Suchergebnisse

In this paper, we focus on a typical task on a web search, in which users want to discover the coherency between two concepts on the Web. In our point of view, this task can be seen as a retrieval process: starting with some source information, the goal is to find target information by following hyperlinks. Given two concepts, e.g. chemistry and gunpowder, are search engines able to find the coherency and explain it? In this paper, we introduce a novel way of linking two concepts by following paths of hyperlinks and collecting short text snippets. We implemented a proof-of-concept prototype, which extracts paths and snippets from Wikipedia articles. Our goal is to provide the user with an overview about the coherency, enriching the connection with a
short but meaningful description. In our experimental study, we compare the results of our approach with the capability of web search engines. The results show that 72% of the participants find ours better than these of web search engines. (author's abstract)

Implementing DDI 3: The German Microcensus Case Study

The dramatic increase in information on the World Wide Web makes it more difficult for web users using web search engines to effectively satisfy their information needs The users' lack of knowledge regarding the searched topics creates a complicated problem when formulating an effective query. Query expansion can play an essential role in overcoming such a deficit. However, because they lack sufficient knowledge about the searched topics, users sometimes find it difficult to evaluate the relatedness of the system's
automatically expanded terms. This problem arises mostly in domain-specific areas, e.g Social Science. In this paper, we expand the queries based on a structured, open knowledge resource on the Web (Wikipedia). We link the entities from a domain-specific corpus (qualitative journals in social science) to Wikipedia entities. With Wikipedia serving as background knowledge, we
help users with their selections by providing the most likely related terms. Furthermore, users are provided with contextual information that describes
each expanded term in order to give users a clearer idea about the meaning of each expanded term. By utilizing 10 test queries chosen by experts who also evaluated the results, we compared the results of using Wikipedia with the
results achieved by using a qualitative journal (author's abstract)

The availability of annotated data is an important prerequisite for the development of machine learning
algorithms for sentiment analysis. However, as manually labeling large datasets is time-consuming
and expensive, few datasets are available and most of them represent a small sample of a very narrow
domain, e.g. movie reviews or reviews of a certain product type. Additionally, many annotated datasets
are available for English texts only. However, the influence of different characteristics of the input
dataset on the performance of algorithms for sentiment analysis remains unclear if only training data
from one specific domain is available or if specific domains are mixed in the test corpus. We therefore
introduce a new dataset for German product reviews of various product types and investigate whether
even small variances in this specific domain (different product types) already exhibit different characteristics,
e.g. with regard to the difficulty of sentiment annotation. The annotation of this corpus lays
the basis for future enhanced annotations of similar corpora and for the extension of our annotations
to corpora of inherently different domains. These will then serve to investigate the influence of different
corpus characteristics on different algorithms for sentiment analysis and as a basis to apply machine
learning methods for sentence-wise sentiment analysis for German texts.

Anschlussvorhaben Forschungsverbund Datenzentren; Verbesserung des Zugangs der Wissenschaft zu Mikrodaten, Teilprojekt: Pilotprojekt zum Aufbau eines Servicezentrums für Mikrodaten der GESIS; Mikrodaten-Informationssystem MISSY. Dieser Bericht informiert über die durchgeführten Arbeiten im Rahmen des Projekts MISSY II (Projektlaufzeit 01.01.2008 - 31.12.2009). Es handelt sich dabei um ein Anschlussvorhaben zum Aufbau des Mikrodaten-Informationssystems MISSY, welches den inhaltlichen Ausbau von MISSY um die Metadaten der Mikrozensus Scientific Use Files der Jahre 1973 bis 2006 sowie die informationstechnologische Neuimplementierung des Systems beinhaltet.