stöckler
  • Benjamin Sack
  • Stefan Borgwardt

Transcranial direct current stimulation (tDCS) for the modulation of smooth pursuit eye movements provides an ideal model for investigating sensorimotor integration. Within neural networks subserving smooth pursuit, visual area V5 is a core hub where visual motion information is integrated with oculomotor control. Here, we applied personalized tDCS explicitly targeting individual V5 in healthy human participants using algorithmic optimization informed by functional magnetic resonance imaging and combined electro- and magnetoencephalography. We hypothesized subtle modulation of sensorimotor integration during pursuit and assessed the effects of personalized anodal and cathodal tDCS targeting V5 compared to (a) sham stimulation, (b) personalized tDCS targeting the frontal eye field (FEF), and (c) conventional normative tDCS over V5. We found pursuit initiation specifically delayed during personalized cathodal tDCS targeting right V5 suggesting the involvement of distinct functional subregions of V5 in initial sensorimotor integration of visual motion information during pursuit eye movements. Results were extensively controlled by anodal and sham tDCS, different pursuit tasks, and finite-element simulations of individual electric fields. Importantly, in contrast to the two control experiments (personalized tDCS targeting FEF and normative tDCS over V5) personalized tDCS targeting V5 effectively modulated pursuit by adapting electric fields to individual anatomical and functional V5 properties. Our results provide evidence for the ability of personalized tDCS targeting V5 to introduce targeted subtle modulation of sensorimotor integration, specifically during smooth pursuit initiation. Further, our results indicate the potential of personalized tDCS to alter behavior as the main aspect of interest in human neuromodulation.

YNICL Journal 2024 Journal Article

Altered brain complexity in first-episode antipsychotic-naïve patients with schizophrenia: A whole-brain voxel-wise study

  • Naici Liu
  • Rebekka Lencer
  • Christina Andreou
  • Mihai Avram
  • Heinz Handels
  • Wenjing Zhang
  • Sun Hui
  • Chengmin Yang

BACKGROUND: Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding. METHODS: T1-weighted brain images of 150 first-episode antipsychotic-naïve schizophrenia (FES) patients and 161 healthy comparison participants (HC) were examined. The Chaos analysis approach was applied to detect alterations in brain structural complexity using the largest Lyapunov exponent (Lambda) as the key measure. Then, the Lambda spatial series was mapped in the frequency domain using the correlation of the Morlet wavelet to reflect cortical folding complexity. RESULTS: A widespread voxel-wise decrease in Lambda values in space and frequency domains was observed in FES, especially in frontal, parietal, temporal, limbic, basal ganglia, thalamic, and cerebellar regions. The widespread decrease indicates a general loss of brain topological complexity and cortical folding. An additional pattern of increased Lambda values in certain regions highlights the redistribution of complexity measures in schizophrenia at an early stage with potential progression as the illness advances. Strong correlations were found between the duration of untreated psychosis and Lambda values related to the cerebellum, temporal, and occipital gyri. CONCLUSIONS: Our findings support the notion that defining brain complexity by non-linear dynamic analyses offers a novel approach for identifying structural brain alterations related to the early stages of schizophrenia.

KR Conference 2024 System Paper

Explaining Reasoning Results for OWL Ontologies with Evee

  • Christian Alrabbaa
  • Stefan Borgwardt
  • Tom Friese
  • Anke Hirsch
  • Nina Knieriemen
  • Patrick Koopmann
  • Alisa Kovtunova
  • Antonio Krüger

One of the advantages of formalizing domain knowledge in OWL ontologies is that one can use reasoning systems to infer implicit information automatically. However, it is not always straightforward to understand why certain entailments are inferred, and others are not. The popular ontology editor Protégé offers two explanation services to deal with this issue: justifications for OWL 2 DL ontologies, and proofs generated by the reasoner ELK for lightweight OWL 2 EL ontologies. Since justifications are often insufficient for explaining inferences, there is thus only little tool support for more comprehensive explanations in expressive ontology languages, and there is no tool support at all to explain why something was not derived. In this paper, we present Evee, a Java library and a collection of plug-ins for Protégé that offers advanced explanation services for both inferred and missing entailments. Evee explains inferred entailments using proofs in description logics up to ALCH. Missing entailments can be explained using counterexamples and abduction. We evaluated the effectiveness and the interface design of our plug-ins with description logic experts, ontology engineers, and students in two user studies. In these experiments, we were able to not only validate the tool but also gather feedback and insights to improve the existing designs.

AAAI Conference 2022 Conference Paper

Classical Planning with Avoid Conditions

  • Marcel Steinmetz
  • Jörg Hoffmann
  • Alisa Kovtunova
  • Stefan Borgwardt

It is often natural in planning to specify conditions that should be avoided, characterizing dangerous or highly undesirable behavior. PDDL3 supports this with temporal-logic state trajectory constraints. Here we focus on the simpler case where the constraint is a non-temporal formula ϕ – the avoid condition – that must be false throughout the plan. We design techniques tackling such avoid conditions effectively. We show how to learn from search experience which states necessarily lead into ϕ, and we show how to tailor abstractions to recognize that avoiding ϕ will not be possible starting from a given state. We run a large-scale experiment, comparing our techniques against compilation methods and against simple state pruning using ϕ. The results show that our techniques are often superior.

AAAI Conference 2022 Conference Paper

Expressivity of Planning with Horn Description Logic Ontologies

  • Stefan Borgwardt
  • Jörg Hoffmann
  • Alisa Kovtunova
  • Markus Krötzsch
  • Bernhard Nebel
  • Marcel Steinmetz

State constraints in AI Planning globally restrict the legal environment states. Standard planning languages make closeddomain and closed-world assumptions. Here we address openworld state constraints formalized by planning over a description logic (DL) ontology. Previously, this combination of DL and planning has been investigated for the light-weight DL DL-Lite. Here we propose a novel compilation scheme into standard PDDL with derived predicates, which applies to more expressive DLs and is based on the rewritability of DL queries into Datalog with stratified negation. We also provide a new rewritability result for the DL Horn-ALCHOIQ, which allows us to apply our compilation scheme to quite expressive ontologies. In contrast, we show that in the slight extension Horn-SROIQ no such compilation is possible unless the weak exponential hierarchy collapses. Finally, we show that our approach can outperform previous work on existing benchmarks for planning with DL ontologies, and is feasible on new benchmarks taking advantage of more expressive ontologies.

KR Conference 2021 Short Paper

Making DL-Lite Planning Practical

  • Stefan Borgwardt
  • Jörg Hoffmann
  • Alisa Kovtunova
  • Marcel Steinmetz

Planning in the presence of background ontologies is a topic of long-standing interest in AI. It combines the problems of (1) belief update complexity and (2) state-space combinatorics. DL-Lite offers an attractive solution to (1), with belief updates possible at the ABox level. Indeed, it has been shown that DL-Lite planning can be compiled into the commonly used planning language PDDL. Yet that compilation was previously found to be infeasible for off-the-shelf planning systems. Here we analyze the reasons for this problem and find that the bottleneck lies in the planner pre-processes, in particular in the naïve DNF transformations used to compile the PDDL input into the planners' internal representations. Consequently, we design a PDDL pre-compiler realizing a polynomial DNF transformation. We leverage a particular PDDL language feature ("derived predicates") to avoid the need for excessive control structure. Our pre-compiler turns out to be quite effective: the previous bottleneck disappears, and experiments on a broad range of benchmarks demonstrate the first practical technology for DL-Lite planning.

YNICL Journal 2021 Journal Article

Neural mapping of anhedonia across psychiatric diagnoses: A transdiagnostic neuroimaging analysis

  • Anna-Chiara Schaub
  • Matthias Kirschner
  • Nina Schweinfurth
  • Laura Mählmann
  • Cedric Kettelhack
  • Etna E. Engeli
  • Jessica P.K. Doll
  • Stefan Borgwardt

Anhedonia has been associated with abnormal reward-related striatal dopamine functioning in patients with different psychiatric disorders. Here, we tested whether anhedonia expression mapped onto striatal volume across several psychiatric diagnoses. T1-weighted images from 313 participants including 89 healthy controls (HC), 22 patients with opioid use disorder (OUD), 50 patients with major depressive disorder (MDD), 45 patients with borderline personality disorder (BPD), 49 patients with first-episode psychosis (FEP), 43 patients with cocaine use disorder (CUD) and 15 patients with schizophrenia (SZ) were included. Anhedonia was assessed with subscores of the Beck Depression Inventory (BDI) and/or the Scale for the Assessment of Negative Symptoms (SANS). Voxel-based morphometry (VBM) was conducted for identifying dimensional symptom-structure associations using region of interest (ROI, dorsal and ventral striatum) and whole-brain analyses, as well as for group comparisons of striatal volume. ROI analyses revealed significant negative relationships between putamen volume and BDI and SANS anhedonia scores across OUD, MDD, BPD, CUD and SZ patients (n = 175) and MDD, FEP and SZ patients (n = 114), respectively. Whole-brain VBM analyses confirmed these associations and further showed negative relationships between anhedonia severity and volume of the bilateral cerebellum. There were group differences in right accumbens volume, which however were not related to anhedonia expression across the different diagnoses. Our findings indicate volumetric abnormalities in the putamen and cerebellum as a common neural substrate of anhedonia severity that cut across psychiatric entities.

LPAR Conference 2020 Conference Paper

Finding Small Proofs for Description Logic Entailments: Theory and Practice

  • Christian Alrabbaa
  • Franz Baader
  • Stefan Borgwardt
  • Patrick Koopmann
  • Alisa Kovtunova

Logic-based approaches to AI have the advantage that their behaviour can in principle be explained by providing their users with proofs for the derived consequences. However, if such proofs get very large, then it may be hard to understand a consequence even if the individual derivation steps are easy to comprehend. This motivates our interest in finding small proofs for Description Logic (DL) entailments. Instead of concentrating on a specific DL and proof calculus for this DL, we introduce a general framework in which proofs are represented as labeled, directed hypergraphs, where each hyperedge corresponds to a single sound derivation step. On the theoretical side, we investigate the complexity of deciding whether a certain consequence has a proof of size at most n along the following orthogonal dimensions: (i) the underlying proof system is polynomial or exponential; (ii) proofs may or may not reuse already derived consequences; and (iii) the number n is represented in unary or binary. We have determined the exact worst-case complexity of this decision problem for all but one of the possible combinations of these options. On the practical side, we have developed and implemented an approach for generating proofs for expressive DLs based on a non-standard reasoning task called forgetting. We have evaluated this approach on a set of realistic ontologies and compared the obtained proofs with proofs generated by the DL reasoner ELK, finding that forgetting-based proofs are often better w. r. t. different measures of proof complexity.

YNIMG Journal 2020 Journal Article

Increased power by harmonizing structural MRI site differences with the ComBat batch adjustment method in ENIGMA

  • Joaquim Radua
  • Eduard Vieta
  • Russell Shinohara
  • Peter Kochunov
  • Yann Quidé
  • Melissa J. Green
  • Cynthia S. Weickert
  • Thomas Weickert

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

JELIA Conference 2019 Conference Paper

Closed-World Semantics for Conjunctive Queries with Negation over ELH _\bot Ontologies

  • Stefan Borgwardt
  • Walter Forkel

Abstract Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. We propose a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic, which is based on the minimal canonical model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity.

IJCAI Conference 2019 Conference Paper

Closed-World Semantics for Conjunctive Queries with Negation over ELH-bottom Ontologies

  • Stefan Borgwardt
  • Walter Forkel

Ontology-mediated query answering is a popular paradigm for enriching answers to user queries with background knowledge. For querying the absence of information, however, there exist only few ontology-based approaches. Moreover, these proposals conflate the closed-domain and closed-world assumption, and therefore are not suited to deal with the anonymous objects that are common in ontological reasoning. We propose a new closed-world semantics for answering conjunctive queries with negation over ontologies formulated in the description logic ELH-bottom, based on the minimal canonical model. We propose a rewriting strategy for dealing with negated query atoms, which shows that query answering is possible in polynomial time in data complexity.

AAAI Conference 2019 Conference Paper

Ontology-Mediated Query Answering over Log-Linear Probabilistic Data

  • Stefan Borgwardt
  • İsmail İlkan Ceylan
  • Thomas Lukasiewicz

Large-scale knowledge bases are at the heart of modern information systems. Their knowledge is inherently uncertain, and hence they are often materialized as probabilistic databases. However, probabilistic database management systems typically lack the capability to incorporate implicit background knowledge and, consequently, fail to capture some intuitive query answers. Ontology-mediated query answering is a popular paradigm for encoding commonsense knowledge, which can provide more complete answers to user queries. We propose a new data model that integrates the paradigm of ontology-mediated query answering with probabilistic databases, employing a log-linear probability model. We compare our approach to existing proposals, and provide supporting computational results.

YNICL Journal 2018 Journal Article

Altered network hub connectivity after acute LSD administration

  • Felix Müller
  • Patrick C. Dolder
  • André Schmidt
  • Matthias E. Liechti
  • Stefan Borgwardt

LSD is an ambiguous substance, said to mimic psychosis and to improve mental health in people suffering from anxiety and depression. Little is known about the neuronal correlates of altered states of consciousness induced by this substance. Limited previous studies indicated profound changes in functional connectivity of resting state networks after the administration of LSD. The current investigation attempts to replicate and extend those findings in an independent sample. In a double-blind, randomized, cross-over study, 100 μg LSD and placebo were orally administered to 20 healthy participants. Resting state brain activity was assessed by functional magnetic resonance imaging. Within-network and between-network connectivity measures of ten established resting state networks were compared between drug conditions. Complementary analysis were conducted using resting state networks as sources in seed-to-voxel analyses. Acute LSD administration significantly decreased functional connectivity within visual, sensorimotor and auditory networks and the default mode network. While between-network connectivity was widely increased and all investigated networks were affected to some extent, seed-to-voxel analyses consistently indicated increased connectivity between networks and subcortical (thalamus, striatum) and cortical (precuneus, anterior cingulate cortex) hub structures. These latter observations are consistent with findings on the importance of hubs in psychopathological states, especially in psychosis, and could underlay therapeutic effects of hallucinogens as proposed by a recent model.

IJCAI Conference 2018 Conference Paper

Recent Advances in Querying Probabilistic Knowledge Bases

  • Stefan Borgwardt
  • İsmail İlkan Ceylan
  • Thomas Lukasiewicz

We give a survey on recent advances at the forefront of research on probabilistic knowledge bases for representing and querying large-scale automatically extracted data. We concentrate especially on increasing the semantic expressivity of formalisms for representing and querying probabilistic knowledge (i) by giving up the closed-world assumption, (ii) by allowing for commonsense knowledge (and in parallel giving up the tuple-independence assumption), and (iii) by giving up the closed-domain assumption, while preserving some computational properties of query answering in such formalisms.

IJCAI Conference 2017 Conference Paper

Most Probable Explanations for Probabilistic Database Queries

  • İsmail İlkan Ceylan
  • Stefan Borgwardt
  • Thomas Lukasiewicz

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

AAAI Conference 2017 Conference Paper

Ontology-Mediated Queries for Probabilistic Databases

  • Stefan Borgwardt
  • Ismail Ceylan
  • Thomas Lukasiewicz

Probabilistic databases (PDBs) are usually incomplete, e. g. , containing only the facts that have been extracted from the Web with high confidence. However, missing facts are often treated as being false, which leads to unintuitive results when querying PDBs. Recently, open-world probabilistic databases (OpenPDBs) were proposed to address this issue by allowing probabilities of unknown facts to take any value from a fixed probability interval. In this paper, we extend Open- PDBs by Datalog± ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before. We show that the dichotomy between P and PP in (Open)PDBs can be lifted to the case of first-order rewritable positive programs (without negative constraints); and that the problem can become NPPP -complete, once negative constraints are allowed. We also propose an approximating semantics that circumvents the increase in complexity caused by negative constraints.

IJCAI Conference 2017 Conference Paper

Query Rewriting for DL-Lite with n-ary Concrete Domains

  • Franz Baader
  • Stefan Borgwardt
  • Marcel Lippmann

We investigate ontology-based query answering (OBQA) in a setting where both the ontology and the query can refer to concrete values such as numbers and strings. In contrast to previous work on this topic, the built-in predicates used to compare values are not restricted to being unary. We introduce restrictions on these predicates and on the ontology language that allow us to reduce OBQA to query answering in databases using the so-called combined rewriting approach. Though at first sight our restrictions are different from the ones used in previous work, we show that our results strictly subsume some of the existing first-order rewritability results for unary predicates.

IJCAI Conference 2016 Conference Paper

Preferential Query Answering over the Semantic Web with Possibilistic Networks

  • Stefan Borgwardt
  • Bettina Fazzinga
  • Thomas Lukasiewicz
  • Akanksha Shrivastava
  • Oana Tifrea-Marciuska

In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs). We call these combinations ontological possibilistic networks (OP-nets). We define skyline and k-rank answers to CQs under preferences and provide complexity (including data tractability) results for deciding consistency and CQ skyline membership for OP-nets. We show that our formalism has a lower complexity than a similar existing formalism.

YNICL Journal 2015 Journal Article

Classifying individuals at high-risk for psychosis based on functional brain activity during working memory processing

  • Kerstin Bendfeldt
  • Renata Smieskova
  • Nikolaos Koutsouleris
  • Stefan Klöppel
  • André Schmidt
  • Anna Walter
  • Fabienne Harrisberger
  • Johannes Wrege

The psychosis high-risk state is accompanied by alterations in functional brain activity during working memory processing. We used binary automatic pattern-classification to discriminate between the at-risk mental state (ARMS), first episode psychosis (FEP) and healthy controls (HCs) based on n-back WM-induced brain activity. Linear support vector machines and leave-one-out-cross-validation were applied to fMRI data of matched ARMS, FEP and HC (19 subjects/group). The HC and ARMS were correctly classified, with an accuracy of 76.2% (sensitivity 89.5%, specificity 63.2%, p = 0.01) using a verbal working memory network mask. Only 50% and 47.4% of individuals were classified correctly for HC vs. FEP (p = 0.46) or ARMS vs. FEP (p = 0.62), respectively. Without mask, accuracy was 65.8% for HC vs. ARMS (p = 0.03) and 65.8% for HC vs. FEP (p = 0.0047), and 57.9% for ARMS vs. FEP (p = 0.18). Regions in the medial frontal, paracingulate, cingulate, inferior frontal and superior frontal gyri, inferior and superior parietal lobules, and precuneus were particularly important for group separation. These results suggest that FEP and HC or FEP and ARMS cannot be accurately separated in small samples under these conditions. However, ARMS can be identified with very high sensitivity in comparison to HC. This might aid classification and help to predict transition in the ARMS.

IJCAI Conference 2015 Conference Paper

Temporal Query Answering in the Description Logic EL

  • Stefan Borgwardt
  • Veronika Thost

Context-aware systems use data collected at runtime to recognize certain predefined situations and trigger adaptations. This can be implemented using ontology-based data access (OBDA), which augments classical query answering in databases by adopting the open-world assumption and including domain knowledge provided by an ontology. We investigate temporalized OBDA w. r. t. ontologies formulated in EL, a description logic that allows for efficient reasoning and is successfully used in practice. We consider a recently proposed temporalized query language that combines conjunctive queries with the operators of propositional linear temporal logic (LTL), and study both data and combined complexity of query entailment in this setting. We also analyze the satisfiability problem in the similar formalism EL-LTL.

IJCAI Conference 2015 Conference Paper

The Complexity of Subsumption in Fuzzy EL

  • Stefan Borgwardt
  • Marco Cerami
  • Rafael Pe
  • ntilde; aloza

Fuzzy Description Logics (DLs) are used to represent and reason about vague and imprecise knowledge that is inherent to many application domains. It was recently shown that the complexity of reasoning in finitely valued fuzzy DLs is often not higher than that of the underlying classical DL. We show that this does not hold for fuzzy extensions of the light-weight DL EL, which is used in many biomedical ontologies, under the Łukasiewicz semantics. The complexity of reasoning increases from PTIME to EXPTIME, even if only one additional truth value is introduced. The same lower bound holds also for infinitely valued Łukasiewicz extensions of EL.

AIJ Journal 2015 Journal Article

The limits of decidability in fuzzy description logics with general concept inclusions

  • Stefan Borgwardt
  • Felix Distel
  • Rafael Peñaloza

Fuzzy description logics (DLs) can be used to represent and reason with vague knowledge. This family of logical formalisms is very diverse, each member being characterized by a specific choice of constructors, axioms, and triangular norms, which are used to specify the semantics. Unfortunately, it has recently been shown that the consistency problem in many fuzzy DLs with general concept inclusion axioms is undecidable. In this paper, we present a proof framework that allows us to extend these results to cover large classes of fuzzy DLs. On the other hand, we also provide matching decidability results for most of the remaining logics. As a result, we obtain a near-universal classification of fuzzy DLs according to the decidability of their consistency problem.

KR Conference 2014 Conference Paper

Decidable Gödel Description Logics without the Finitely-Valued Model Property

  • Stefan Borgwardt
  • Felix Distel
  • Rafael Penaloza

infima and suprema of truth values, respectively. To avoid issues arising from having infinitely many truth values, reasoning in fuzzy DLs is usually restricted to so-called witnessed models (Hájek 2007). The study of fuzzy DLs underwent a large change in recent years, after some relatively inexpressive fuzzy DLs were shown to be undecidable when reasoning w. r. t. general ontologies (Baader and Peñaloza 2011a; 2011b; Cerami and Straccia 2013). Since then, the limits of decidability have been explored, yielding very expressive decidable logics on the one hand (Borgwardt, Distel, and Peñaloza 2012), and inexpressive undecidable logics on the other (Borgwardt and Peñaloza 2012). Despite being widely regarded as the simplest t-norm, surprisingly little is known about fuzzy DLs based on Gödel semantics. It is generally believed that—at least w. r. t. witnessed models—these logics are decidable, but no proof exists to support this claim. The only results for similar logics restrict reasoning a priori to a finite subset of [0, 1]; in this case, a reduction to classical reasoning then yields decidability (Bobillo et al. 2009; 2012). All existing approaches for reasoning in fuzzy DLs depend on limiting models to use only finitely many different truth degrees. Indeed, for these approaches to work, one must either (i) restrict the semantics to a finite set of truth degrees (Bobillo et al. 2009; 2012; Bobillo and Straccia 2011; 2013; Borgwardt and Peñaloza 2013a; 2013b; Straccia 2006); (ii) prove that reasoning can be restricted to a finite set of degrees (Bobillo, Delgado, and Gómez-Romero 2008; Borgwardt, Distel, and Peñaloza 2012; Straccia 2001); or (iii) prove that models can be built from a finite pattern (Stoilos et al. 2007; Straccia and Bobillo 2007). In all three cases, the proofs of correctness of these algorithms imply the finitely-valued model property: an ontology has a model iff it has a model using only finitely many truth values. Conversely, the proofs of undecidability (Baader and Peñaloza 2011a; 2011b; Borgwardt and Peñaloza 2012; Cerami and Straccia 2013) construct a model that uses infinitely many truth degrees. Thus, this finitely-valued model property appears to be a good indicator of the decidability of a fuzzy DL. In this paper we study the standard reasoning problems for the DL G-IALC, a fuzzy extension of ALC based on the Gödel semantics w. r. t. witnessed models. First, we show that In the last few years, there has been a large effort for analyzing the computational properties of reasoning in fuzzy description logics. This has led to a number of papers studying the complexity of these logics, depending on the chosen semantics. Surprisingly, despite being arguably the simplest form of fuzzy semantics, not much is known about the complexity of reasoning in fuzzy description logics w. r. t. witnessed models over the Gödel t-norm. We show that in the logic G-IALC, reasoning cannot be restricted to finitelyvalued models in general. Despite this negative result, we also show that all the standard reasoning problems can be solved in exponential time, matching the complexity of reasoning in classical ALC.

JELIA Conference 2014 Conference Paper

The Fuzzy Description Logic $\mathsf{G}\text{-}{\mathcal{F\! L}_0} $ with Greatest Fixed-Point Semantics

  • Stefan Borgwardt
  • José A. Leyva Galano
  • Rafael Peñaloza

Abstract We study the fuzzy extension of the Description Logic \(\mathcal{F\! L}_0\) with semantics based on the Gödel t-norm. We show that subsumption w. r. t. a finite set of primitive definitions, using greatest fixed-point semantics, can be characterized by a relation on weighted automata. We use this result to provide tight complexity bounds for reasoning in this logic, showing that it is PSpace -complete. If the definitions do not contain cycles, subsumption becomes co-NP -complete.

YNIMG Journal 2013 Journal Article

Abnormal effective connectivity in the psychosis high-risk state

  • André Schmidt
  • Stefan Borgwardt

In a recently published fMRI study (Dauvermann et al. , 2013), nonlinear dynamic causal modeling (DCM) was used to examine condition-specific effective connectivity in subjects at high genetic risk of schizophrenia. The authors concluded that nonlinear DCM could lead to new insights in the development of psychotic symptoms and functional and effective dysconnection at the network level in subjects at high familial risk. In this paper, we place these interesting findings in the context of recent evidence from bilinear DCM studies in subjects at high clinical risk with an at-risk mental state (ARMS) for psychosis by considering their consistency and potential differences with implications for future research in the field of emerging psychosis.

IJCAI Conference 2013 Conference Paper

Positive Subsumption in Fuzzy EL with General t-Norms

  • Stefan Borgwardt
  • Rafael Peñaloza

The Description Logic EL is used to formulate several large biomedical ontologies. Fuzzy extensions of EL can express the vagueness inherent in many biomedical concepts. We study the reasoning problem of deciding positive subsumption in fuzzy EL with semantics based on general t-norms. We show that the complexity of this problem depends on the specific t-norm chosen. More precisely, if the t-norm has zero divisors, then the problem is co-NP-hard; otherwise, it can be decided in polynomial time. We also show that the best subsumption degree cannot be computed in polynomial time if the t-norm contains the Łukasiewicz t-norm.

KR Conference 2012 Short Paper

Extending Unification in EL towards General TBoxes

  • Franz Baader
  • Stefan Borgwardt
  • Barbara Morawska

can obviously be made equivalent by treating the concept names Head injury and Severe injury as variables, and substituting the first one by Injury u ∃finding site. Head and the second one by Injury u ∃severity. Severe. In this case, we say that the descriptions are unifiable, and call the substitution that makes them equivalent a unifier. Intuitively, such a unifier proposes definitions for the concept names that are used as variables: in our example, we know that, if we define Head injury as Injury u ∃finding site. Head and Severe injury as Injury u ∃severity. Severe, then the two concept descriptions (1) and (2) are equivalent w. r. t. these definitions. Here equivalence holds without additional GCIs. To motivate our interest in unification w. r. t. GCIs, assume that the second developer uses the description Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. The inexpressive Description Logic EL is of particular interest in this context since, on the one hand, several large biomedical ontologies are defined using EL. On the other hand, unification in EL has recently been shown to be NP-complete, and thus of significantly lower complexity than unification in other DLs of similarly restricted expressive power. However, the unification algorithms for EL developed so far cannot deal with general concept inclusion axioms (GCIs). This paper makes a considerable step towards addressing this problem, but the GCIs our new unification algorithm can deal with still need to satisfy a certain cycle restriction. 1 ∃status. Emergency u ∃finding. (Severe injury u ∃finding site. Head)

LPAR Conference 2012 Conference Paper

Finding Finite Herbrand Models

  • Stefan Borgwardt
  • Barbara Morawska 0001

Abstract We show that finding finite Herbrand models for a restricted class of first-order clauses is ExpTime -complete. A Herbrand model is called finite if it interprets all predicates by finite subsets of the Herbrand universe. The restricted class of clauses consists of anti-Horn clauses with monadic predicates and terms constructed over unary function symbols and constants. The decision procedure can be used as a new goal-oriented algorithm to solve linear language equations and unification problems in the description logic \(\mathcal{FL}_0\). The new algorithm has only worst-case exponential runtime, in contrast to the previous one which was even best-case exponential.

KR Conference 2012 Conference Paper

Undecidability of Fuzzy Description Logics

  • Stefan Borgwardt
  • Rafael Peñaloza

Armengol, and Esteva 2010) for a survey). In fact, fuzzy DLs have several degrees of freedom for defining their expressiveness. In addition to the choice of concept constructors (such as conjunction u or existential restriction ∃), and the type of axioms allowed (like acyclic concept definitions or general concept inclusions), one must also decide how to interpret the different constructors, through a choice of functions over the domain of fuzzy values [0, 1]. These functions are typically determined by a continuous t-norm (like Gödel, Łukasiewicz, or product) that interprets conjunction; there exist uncountably many such t-norms, each with different properties. For example, under the product t-norm semantics, existential- (∃) and value-restrictions (∀) are not interdefinable, while under the Łukasiewicz t-norm they are. Even after fixing the t-norm, one can choose whether to interpret negation by the involutive negation operator, or using the residual negation. An additional level of liberty comes from selecting the class of models over which reasoning is considered: either all models, or so-called witnessed models only (Hájek 2005). Most existing reasoning algorithms have been developed for the Gödel semantics, either by a reduction to crisp reasoning (Straccia 2001; Bobillo et al. 2009), or by a simple adaptation of the known algorithms for crisp DLs (Stoilos et al. 2005; 2006; Tresp and Molitor 1998). However, methods based on other t-norms have also been explored (Bobillo and Straccia 2007; 2008; 2009; Straccia and Bobillo 2007; Stoilos and Stamou 2009). Usually, these algorithms reason w. r. t. witnessed models. 3 Very recently, it was shown that the tableaux-based algorithms for logics with semantics based on t-norms other than the Gödel t-norm and allowing general concept inclusions were incorrect (Baader and Peñaloza 2011a; Bobillo, Bou, and Straccia 2011). This raised doubts about the decidability of these logics, and eventually led to a series of undecidability results for fuzzy DLs (Baader and Peñaloza 2011a; 2011b; 2011c; Cerami and Straccia 2011). All these papers, except (Baader and Peñaloza 2011c), focus on one specific fuzzy DL; that is, undecidability is proven for a specific set of constructors, axioms, and underlying semantics. A small generalization is made in (Baader and Peñaloza Fuzzy description logics (DLs) have been investigated for over two decades, due to their capacity to formalize and reason with imprecise concepts. Very recently, it has been shown that for several fuzzy DLs, reasoning becomes undecidable. Although the proofs of these results differ in the details of each specific logic considered, they are all based on the same basic idea. In this paper, we formalize this idea and provide sufficient conditions for proving undecidability of a fuzzy DL. We demonstrate the effectiveness of our approach by strengthening all previously-known undecidability results and providing new ones. In particular, we show that undecidability may arise even if only crisp axioms are considered.

IJCAI Conference 2011 Conference Paper

Description Logics over Lattices with Multi-Valued Ontologies

  • Stefan Borgwardt
  • Rafael Pe
  • ntilde; aloza

Uncertainty is unavoidable when modeling most application domains. In medicine, for example, symptoms (such as pain, dizziness, or nausea) are always subjective, and hence imprecise and incomparable. Additionally, concepts and their relationships may be inexpressible in a crisp, clear-cut manner. We extend the description logic ALC with multi-valued semantics based on lattices that can handle uncertainty on concepts as well as on the axioms of the ontology. We introduce reasoning methods for this logic w. r. t. general concept inclusions and show that the complexity of reasoning is not increased by this new semantics.

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