A Comparative Analysis of Web Search Query: Informational Vs. Navigational Queries

The search engines are mainly used to retrieve relevant information. Information retrieval researchers show that queries are the basis for providing better search engine performance. The search query is becoming a means for users to search for their needed information. Web search query is one of the common search queries that is widely used in domain areas. However, the main challenge is the absence of a clear understanding of how web search query influences the users’ behavior on different web search engines. With the emergence of different types of a web search query, the understanding of user behavior on a web search query guides in improving the performance of many web search engines. Current research focused on using informational queries to search relevance information from a database while ignoring the importance of navigational queries. In this paper, we compared the informational and navigational type of a web search query that is mostly used in academic settings. Specifically, we examine the problems, solutions and techniques used in each of these types. We used a query log to conduct an experiment using BM25 mathematical model. The results indicated that the informational search query performed best because several keywords have been included to properly explain the queries. Also, language vocabularies used in informational queries contributed to better search performance. We believed that the outcomes of our comparisons will guide web search engine developers on the right search query for their web search engines

Improving the effectiveness and efficiency of web-based search tasks for policy workers

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands

Improving the Effectiveness and Efficiency of Web-Based Search Tasks for Policy Workers

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands

Automatic understanding of multimodal content for Web-based learning

Web-based learning has become an integral part of everyday life for all ages and backgrounds. On the one hand, the advantages of this learning type, such as availability, accessibility, flexibility, and cost, are apparent. On the other hand, the oversupply of content can lead to learners struggling to find optimal resources efficiently. The interdisciplinary research field Search as Learning is concerned with the analysis and improvement of Web-based learning processes, both on the learner and the computer science side. So far, automatic approaches that assess and recommend learning resources in Search as Learning (SAL) focus on textual, resource, and behavioral features. However, these approaches commonly ignore multimodal aspects. This work addresses this research gap by proposing several approaches that address the question of how multimodal retrieval methods can help support learning on the Web. First, we evaluate whether textual metadata of the TIB AV-Portal can be exploited and enriched by semantic word embeddings to generate video recommendations and, in addition, a video summarization technique to improve exploratory search. Then we turn to the challenging task of knowledge gain prediction that estimates the potential learning success given a specific learning resource. We used data from two user studies for our approaches. The first one observes the knowledge gain when learning with videos in a Massive Open Online Course (MOOC) setting, while the second one provides an informal Web-based learning setting where the subjects have unrestricted access to the Internet. We then extend the purely textual features to include visual, audio, and cross-modal features for a holistic representation of learning resources. By correlating these features with the achieved knowledge gain, we can estimate the impact of a particular learning resource on learning success. We further investigate the influence of multimodal data on the learning process by examining how the combination of visual and textual content generally conveys information. For this purpose, we draw on work from linguistics and visual communications, which investigated the relationship between image and text by means of different metrics and categorizations for several decades. We concretize these metrics to enable their compatibility for machine learning purposes. This process includes the derivation of semantic image-text classes from these metrics. We evaluate all proposals with comprehensive experiments and discuss their impacts and limitations at the end of the thesis.Web-basiertes Lernen ist ein fester Bestandteil des Alltags aller Alters- und Bevölkerungsschichten geworden. Einerseits liegen die Vorteile dieser Art des Lernens wie Verfügbarkeit, Zugänglichkeit, Flexibilität oder Kosten auf der Hand. Andererseits kann das Überangebot an Inhalten auch dazu führen, dass Lernende nicht in der Lage sind optimale Ressourcen effizient zu finden. Das interdisziplinäre Forschungsfeld Search as Learning beschäftigt sich mit der Analyse und Verbesserung von Web-basierten Lernprozessen. Bisher sind automatische Ansätze bei der Bewertung und Empfehlung von Lernressourcen fokussiert auf monomodale Merkmale, wie Text oder Dokumentstruktur. Die multimodale Betrachtung ist hingegen noch nicht ausreichend erforscht. Daher befasst sich diese Arbeit mit der Frage wie Methoden des Multimedia Retrievals dazu beitragen können das Lernen im Web zu unterstützen. Zunächst wird evaluiert, ob textuelle Metadaten des TIB AV-Portals genutzt werden können um in Verbindung mit semantischen Worteinbettungen einerseits Videoempfehlungen zu generieren und andererseits Visualisierungen zur Inhaltszusammenfassung von Videos abzuleiten. Anschließend wenden wir uns der anspruchsvollen Aufgabe der Vorhersage des Wissenszuwachses zu, die den potenziellen Lernerfolg einer Lernressource schätzt. Wir haben für unsere Ansätze Daten aus zwei Nutzerstudien verwendet. In der ersten wird der Wissenszuwachs beim Lernen mit Videos in einem MOOC-Setting beobachtet, während die zweite eine informelle web-basierte Lernumgebung bietet, in der die Probanden uneingeschränkten Internetzugang haben. Anschließend erweitern wir die rein textuellen Merkmale um visuelle, akustische und cross-modale Merkmale für eine ganzheitliche Darstellung der Lernressourcen. Durch die Korrelation dieser Merkmale mit dem erzielten Wissenszuwachs können wir den Einfluss einer Lernressource auf den Lernerfolg vorhersagen. Weiterhin untersuchen wir wie verschiedene Kombinationen von visuellen und textuellen Inhalten Informationen generell vermitteln. Dazu greifen wir auf Arbeiten aus der Linguistik und der visuellen Kommunikation zurück, die seit mehreren Jahrzehnten die Beziehung zwischen Bild und Text untersucht haben. Wir konkretisieren vorhandene Metriken, um ihre Verwendung für maschinelles Lernen zu ermöglichen. Dieser Prozess beinhaltet die Ableitung semantischer Bild-Text-Klassen. Wir evaluieren alle Ansätze mit umfangreichen Experimenten und diskutieren ihre Auswirkungen und Limitierungen am Ende der Arbeit