@article{NevesLeser2015, author = {Neves, Mariana and Leser, Ulf}, title = {Question answering for Biology}, series = {Methods : focusing on rapidly developing techniques}, volume = {74}, journal = {Methods : focusing on rapidly developing techniques}, publisher = {Elsevier}, address = {San Diego}, issn = {1046-2023}, doi = {10.1016/j.ymeth.2014.10.023}, pages = {36 -- 46}, year = {2015}, abstract = {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.}, language = {en} } @article{AaRebmannLeopold2021, author = {Aa, Han van der and Rebmann, Adrian and Leopold, Henrik}, title = {Natural language-based detection of semantic execution anomalies in event logs}, series = {Information systems : IS ; an international journal ; data bases}, volume = {102}, journal = {Information systems : IS ; an international journal ; data bases}, publisher = {Elsevier}, address = {Amsterdam}, issn = {0306-4379}, doi = {10.1016/j.is.2021.101824}, pages = {13}, year = {2021}, abstract = {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.}, language = {en} } @article{BaierDiCiccioMendlingetal.2018, author = {Baier, Thomas and Di Ciccio, Claudio and Mendling, Jan and Weske, Mathias}, title = {Matching events and activities by integrating behavioral aspects and label analysis}, series = {Software and systems modeling}, volume = {17}, journal = {Software and systems modeling}, number = {2}, publisher = {Springer}, address = {Heidelberg}, issn = {1619-1366}, doi = {10.1007/s10270-017-0603-z}, pages = {573 -- 598}, year = {2018}, abstract = {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.}, language = {en} } @article{CoupetteHartungBeckedorfetal.2022, author = {Coupette, Corinna and Hartung, Dirk and Beckedorf, Janis and B{\"o}ther, Maximilian and Katz, Daniel Martin}, title = {Law smells}, series = {Artificial intelligence and law}, volume = {31}, journal = {Artificial intelligence and law}, publisher = {Springer}, address = {Dordrecht}, issn = {0924-8463}, doi = {10.1007/s10506-022-09315-w}, pages = {335 -- 368}, year = {2022}, abstract = {Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples-namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession-, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in practice using the United States Code as a test case. Our work demonstrates how ideas from software engineering can be leveraged to assess and improve the quality of legal code, thus drawing attention to an understudied area in the intersection of law and computer science and highlighting the potential of computational legal drafting.}, language = {en} } @misc{HanvanderDiCiccioLeopoldetal.2019, author = {Han van der, Aa and Di Ciccio, Claudio and Leopold, Henrik and Reijers, Hajo A.}, title = {Extracting Declarative Process Models from Natural Language}, series = {Advanced Information Systems Engineering (CAISE 2019)}, volume = {11483}, journal = {Advanced Information Systems Engineering (CAISE 2019)}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-21290-2}, issn = {0302-9743}, doi = {10.1007/978-3-030-21290-2_23}, pages = {365 -- 382}, year = {2019}, abstract = {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.}, language = {en} }