Was Suchmaschinen nicht können. Holistische Entitätssuche auf Web Daten

Mehr als 50% aller Web Suchanfragen sind entitätsbezogen. Benutzer suchen entweder nach Entitäten oder nach Entitätsinformationen. Dennoch solche Anfragen von Suchmaschinen nicht gut unterstützt. Aufbauend auf dem Konzept des semiotischen Dreiecks aus der kognitiven Psychologie, haben wir drei Anfragetypen zur Entitätssuche identifiziert: typbasierte Anfragen – Suche nach Entitäten eines gegebenen Typs, prototypbasierte Anfragen – Suche nach Entitäten mit bestimmten Eigenschaften, und instanzbasierte Anfragen – Suche nach Entitäten die ähnlich zu einer gegebene Entität sind. Für typbasierte Anfragen haben wir eine Methode entwickelt die query expansion mit einer self-supervised vocabulary learning Technik auf strukturierten und unstrukturierten Daten verbindet. Unser Ansatz liefert einen guten Kompromiss zwischen Precision und Recall. Für prototypbasierte Anfragen stellen wir ProSWIP vor. Dies ist ein eigenschaftsbasiertes System um Entitäten aus dem Web abzurufen. Da aber die Anzahl der Eigenschaften die durch die Benutzer bereitgestellt werden relativ klein sein kann, baut ProSWIP auf direkten Fragen und Benutzer Feedback um die Menge der Eigenschaften zu einer Menge welche die Intentionen der Benutzer korrekt erfasst zu erweitern. Unsere Experimente zeigen dass mit maximal vier Fragen eine perfekte Precision erreicht wird. In dem Fall von instanzbasierten Anfragen besteht die Schwierigkeit darin eine Anfrageform zu finden die die Benutzerintentionen eindeutig macht. Wir stellen eine minimalistische instanzbasierte Anfrage, die aus einem Beispiel und dem entsprechenden Entitätstypen besteht vor. Mit Hilfe des Konzepts der Familienähnlichkeit entwickeln wir eine praktische Lösung um Entitäten mit Bezug zur der Anfragenentität direkt aus dem Web abzurufen. Unser Ansatz erzielt sogar für Anfragen, die für standard Entitätssuchaufgaben wie related entity finding problematisch waren, gute Ergebnisse. Entitätszusammenfassung ist ein anderer Typ von entitätszentrischen Anfragen, der Informationen bezüglich einer Entität bereitstellt. Googles Knowledge Graph ist der Stand der Technik für solche Aufgaben. Aber das Zurückgreifen auf manuell erstellte Knowledgebases schließt weniger bekannten Entitäten für das Knowledge Graph aus. Wir schlagen daher vor datengetriebene Ansätze zu nutzen. Wir sind überzeugt dass das Bewältigen dieser vier Anfragetypen eine holistische Entitätssuche auf Web Daten für die nächste Generation von Suchmaschinen ermöglicht.More than 50% of all Web queries are entity related. Users search either for entities or for entity information. Still, search engines do not accommodate entity-centric search very well. Building on the concept of the semiotic triangle from cognitive psychology, which models entity types in terms of intensions and extensions, we identified three types of queries for retrieving entities: type-based queries - searching for entities of a given type, prototype-based queries - searching for entities having certain properties, and instance-based queries - searching for entities being similar to a given entity. For type-based queries we present a method that combines query expansion with a self-supervised vocabulary learning technique built on both structured and unstructured data. Our approach is able to achieve a good tradeoff between precision and recall. For prototype-based queries we propose ProSWIP, a property-based system for retrieving entities from the Web. Since the number of properties given by the users can be quite small, ProSWIP relies on direct questions and user feedback to expand the set of properties to a set that captures the user’s intentions correctly. Our experiments show that within a maximum of four questions the system achieves perfect precision of the selected entities. In the case of instance-based queries the first challenge is to establish a query form that allows for disambiguating user intentions without putting too much cognitive pressure on the user. We propose a minimalistic instance-based query comprising the example entity and intended entity type. With this query and building on the concept of family resemblance we present a practical way for retrieving entities directly from the Web. Our approach can even cope with queries which have proven problematic for benchmark tasks like related entity finding. Providing information about a given entity, entity summarization is another kind of entity-centric query. Google’s Knowledge Graph is the state of the art for this task. But relying entirely on manually curated knowledge bases, the Knowledge Graph does not include all new and less known entities. We propose to use a data-driven approach. Our experiments on real-world entities show the superiority of our method. We are confident that mastering these four query types enables holistic entity search on Web data for the next generation of search engines

Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving

Behavior and motion planning play an important role in automated driving. Traditionally, behavior planners instruct local motion planners with predefined behaviors. Due to the high scene complexity in urban environments, unpredictable situations may occur in which behavior planners fail to match predefined behavior templates. Recently, general-purpose planners have been introduced, combining behavior and local motion planning. These general-purpose planners allow behavior-aware motion planning given a single reward function. However, two challenges arise: First, this function has to map a complex feature space into rewards. Second, the reward function has to be manually tuned by an expert. Manually tuning this reward function becomes a tedious task. In this paper, we propose an approach that relies on human driving demonstrations to automatically tune reward functions. This study offers important insights into the driving style optimization of general-purpose planners with maximum entropy inverse reinforcement learning. We evaluate our approach based on the expected value difference between learned and demonstrated policies. Furthermore, we compare the similarity of human driven trajectories with optimal policies of our planner under learned and expert-tuned reward functions. Our experiments show that we are able to learn reward functions exceeding the level of manual expert tuning without prior domain knowledge.Comment: Appeared at IROS 2019. Accepted version. Added/updated footnote, minor correction in preliminarie

Driving Style Encoder: Situational Reward Adaptation for General-Purpose Planning in Automated Driving

General-purpose planning algorithms for automated driving combine mission, behavior, and local motion planning. Such planning algorithms map features of the environment and driving kinematics into complex reward functions. To achieve this, planning experts often rely on linear reward functions. The specification and tuning of these reward functions is a tedious process and requires significant experience. Moreover, a manually designed linear reward function does not generalize across different driving situations. In this work, we propose a deep learning approach based on inverse reinforcement learning that generates situation-dependent reward functions. Our neural network provides a mapping between features and actions of sampled driving policies of a model-predictive control-based planner and predicts reward functions for upcoming planning cycles. In our evaluation, we compare the driving style of reward functions predicted by our deep network against clustered and linear reward functions. Our proposed deep learning approach outperforms clustered linear reward functions and is at par with linear reward functions with a-priori knowledge about the situation.Comment: To appear in Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Paris, France, June 2020 (Virtual Conference). Accepted version. Corrected figure fon

Mining semantic relations from product features

El proyecto ha sido desarrollado en el Departamento de Sistemas de Información (IfIS) de la Unversidad Técnica de Braunschweig (TU Braunschweig) y tiene como objetivo principal la extracción de relaciones semánticas entre, los términos extraídos inicialmente de las características de productos que forman grupos semánticos, y los términos que aparecen en los comentarios que los usuarios hacen sobre los mismos productos, con el objetivo de formar un grupo semántico mayor. Para conseguir este objetivo se ha trabajado con comentarios reales con técnicas de procesamiento de lenguaje natural y posteriormente se han aplicado técnicas clásicas de recuperación de información, como son LSI/LSA, PLSI/PLSA y LDA. El grueso del proyecto se basa en el análisis de cómo estas técnicas, que no están diseñadas para esto, alcanzan los objetivos descritos. Los resultados demuestran que LDA es el método que más se ajusta a estos objetivos, corroborando las premisas iniciales

Simulation-based reinforcement learning for real-world autonomous driving

We use reinforcement learning in simulation to obtain a driving system controlling a full-size real-world vehicle. The driving policy takes RGB images from a single camera and their semantic segmentation as input. We use mostly synthetic data, with labelled real-world data appearing only in the training of the segmentation network. Using reinforcement learning in simulation and synthetic data is motivated by lowering costs and engineering effort. In real-world experiments we confirm that we achieved successful sim-to-real policy transfer. Based on the extensive evaluation, we analyze how design decisions about perception, control, and training impact the real-world performance

Proswip: Property-based data access for semantic web interactive programming

Abstract. The Semantic Web has matured from a mere theoretical vision to a variety of ready-to-use linked open data sources currently available on the Web. Still, with respect to application development, the Web community is just starting to develop new paradigms in which data as the main driver of applications is promoted to first class status. Relying on properties of resources as an indicator for the type, property-based typing is such a paradigm. In this paper, we inspect the feasibility of property-based typing for accessing data from the linked open data cloud. Problems in terms of transparency and quality of the selected data were noticeable. To alleviate these problems, we developed an iterative approach that builds on human feedback. Introduction The amount of data available on the Web has considerably increased in the last few years. Despite huge efforts in the area of the Semantic Web to make such web data machine-processable, only a few applications have been developed that can take full advantage of this data. Besides the general sparseness of semantic data, this behavior is currently explained by the different representation formalisms of semantic data and application programming languages causing a problem of data-model/programming language interoperability. However, generated code is often unintelligible, hard to customize and almost impossible to maintain. While some frameworks make customization and maintainability more convenient by including support for IDEs, compile-time meta-programming [4] represents a better technique to cope with the interoperability problem. With compile-time meta-programming, developers can programmatically generate required classes instead of providing them directly into the source code. One such approach was recently presented by Microsoft as a feature of F# 3.

Case study on cooperative car data for estimating traffic states in an urban network

The use of floating car data as a particular case of probe vehicle data has been the object of extensive research for estimating traffic conditions, travel times, and origin-to-destination trip matrices. It is based on data collected from a GPS-equipped vehicle fleet or available cell phones. Cooperative cars with vehicle-to-vehicle and vehicle-to-infrastructure communication capabilities represent a step forward, as they also allow tracking of vehicles surrounding the equipped car. This paper presents the results of a limited experiment with a small fleet of cooperative cars in the central business district of Barcelona, Spain, known as L’Eixample District. Data collected from the experiment were used to build and calibrate the emulation of cooperative functions in a microscopic simulation model that captured the behavior of vehicle sensors in Barcelona’s central business district. Such a calibrated model allows emulating fleet data on a large scale that goes far beyond what a small fleet of cooperative vehicles could capture. To determine the traffic state, several approaches were developed for estimating traffic variables—whose accuracy depends on the penetration level of the technology—on the basis of extensions of Edie’s generalized definitions of the fundamental traffic variables with the emulated data.Peer ReviewedPostprint (author's final draft