Business process simulation (BPS) is a versatile technique for estimating process performance across various scenarios. Traditionally, BPS approaches employ a control-flow-first perspective by enriching a process model with simulation parameters. Although such approaches can mimic the behavior of centrally orchestrated processes, such as those supported by workflow systems, current control-flow-first approaches cannot faithfully capture the dynamics of real-world processes that involve distinct resource behavior and decentralized decision-making. Recognizing this issue, this paper introduces AgentSimulator, a resource-first BPS approach that discovers a multi-agent system from an event log, modeling distinct resource behaviors and interaction patterns to simulate the underlying process. Our experiments show that AgentSimulator achieves state-of-the-art simulation accuracy with significantly lower computation times than existing approaches while providing high interpretability and adaptability to different types of process-execution scenarios.

Abstract Knowledge graph completion is the task of predicting correct facts that can be expressed by the vocabulary of a given knowledge graph, which are not explicitly stated in that graph. Broadly, there are two main approaches for solving the knowledge graph completion problem. Sub-symbolic approaches embed the nodes and/or edges of a given graph into a low-dimensional vector space and use a scoring function to determine the plausibility of a given fact. Symbolic approaches learn a model that remains within the primary representation of the given knowledge graph. Rule-based approaches are well-known examples. One such approach is AnyBURL. It works by sampling random paths, which are generalized into Horn rules. Previously published results show that the prediction quality of AnyBURL is close to current state of the art with the additional benefit of offering an explanation for a predicted fact. In this paper, we propose several improvements and extensions of AnyBURL. In particular, we focus on AnyBURL’s capability to be successfully applied to large and very large datasets. Overall, we propose four separate extensions: (i) We add to each rule a set of pairwise inequality constraints which enforces that different variables cannot be grounded by the same entities, which results into more appropriate confidence estimations. (ii) We introduce reinforcement learning to guide path sampling in order to use available computational resources more efficiently. (iii) We propose an efficient sampling strategy to approximate the confidence of a rule instead of computing its exact value. (iv) We develop a new multithreaded AnyBURL, which incorporates all previously mentioned modifications. In an experimental study, we show that our approach outperforms both symbolic and sub-symbolic approaches in large-scale knowledge graph completion. It has a higher prediction quality and requires significantly less time and computational resources.

Rule-based approaches have proven to be an efficient and explainable method for knowledge base completion. Their predictive quality is on par with classic knowledge graph embedding models such as TransE or ComplEx, however, they cannot achieve the results of neural models proposed recently. The performance of a rule-based approach depends crucially on the solution of the rule aggregation problem, which is concerned with the computation of a score for a prediction that is generated by several rules. Within this paper, we propose a supervised approach to learn a reweighted confidence value for each rule to get an optimal explanation for the training set given a specific aggregation function. In particular, we apply our approach to two aggregation functions: We learn weights for a noisy-or multiplication and apply logistic regression, which computes the score of a prediction as a sum of these weights. Due to the simplicity of both models the final score is fully explainable. Our experimental results show that we can significantly improve the predictive quality of a rule-based approach. We compare our method with current state-of-the-art latent models that lack explainability, and achieve promising results.

How much does a CEO’s personality impact the performanceof their company? Management theory posits a great influence, but it is difficult to show empirically—there is a lack of publicly available self-reported personality data of top managers. Instead, we propose a text-based personality regressor based on crowd-sourced Myers–Briggs Type Indicator (MBTI) assessments. The ratings have a high internal and external validity and can be predicted with moderate to strong correlations for three out of four dimensions. Providing evidence for the upper echelons theory, we demonstrate that the predicted CEO personalities have explanatory power of financial risk.

Recognition of activities of daily living (ADLs) is an enabling technology for several ubiquitous computing applications. Most activity recognition systems rely on supervised learning to extract activity models from labeled datasets. A problem with that approach is the acquisition of comprehensive activity datasets, which is an expensive task. The problem is particularly challenging when focusing on complex ADLs characterized by large variability of execution. Moreover, several activity recognition systems are limited to offline recognition, while many applications claim for online activity recognition. In this paper, we propose POLARIS, a framework for unsupervised activity recognition. POLARIS can recognize complex ADLs exploiting the semantics of activities, context data, and sensors. Through ontological reasoning, our algorithm derives semantic correlations among activities and sensor events. By matching observed events with semantic correlations, a statistical reasoner formulates initial hypotheses about the occurred activities. Those hypotheses are refined through probabilistic reasoning, exploiting semantic constraints derived from the ontology. Our system supports online recognition, thanks to a novel segmentation algorithm. Extensive experiments with real-world datasets show that the accuracy of our unsupervised method is comparable to the one of supervised approaches. Moreover, the online version of our system achieves essentially the same accuracy of the offline version.

We study the problem of explaining differences between two snapshots of the same database table including record insertions, deletions and in particular record updates. Unlike existing alternatives, our solution induces transformation functions and does not require knowledge of the correct alignment between the record sets. This allows profiling snapshots of tables with unspecified or modified primary keys. In such a problem setting, there are always multiple explanations for the differences. Our goal is to find the simplest explanation. We propose to measure the complexity of explanations on the basis of minimum description length in order to formulate the task as an optimization problem. We show that the problem is NP-hard and propose a heuristic search algorithm to solve practical problem instances. We implement a prototype called Affidavit to assess the explanatory qualities of our approach in experiments based on different real-world data sets. We show that it can scale to both a large number of records and attributes and is able to reliably provide correct explanations under practical levels of modifications.

Several probabilistic extensions of description logic languages have been proposed and thoroughly studied. However, their practical use has been hampered by intractability of various reasoning tasks. While present-day knowledge bases (KBs) contain millions of instances and thousands of axioms, most state-of-the-art reasoners are capable of handling small scale KBs with thousands of instances. Thus, recent research has focused on leveraging the structure of KBs and queries in order to speed up inference runtime. However, these efforts have not been satisfactory in providing reasoners that are suitable for practical use in large scale KBs. In this study, we aim to tackle this challenging problem. In doing so, we use a probabilistic extension of OWL RL (called PRORL) as a modeling language and exploit graph neighborhoods (of undirected graphical models) for efficient approximate probabilistic inference. We show that subgraph extraction based inference is much faster and has comparable accuracy to full graph inference. We perform several experiments, in order to support our claim, over a NELL KB containing millions of instances and thousands of axioms. Furthermore, we propose a novel graph-based algorithm to automatically partition inferences rules based on their structure for efficient parallel inference.

There is an ever increasing number of rule learning algorithms and tools for automatic knowledge base (KB) construction. These tools often produce weighted rules and facts that make up a probabilistic KB (PKB). In such a PKB, probabilistic inference is used in order to perform marginal inference, consistency checking and other tasks. However, in general, inference is known to be intractable. Hence, recently, there are a number of studies aimed at lifting (making tractable or approximating) inference by exploiting symmetries in the structure of a PKB. These studies alleviate grounding entirely a given PKB which can generate a sizable factor graph for inference (e.g. to compute the probability of a query). In line with this, we propose a novel technique to automatically partition rules based on their structure for efficient parallel grounding. In addition, we perform query expansion so as to generate a factor graph small enough to be used for efficient probability computation. We present a novel approximate marginal inference algorithm that uses N-hop subgraph extraction and query expansion. Moreover, we show that our system is much faster than state-of-the-art systems.

The management of uncertainty is crucial when harvesting structured content from unstructured and noisy sources. Knowledge Graphs ( kg s), maintaining both numerical and non-numerical facts supported by an underlying schema, are a prominent example. Knowledge Graph management is challenging because: (i) most of existing kg s focus on static data, thus impeding the availability of timewise knowledge; (ii) facts in kg s are usually accompanied by a confidence score, which witnesses how likely it is for them to hold. We demonstrate T e C o R e , a system for temporal inference and conflict resolution in uncertain temporal knowledge graphs ( utkg s). At the heart of T e C o R e are two state-of-the-art probabilistic reasoners that are able to deal with temporal constraints efficiently. While one is scalable, the other can cope with more expressive constraints. The demonstration will focus on enabling users and applications to find inconsistencies in utkg s. T e C o R e provides an interface allowing to select utkg s and editing constraints; shows the maximal consistent subset of the utkg , and displays statistics (e.g., number of noisy facts removed) about the debugging process.

This paper introduces the Protégé plugin uDecide. With the help of uDecide it is possible to solve multi-attribute decision making problems encoded in a straight forward extension of standard Description Logics. The formalism allows to specify background knowledge in terms of an ontology, while each attribute is represented as a weighted class expression. On top of such an approach one can compute the best choice (or the best k-choices) taking background knowledge into account in the appropriate way. We show how to implement the approach on top of existing semantic web technologies and demonstrate its benefits with the help of an interesting use case that illustrates how to convert an existing web resource into an expert system with the help of uDecide.

We propose an approach for semantifying web extracted facts. In particular, we map subject and object terms of these facts to instances; and relational phrases to object properties defined in a target knowledge base. By doing this we resolve the ambiguity inherent in the web extracted facts, while simultaneously enriching the target knowledge base with a significant number of new assertions. In this paper, we focus on the mapping of the relational phrases in the context of the overall work ow. Furthermore, in an open extraction setting identical semantic relationships can be represented by different surface forms, making it necessary to group these surface forms together. To solve this problem we propose the use of markov clustering. In this work we present a complete, ontology independent, generalized workflow which we evaluate on facts extracted by Nell and Reverb. Our target knowledge base is DBpedia. Our evaluation shows promising results in terms of producing highly precise facts. Moreover, the results indicate that the clustering of relational phrases pays of in terms of an improved instance and property mapping.

The benefits of modular ontologies in terms of easier creation and maintenance as well as better computational properties have been recognized by different researchers. As most real world ontologies, however, are still designed in a monolithic way, there is a need for methods that partition an existing ontology into a set of modules. Currently, existing work suffers from the fact that the notion of modularization is not as well understood in the context of ontologies as it is in software engineering. In this paper we present a flexible partitioning tool for large ontologies that can be adapted to the needs of different applications based on criteria that the resulting modular ontology should satisfy.

The use of description logics as one of the primary logical languages for knowledge representation on the Web has created new challenges with respect to reasoning in these logics. In order to support the vision of a semantic Web of interrelated ontologies, reasoning procedures have to be highly scalable and able to deal with physically distributed knowledge models. A natural way of addressing these problems is to rely on distributed inference procedures that can distribute the load between different solvers, thus reducing potential bottlenecks both in terms of memory and computation time. In this paper, we propose a distributed resolution approach that solves the problem by local resolution and propagation of derived axioms between different reasoners. The method is complete for first order logic, terminates for ALC ontologies and avoids duplication of axioms and inferences. The work can be seen as a building block for a large scale distributed reasoning infrastructure for the semantic Web as envisioned in recent activities such as the large knowledge collider (LarKC) project.

The use of thesaurus-based indexing is a common approach for increasing the performance of document retrieval. With the growing amount of documents available, manual indexing is not a feasible option. Statistical methods for automated document indexing are an attractive alternative. We argue that the quality of the thesaurus used as a basis for indexing in regard to its ability to adequately cover the contents to be indexed is of crucial importance inautomatic indexing because there is no human in the loop that can spot and avoid indexing errors. We propose a method for thesaurus evaluation that is based on a combination of statistical measures and appropriate visualization techniques that supports the detection of potential problems in a thesaurus. We describe this method and show its application in the context of two automatic indexing tasks. The examples show that the methods indeed eases the detection and correction of errors leading to a better indexing result. Please refer to http://www.kaiec.org for high resolution media of all figures used in this paper, as well as an animated presentation of the interactive tool.

No abstract available.

It is widely agreed that the semantic Web will be build on RDF. Therefore the success of the semantic Web also depends on the availability of a scalable and reliable infrastructure for storing and accessing RDF data. A number of storage and retrieval systems have been developed recently, but despite the inherently distributed nature of the semantic Web most of these systems do not support distributed storage and retrieval of data. In this paper we report our experiences with implementing a distributed storage and query infrastructure on top of an existing RDF infrastructure. We present the system architecture and discuss performance issues based on a set of experiments with the infrastructure. We conclude with an identification of remaining performance bottlenecks and point to further improvements.

A technical infrastructure for storing, querying and managing RDFdata is a key element in the current semantic web development. Systems like Jena, Sesame or the ICS-FORTH RDF Suite are widelyused for building semantic web applications. Currently, none ofthese systems supports the integrated querying of distributed RDF repositories. We consider this a major shortcoming since the semanticweb is distributed by nature. In this paper we present an architecture for querying distributed RDF repositories by extending the existing Sesame system. We discuss the implications of our architectureand propose an index structure as well as algorithms forquery processing and optimization in such a distributed context.

Content-related metadata plays an important role in intelligent information systems. Especially on the world-wide web meaningful metadata describing the contents of a web-site is the key to intelligent retrieval and access of information. Metadata description standards like RDF and RDF schema have been developed and work in progress addresses the use of ontologies to provide a logical foundation for metadata. However, the acquisition of appropriate metadata is still a problem. The main part of the paper is concerned with the specification of ontologies and metadata models. We describe the Spectacle approach, a knowledge-based approach for metadata validation and generation as well as tools related to the ontology language OIL. We conclude that the specification of ontologies and the generation of metadata models are processes that supplement each other and propose a method for semi-automatic generation of metadata models on the basis of ontologies.