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Companies often build their businesses based on product information and therefore try to automate the process of information extraction (IE). Since the information source is usually heterogeneous and non-standardized, classic extract, transform, load techniques reach their limits. Hence, companies must implement the newest findings from research to tackle the challenges of process automation. They require a flexible and robust system that is extendable and ensures the optimal processing of the different document types. This paper provides a distributed microservice architecture pattern that enables the automated generation of IE pipelines. Since their optimal design is individual for each input document, the system ensures the ad-hoc generation of pipelines depending on specific document characteristics at runtime. Furthermore, it introduces the automated quality determination of each available pipeline and controls the integration of new microservices based on their impact on the business value. The introduced system enables fast prototyping of the newest approaches from research and supports companies in automating their IE processes. Based on the automated quality determination, it ensures that the generated pipelines always meet defined business requirements when they come into productive use.
Supervised machine learning and deep learning require a large amount of labeled data, which data scientists obtain in a manual, and time-consuming annotation process. To mitigate this challenge, Active Learning (AL) proposes promising data points to annotators they annotate next instead of a subsequent or random sample. This method is supposed to save annotation effort while maintaining model performance.
However, practitioners face many AL strategies for different tasks and need an empirical basis to choose between them. Surveys categorize AL strategies into taxonomies without performance indications. Presentations of novel AL strategies compare the performance to a small subset of strategies. Our contribution addresses the empirical basis by introducing a reproducible active learning evaluation (ALE) framework for the comparative evaluation of AL strategies in NLP.
The framework allows the implementation of AL strategies with low effort and a fair data-driven comparison through defining and tracking experiment parameters (e.g., initial dataset size, number of data points per query step, and the budget). ALE helps practitioners to make more informed decisions, and researchers can focus on developing new, effective AL strategies and deriving best practices for specific use cases. With best practices, practitioners can lower their annotation costs. We present a case study to illustrate how to use the framework.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
Applications of Graph Transformations with Industrial Relevance Lecture Notes in Computer Science, 2004, Volume 3062/2004, 434-439, DOI: http://dx.doi.org/10.1007/978-3-540-25959-6_33 This paper gives a brief overview of the tools we have developed to support conceptual design in civil engineering. Based on the UPGRADE framework, two applications, one for the knowledge engineer and another for architects allow to store domain specific knowledge and to use this knowledge during conceptual design. Consistency analyses check the design against the defined knowledge and inform the architect if rules are violated.
Often, research results from collaboration projects are not transferred into productive environments even though approaches are proven to work in demonstration prototypes. These demonstration prototypes are usually too fragile and error-prone to be transferred
easily into productive environments. A lot of additional work is required.
Inspired by the idea of an incremental delivery process, we introduce an architecture pattern, which combines the approach of Metrics Driven Research Collaboration with microservices for the ease of integration. It enables keeping track of project goals over the course of the collaboration while every party may focus on their expert skills: researchers may focus on complex algorithms,
practitioners may focus on their business goals.
Through the simplified integration (intermediate) research results can be introduced into a productive environment which enables
getting an early user feedback and allows for the early evaluation of different approaches. The practitioners’ business model benefits throughout the full project duration.
In recent years, the development of large pretrained language models, such as BERT and GPT, significantly improved information extraction systems on various tasks, including relation classification. State-of-the-art systems are highly accurate on scientific benchmarks. A lack of explainability is currently a complicating factor in many real-world applications. Comprehensible systems are necessary to prevent biased, counterintuitive, or harmful decisions.
We introduce semantic extents, a concept to analyze decision patterns for the relation classification task. Semantic extents are the most influential parts of texts concerning classification decisions. Our definition allows similar procedures to determine semantic extents for humans and models. We provide an annotation tool and a software framework to determine semantic extents for humans and models conveniently and reproducibly. Comparing both reveals that models tend to learn shortcut patterns from data. These patterns are hard to detect with current interpretability methods, such as input reductions. Our approach can help detect and eliminate spurious decision patterns during model development. Semantic extents can increase the reliability and security of natural language processing systems. Semantic extents are an essential step in enabling applications in critical areas like healthcare or finance. Moreover, our work opens new research directions for developing methods to explain deep learning models.
Messenger apps like WhatsApp or Telegram are an integral part of daily communication. Besides the various positive effects, those services extend the operating range of criminals. Open trading groups with many thousand participants emerged on Telegram. Law enforcement agencies monitor suspicious users in such chat rooms. This research shows that text analysis, based on natural language processing, facilitates this through a meaningful domain overview and detailed investigations. We crawled a corpus from such self-proclaimed black markets and annotated five attribute types products, money, payment methods, user names, and locations. Based on each message a user sends, we extract and group these attributes to build profiles. Then, we build features to cluster the profiles. Pretrained word vectors yield better unsupervised clustering results than current
state-of-the-art transformer models. The result is a semantically meaningful high-level overview of the user landscape of black market chatrooms. Additionally, the extracted structured information serves as a foundation for further data exploration, for example, the most active users or preferred payment methods.
Messenger apps like WhatsApp and Telegram are frequently used for everyday communication, but they can also be utilized as a platform for illegal activity. Telegram allows public groups with up to 200.000 participants. Criminals use these public groups for trading illegal commodities and services, which becomes a concern for law enforcement agencies, who manually monitor suspicious activity in these chat rooms. This research demonstrates how natural language processing (NLP) can assist in analyzing these chat rooms, providing an explorative overview of the domain and facilitating purposeful analyses of user behavior. We provide a publicly available corpus of annotated text messages with entities and relations from four self-proclaimed black market chat rooms. Our pipeline approach aggregates the extracted product attributes from user messages to profiles and uses these with their sold products as features for clustering. The extracted structured information is the foundation for further data exploration, such as identifying the top vendors or fine-granular price analyses. Our evaluation shows that pretrained word vectors perform better for unsupervised clustering than state-of-the-art transformer models, while the latter is still superior for sequence labeling.
In: Advances in intelligent computing in engineering : proceedings of the 9.International EG-ICE Workshop ; Darmstadt, (01 - 03 August) 2002 / Martina Schnellenbach-Held ... (eds.) . - Düsseldorf: VDI-Verl., 2002 .- Fortschritt-Berichte VDI, Reihe 4, Bauingenieurwesen ; 180 ; S. 1-35 The paper describes a novel way to support conceptual design in civil engineering. The designer uses semantical tools guaranteeing certain internal structures of the design result but also the fulfillment of various constraints. Two different approaches and corresponding tools are discussed: (a) Visually specified tools with automatic code generation to determine a design structure as well as fixing various constraints a design has to obey. These tools are also valuable for design knowledge specialist. (b) Extensions of existing CAD tools to provide semantical knowledge to be used by an architect. It is sketched how these different tools can be combined in the future. The main part of the paper discusses the concepts and realization of two prototypes following the two above approaches. The paper especially discusses that specific graphs and the specification of their structure are useful for both tool realization projects.
In: Proceedings of the 39th Annual Hawaii International Conference on System Sciences, 2006. HICSS '06 http://dx.doi.org/10.1109/HICSS.2006.200 The conceptual design phase at the beginning of the building construction process is not adequately supported by any CAD-tool. Conceptual design support needs regarding two aspects: first, the architect must be able to develop conceptual sketches that provide abstraction from constructive details. Second, conceptually relevant knowledge should be available to check these conceptual sketches. The paper deals with knowledge to formalize for conceptual design. To enable domain experts formalizing knowledge, a graph-based specification is presented that allows the development of a domain ontology and design rules specific for one class of buildings at runtime. The provided tool support illustrates the introduced concepts and demonstrates the consistency analysis between knowledge and conceptual design.
Der konstruktive Entwurf wird in derzeitigen CAD-Systemen gut unterstützt, nicht aber der konzeptuelle Gebäude-Entwurf. Dieser abstrahiert von konstruktiven Elementen wie Linie, Wand oder Decke, um auf die Konzepte, d.h. die eigentlichen Funktionen, heraus zu arbeiten. Diese abstraktere, funktionale Sichtweise auf ein Gebäude ist während der frühen Entwurfsphase essentiell, um Struktur und Organisation des gesamten Gebäudes zu erfassen. Bereits in dieser Phase muss Fachwissen (z. B. rechtliche, ökonomische und technische Bestimmungen) berücksichtigt werden. Im Rahmen des vorliegenden Projekts werden Software-Werkzeuge integriert in industrielle CAD-Systeme entwickelt, die den konzeptuellen Gebäude-Entwurf ermöglichen und diesen gegen Fachwissen prüfen. Das Projekt ist in zwei Teile gegliedert. Im Top-Down-Ansatz werden Datenstrukturen und Methoden zur Strukturierung, Repräsentation und Evaluation von gebäudespezifischem Fachwissen erarbeitet. Dieser Teil baut auf den graphbasierten Werkzeugen PROGRES und UPGRADE des Lehrstuhls auf. Der Bottom-Up-Ansatz ist industriell orientiert und hat zum Ziel, das kommerzielle CAD-System ArchiCAD zu erweitern. Hierbei soll der frühe, konzeptuelle Gebäude-Entwurf in einem CAD-System ermöglicht werden. Der Entwurf kann darüber hinaus gegen das definierte Fachwissen geprüft werden. Im Rahmen des graphbasierten Top-Down-Ansatzes wurde zunächst eine neue Spezifikationsmethode für die Sprache PROGRES entwickelt. Das PROGRES-System erlaubt die Spezifikation von Werkzeugen in deklarativer Form. Üblicherweise wird domänenspezifisches Fachwissen in der PROGRES-Spezifikation codiert, das daraus generierte visuelle Werkzeug stellt dann die entsprechende Funktionalität zur Verfügung. Mit dieser Methode sind am Lehrstuhl für Informatik III Werkzeuge für verschie-dene Anwendungsdomänen entstanden. In unserem Fall versetzen wir einen Domänen-Experten, z. B. einen erfahrenen Architekten, in die Lage, Fachwissen zur Laufzeit einzugeben, dieses zu evaluieren, abzuändern oder zu ergänzen. Im Rahmen der bisherigen Arbeit wurde dazu eine parametrisierte PROGRES-Spezifikation und zwei darauf aufbauende Werkzeuge entwickelt, welche die dynamische Eingabe von gebäude-technisch relevantem Fachwissen erlauben und einen graphbasierten, konzeptuellen Gebäude-Entwurf ermöglichen. In diesem konzeptuellen Gebäude-Entwurf wird von Raumgrößen und Positionen abstrahiert, um die funktionale Struktur eines Gebäudes zu beschreiben. Das Fachwissen kann von einem Architekten visuell definiert werden. Es können semantische Einheiten, im einfachsten Fall Räume, nach verschiedenen Kriterien kategorisiert und klassifiziert werden. Mit Hilfe von Attributen und Relationen können die semantischen Einheiten präziser beschrieben und in Beziehung zueinander gesetzt werden. Die in PROGRES spezifizierten Konsistenz-Analysen erlauben die Prüfung eines graphbasierten konzeptuellen Gebäude-Entwurfs gegen das dynamisch eingefügte Fachwissen. Im zweiten Teil des Forschungsprojekts, dem Bottom-Up-Ansatz, wird das CAD-System ArchiCAD erweitert, um den integrierten konzeptuellen Gebäude-Entwurf zu ermöglichen. Der Architekt erhält dazu neue Entwurfselemente, die Raumobjekte, welche die relevanten semantischen Einheiten während der frühen Entwurfsphase repräsentieren. Mit Hilfe der Raumobjekte kann der Architekt in ArchiCAD den Grundriss und das Raumprogramm eines Gebäudes entwerfen, ohne von konstruktiven Details in seiner Kreativität eingeschränkt zu werden. Die Arbeitsweise mit Raumobjekten entspricht dem informellen konzeptuellen Entwurf auf einer Papierskizze und ist daher für den Architekten intuitiv und einfach zu verwenden. Durch die Integration in ArchiCAD ergibt sich eine weitere Unterstützung: Das im Top-Down-Ansatz spezifizierte Fach-wissen wird verwendet, um den konzeptuellen Gebäude-Entwurf des Architekten auf Regelverletzungen zu überprüfen. Entwurfsfehler werden angezeigt. Zum Abschluss des konzeptuellen Gebäude-Entwurfs mit Raumobjekten wird durch ein weiteres neu entwickeltes Werkzeug eine initiale Wandstruktur automatisch erzeugt, die als Grundlage für die folgenden konstruktiven Entwurfsphasen dient. Alle beschriebenen Erwei-terungen sind in ArchiCAD integriert, sie sind für den Architekten daher leicht zu erlernen und einfach zu bedienen.