TY - JOUR A1 - Aa, Han van der A1 - Rebmann, Adrian A1 - Leopold, Henrik T1 - Natural language-based detection of semantic execution anomalies in event logs JF - Information systems : IS ; an international journal ; data bases N2 - Anomaly detection in process mining aims to recognize outlying or unexpected behavior in event logs for purposes such as the removal of noise and identification of conformance violations. Existing techniques for this task are primarily frequency-based, arguing that behavior is anomalous because it is uncommon. However, such techniques ignore the semantics of recorded events and, therefore, do not take the meaning of potential anomalies into consideration. In this work, we overcome this caveat and focus on the detection of anomalies from a semantic perspective, arguing that anomalies can be recognized when process behavior does not make sense. To achieve this, we propose an approach that exploits the natural language associated with events. Our key idea is to detect anomalous process behavior by identifying semantically inconsistent execution patterns. To detect such patterns, we first automatically extract business objects and actions from the textual labels of events. We then compare these against a process-independent knowledge base. By populating this knowledge base with patterns from various kinds of resources, our approach can be used in a range of contexts and domains. We demonstrate the capability of our approach to successfully detect semantic execution anomalies through an evaluation based on a set of real-world and synthetic event logs and show the complementary nature of semantics-based anomaly detection to existing frequency-based techniques. KW - Process mining KW - Natural language processing KW - Anomaly detection Y1 - 2021 U6 - https://doi.org/10.1016/j.is.2021.101824 SN - 0306-4379 SN - 1873-6076 VL - 102 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Baier, Thomas A1 - Di Ciccio, Claudio A1 - Mendling, Jan A1 - Weske, Mathias T1 - Matching events and activities by integrating behavioral aspects and label analysis JF - Software and systems modeling N2 - Nowadays, business processes are increasingly supported by IT services that produce massive amounts of event data during the execution of a process. These event data can be used to analyze the process using process mining techniques to discover the real process, measure conformance to a given process model, or to enhance existing models with performance information. Mapping the produced events to activities of a given process model is essential for conformance checking, annotation and understanding of process mining results. In order to accomplish this mapping with low manual effort, we developed a semi-automatic approach that maps events to activities using insights from behavioral analysis and label analysis. The approach extracts Declare constraints from both the log and the model to build matching constraints to efficiently reduce the number of possible mappings. These mappings are further reduced using techniques from natural language processing, which allow for a matching based on labels and external knowledge sources. The evaluation with synthetic and real-life data demonstrates the effectiveness of the approach and its robustness toward non-conforming execution logs. KW - Process mining KW - Event mapping KW - Business process intelligence KW - Constraint satisfaction KW - Declare KW - Natural language processing Y1 - 2018 U6 - https://doi.org/10.1007/s10270-017-0603-z SN - 1619-1366 SN - 1619-1374 VL - 17 IS - 2 SP - 573 EP - 598 PB - Springer CY - Heidelberg ER - TY - GEN A1 - Han van der, Aa A1 - Di Ciccio, Claudio A1 - Leopold, Henrik A1 - Reijers, Hajo A. T1 - Extracting Declarative Process Models from Natural Language T2 - Advanced Information Systems Engineering (CAISE 2019) N2 - Process models are an important means to capture information on organizational operations and often represent the starting point for process analysis and improvement. Since the manual elicitation and creation of process models is a time-intensive endeavor, a variety of techniques have been developed that automatically derive process models from textual process descriptions. However, these techniques, so far, only focus on the extraction of traditional, imperative process models. The extraction of declarative process models, which allow to effectively capture complex process behavior in a compact fashion, has not been addressed. In this paper we close this gap by presenting the first automated approach for the extraction of declarative process models from natural language. To achieve this, we developed tailored Natural Language Processing techniques that identify activities and their inter-relations from textual constraint descriptions. A quantitative evaluation shows that our approach is able to generate constraints that closely resemble those established by humans. Therefore, our approach provides automated support for an otherwise tedious and complex manual endeavor. KW - Declarative modelling KW - Natural language processing KW - Model extraction Y1 - 2019 SN - 978-3-030-21290-2 SN - 978-3-030-21289-6 U6 - https://doi.org/10.1007/978-3-030-21290-2_23 SN - 0302-9743 SN - 1611-3349 VL - 11483 SP - 365 EP - 382 PB - Springer CY - Cham ER - TY - JOUR A1 - Neves, Mariana A1 - Leser, Ulf T1 - Question answering for Biology JF - Methods : focusing on rapidly developing techniques N2 - Biologists often pose queries to search engines and biological databases to obtain answers related to ongoing experiments. This is known to be a time consuming, and sometimes frustrating, task in which more than one query is posed and many databases are consulted to come to possible answers for a single fact. Question answering comes as an alternative to this process by allowing queries to be posed as questions, by integrating various resources of different nature and by returning an exact answer to the user. We have surveyed the current solutions on question answering for Biology, present an overview on the methods which are usually employed and give insights on how to boost performance of systems in this domain. (C) 2014 Elsevier Inc. All rights reserved. KW - Question answering KW - Biomedicine KW - Natural language processing KW - Data integration Y1 - 2015 U6 - https://doi.org/10.1016/j.ymeth.2014.10.023 SN - 1046-2023 SN - 1095-9130 VL - 74 SP - 36 EP - 46 PB - Elsevier CY - San Diego ER -