Partially Observable Markov Decision Processes with Behavioral Norms

This extended abstract discusses various approaches to the constraining of Partially Observable Markov Decision Processes (POMDPs) using social norms and logical assertions in a dynamic logic framework. Whereas the exploitation of synergies among formal logic on the one hand and stochastic approaches and machine learning on the other is gaining significantly increasing interest since several years, most of the respective approaches fall into the category of relational learning in the widest sense, including inductive (stochastic) logic programming. In contrast, the use of formal knowledge (including knowledge about social norms) for the provision of hard constraints and prior knowledge for some stochastic learning or modeling task is much less frequently approached. Although we do not propose directly implementable technical solutions, it is hoped that this work is a useful contribution to a discussion about the usefulness and feasibility of approaches from norm research and formal logic in the context of stochastic behavioral models, and vice versa

Toward Real Event Detection

News agencies and other news providers or consumers are confronted with the task of extracting events from news articles. This is done i) either to monitor and, hence, to be informed about events of specific kinds over time and/or ii) to react to events immediately. In the past, several promising approaches to extracting events from text have been proposed. Besides purely statistically-based approaches there are methods to represent events in a semantically-structured form, such as graphs containing actions (predicates), participants (entities), etc. However, it turns out to be very dificult to automatically determine whether an event is real or not. In this paper, we give an overview of approaches which proposed solutions for this research problem. We show that there is no gold standard dataset where real events are annotated in text documents in a fine-grained, semantically-enriched way. We present A methodology of creating such a dataset with the help of crowdsourcing and present preliminary results

A Statistical Comparison of Current Knowledge Bases

In the last years, many knowledge bases have been developed and used in real-world applications. These include DBpedia, Wikidata, and YAGO which all cover general knowledge and therefore similar topics. In this poster, we present statistical measurements on these KBs. Our experiments reveal that despite that fact that these KBs cover the same domains to a considerable amount, they differ from each other significantly w.r.t. their graph-based structure and ontological aspects

Browsing DBpedia Entities with Summaries

Abstract. The term "Linked Data" describes online-retrievable formal descriptions of entities and their links to each other. Machines and humans alike can retrieve these descriptions and discover information about links to other entities. However, for human users it becomes difficult to browse descriptions of single entities because, in many cases, they are referenced in more than a thousand statements. In this demo paper we present summarum, a system that ranks triples and enables entity summaries for improved navigation within Linked Data. In its current implementation, the system focuses on DBpedia with the summaries being based on the PageRank scores of the involved entities