517 research outputs found

    Building a semantic search engine with games and crowdsourcing

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    Semantic search engines aim at improving conventional search with semantic information, or meta-data, on the data searched for and/or on the searchers. So far, approaches to semantic search exploit characteristics of the searchers like age, education, or spoken language for selecting and/or ranking search results. Such data allow to build up a semantic search engine as an extension of a conventional search engine. The crawlers of well established search engines like Google, Yahoo! or Bing can index documents but, so far, their capabilities to recognize the intentions of searchers are still rather limited. Indeed, taking into account characteristics of the searchers considerably extend both, the quantity of data to analyse and the dimensionality of the search problem. Well established search engines therefore still focus on general search, that is, "search for all", not on specialized search, that is, "search for a few". This thesis reports on techniques that have been adapted or conceived, deployed, and tested for building a semantic search engine for the very specific context of artworks. In contrast to, for example, the interpretation of X-ray images, the interpretation of artworks is far from being fully automatable. Therefore artwork interpretation has been based on Human Computation, that is, a software-based gathering of contributions by many humans. The approach reported about in this thesis first relies on so called Games With A Purpose, or GWAPs, for this gathering: Casual games provide an incentive for a potentially unlimited community of humans to contribute with their appreciations of artworks. Designing convenient incentives is less trivial than it might seem at first. An ecosystem of games is needed so as to collect the meta-data on artworks intended for. One game generates the data that can serve as input of another game. This results in semantically rich meta-data that can be used for building up a successful semantic search engine. Thus, a first part of this thesis reports on a "game ecosystem" specifically designed from one known game and including several novel games belonging to the following game classes: (1) Description Games for collecting obvious and trivial meta-data, basically the well-known ESP (for extra-sensorial perception) game of Luis von Ahn, (2) the Dissemination Game Eligo generating translations, (3) the Diversification Game Karido aiming at sharpening differences between the objects, that is, the artworks, interpreted and (3) the Integration Games Combino, Sentiment and TagATag that generate structured meta-data. Secondly, the approach to building a semantic search engine reported about in this thesis relies on Higher-Order Singular Value Decomposition (SVD). More precisely, the data and meta-data on artworks gathered with the afore mentioned GWAPs are collected in a tensor, that is a mathematical structure generalising matrices to more than only two dimensions, columns and rows. The dimensions considered are the artwork descriptions, the players, and the artwork themselves. A Higher-Order SVD of this tensor is first used for noise reduction in This thesis reports also on deploying a Higher-Order LSA. The parallel Higher-Order SVD algorithm applied for the Higher-Order LSA and its implementation has been validated on an application related to, but independent from, the semantic search engine for artworks striven for: image compression. This thesis reports on the surprisingly good image compression which can be achieved with Higher-Order SVD. While compression methods based on matrix SVD for each color, the approach reported about in this thesis relies on one single (higher-order) SVD of the whole tensor. This results in both, better quality of the compressed image and in a significant reduction of the memory space needed. Higher-Order SVD is extremely time-consuming what calls for parallel computation. Thus, a step towards automatizing the construction of a semantic search engine for artworks was parallelizing the higher-order SVD method used and running the resulting parallel algorithm on a super-computer. This thesis reports on using Hestenes’ method and R-SVD for parallelising the higher-order SVD. This method is an unconventional choice which is explained and motivated. As of the super-computer needed, this thesis reports on turning the web browsers of the players or searchers into a distributed parallel computer. This is done by a novel specific system and a novel implementation of the MapReduce data framework to data parallelism. Harnessing the web browsers of the players or searchers saves computational power on the server-side. It also scales extremely well with the number of players or searchers because both, playing with and searching for artworks, require human reflection and therefore results in idle local processors that can be brought together into a distributed super-computer.Semantische Suchmaschinen dienen der Verbesserung konventioneller Suche mit semantischen Informationen, oder Metadaten, zu Daten, nach denen gesucht wird, oder zu den Suchenden. Bisher nutzt Semantische Suche Charakteristika von Suchenden wie Alter, Bildung oder gesprochene Sprache fĂŒr die Auswahl und/oder das Ranking von Suchergebnissen. Solche Daten erlauben den Aufbau einer Semantischen Suchmaschine als Erweiterung einer konventionellen Suchmaschine. Die Crawler der fest etablierten Suchmaschinen wie Google, Yahoo! oder Bing können Dokumente indizieren, bisher sind die FĂ€higkeiten eher beschrĂ€nkt, die Absichten von Suchenden zu erkennen. TatsĂ€chlich erweitert die BerĂŒcksichtigung von Charakteristika von Suchenden betrĂ€chtlich beides, die Menge an zu analysierenden Daten und die DimensionalitĂ€t des Such-Problems. Fest etablierte Suchmaschinen fokussieren deswegen stark auf allgemeine Suche, also "Suche fĂŒr alle", nicht auf spezialisierte Suche, also "Suche fĂŒr wenige". Diese Arbeit berichtet von Techniken, die adaptiert oder konzipiert, eingesetzt und getestet wurden, um eine semantische Suchmaschine fĂŒr den sehr speziellen Kontext von Kunstwerken aufzubauen. Im Gegensatz beispielsweise zur Interpretation von Röntgenbildern ist die Interpretation von Kunstwerken weit weg davon gĂ€nzlich automatisiert werden zu können. Deswegen basiert die Interpretation von Kunstwerken auf menschlichen Berechnungen, also Software-basiertes Sammeln von menschlichen BeitrĂ€gen. Der Ansatz, ĂŒber den in dieser Arbeit berichtet wird, beruht auf sogenannten "Games With a Purpose" oder GWAPs die folgendes sammeln: Zwanglose Spiele bieten einen Anreiz fĂŒr eine potenziell unbeschrĂ€nkte Gemeinde von Menschen, mit Ihrer WertschĂ€tzung von Kunstwerken beizutragen. Geeignete Anreize zu entwerfen in weniger trivial als es zuerst scheinen mag. Ein Ökosystem von Spielen wird benötigt, um Metadaten gedacht fĂŒr Kunstwerke zu sammeln. Ein Spiel erzeugt Daten, die als Eingabe fĂŒr ein anderes Spiel dienen können. Dies resultiert in semantisch reichhaltigen Metadaten, die verwendet werden können, um eine erfolgreiche Semantische Suchmaschine aufzubauen. Deswegen berichtet der erste Teil dieser Arbeit von einem "Spiel-Ökosystem", entwickelt auf Basis eines bekannten Spiels und verschiedenen neuartigen Spielen, die zu verschiedenen Spiel-Klassen gehören. (1) Beschreibungs-Spiele zum Sammeln offensichtlicher und trivialer Metadaten, vor allem dem gut bekannten ESP-Spiel (Extra Sensorische Wahrnehmung) von Luis von Ahn, (2) dem Verbreitungs-Spiel Eligo zur Erzeugung von Übersetzungen, (3) dem Diversifikations-Spiel Karido, das Unterschiede zwischen Objekten, also interpretierten Kunstwerken, schĂ€rft und (3) Integrations-Spiele Combino, Sentiment und Tag A Tag, die strukturierte Metadaten erzeugen. Zweitens beruht der Ansatz zum Aufbau einer semantischen Suchmaschine, wie in dieser Arbeit berichtet, auf SingulĂ€rwertzerlegung (SVD) höherer Ordnung. PrĂ€ziser werden die Daten und Metadaten ĂŒber Kunstwerk gesammelt mit den vorher genannten GWAPs in einem Tensor gesammelt, einer mathematischen Struktur zur Generalisierung von Matrizen zu mehr als zwei Dimensionen, Spalten und Zeilen. Die betrachteten Dimensionen sind die Beschreibungen der Kunstwerke, die Spieler, und die Kunstwerke selbst. Eine SingulĂ€rwertzerlegung höherer Ordnung dieses Tensors wird zuerst zur Rauschreduktion verwendet nach der Methode der sogenannten Latenten Semantischen Analyse (LSA). Diese Arbeit berichtet auch ĂŒber die Anwendung einer LSA höherer Ordnung. Der parallele Algorithmus fĂŒr SingulĂ€rwertzerlegungen höherer Ordnung, der fĂŒr LSA höherer Ordnung verwendet wird, und seine Implementierung wurden validiert an einer verwandten aber von der semantischen Suche unabhĂ€ngig angestrebten Anwendung: Bildkompression. Diese Arbeit berichtet von ĂŒberraschend guter Kompression, die mit SingulĂ€rwertzerlegung höherer Ordnung erzielt werden kann. Neben Matrix-SVD-basierten Kompressionsverfahren fĂŒr jede Farbe, beruht der Ansatz wie in dieser Arbeit berichtet auf einer einzigen SVD (höherer Ordnung) auf dem gesamten Tensor. Dies resultiert in beidem, besserer QualitĂ€t von komprimierten Bildern und einer signifikant geringeren des benötigten Speicherplatzes. SingulĂ€rwertzerlegung höherer Ordnung ist extrem zeitaufwĂ€ndig, was parallele Berechnung verlangt. Deswegen war ein Schritt in Richtung Aufbau einer semantischen Suchmaschine fĂŒr Kunstwerke eine Parallelisierung der verwendeten SVD höherer Ordnung auf einem Super-Computer. Diese Arbeit berichtet vom Einsatz der Hestenes’-Methode und R-SVD zur Parallelisierung der SVD höherer Ordnung. Diese Methode ist eine unkonventionell Wahl, die erklĂ€rt und motiviert wird. Ab nun wird ein Super-Computer benötigt. Diese Arbeit berichtet ĂŒber die Wandlung der Webbrowser von Spielern oder Suchenden in einen verteilten Super-Computer. Dies leistet ein neuartiges spezielles System und eine neuartige Implementierung des MapReduce Daten-Frameworks fĂŒr Datenparallelismus. Das Einspannen der Webbrowser von Spielern und Suchenden spart server-seitige Berechnungskraft. Ebenso skaliert die Berechnungskraft so extrem gut mit der Spieleranzahl oder Suchenden, denn beides, Spiel mit oder Suche nach Kunstwerken, benötigt menschliche Reflektion, was deswegen zu ungenutzten lokalen Prozessoren fĂŒhrt, die zu einem verteilten Super-Computer zusammengeschlossen werden können

    Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

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    The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation

    A TOPIC SENSITIVE SIMRANK (TSSR) MODEL FOR EXPERTS FINDING ON ONLINE RESEARCH SOCIAL PLATFORMS

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    As an efficient online academic information repository and information channel with crowds’ contribution, online research social platforms have become an efficient tool for various kinds of research & management applications. Social network platforms have also become a major source to seek for field experts. They have advantages of crowd contributions, easy to access without geographic restrictions and avoiding conflict of interests over traditional database and search engine based approaches. However, current research attempts to find experts based on features such as published research work, social relationships, and online behaviours (e.g. reads and downloads of publications) on social platforms, they ignore to verify the reliability of identified experts. To bridge this gap, this research proposes an innovative Topic Sensitive SimRank (TSSR) model to identify “real” experts on social network platforms. TSSR model includes three components: LDA for Expertise Extension, Topic Sensitive Network for Reputation Measurement, and Topic Sensitive SimRank for unsuitable experts detection. We also design a parallel computing strategy to improve the efficiency of the proposed methods. Last, to verify the effectiveness of the proposed model, we design an experiment on one of the research social platforms-ScholarMate to seek for experts for companies that need academic-industry collaboration

    Hierarchical Classification and its Application in University Search

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    Web search engines have been adopted by most universities for searching webpages in their own domains. Basically, a user sends keywords to the search engine and the search engine returns a flat ranked list of webpages. However, in university search, user queries are usually related to topics. Simple keyword queries are often insufficient to express topics as keywords. On the other hand, most E-commerce sites allow users to browse and search products in various hierarchies. It would be ideal if hierarchical browsing and keyword search can be seamlessly combined for university search engines. The main difficulty is to automatically classify and rank a massive number of webpages into the topic hierarchies for universities. In this thesis, we use machine learning and data mining techniques to build a novel hybrid search engine with integrated hierarchies for universities, called SEEU (Search Engine with hiErarchy for Universities). Firstly, we study the problem of effective hierarchical webpage classification. We develop a parallel webpage classification system based on Support Vector Machines. With extensive experiments on the well-known ODP (Open Directory Project) dataset, we empirically demonstrate that our hierarchical classification system is very effective and outperforms the traditional flat classification approaches significantly. Secondly, we study the problem of integrating hierarchical classification into the ranking system of keywords-based search engines. We propose a novel ranking framework, called ERIC (Enhanced Ranking by hIerarchical Classification), for search engines with hierarchies. Experimental results on four large-scale TREC (Text REtrieval Conference) web search datasets show that our ranking system with hierarchical classification outperforms the traditional flat keywords-based search methods significantly. Thirdly, we propose a novel active learning framework to improve the performance of hierarchical classification, which is important for ranking webpages in hierarchies. From our experiments on the benchmark text datasets, we find that our active learning framework can achieve good classification performance yet save a considerable number of labeling effort compared with the state-of-the-art active learning methods for hierarchical text classification. Fourthly, based on the proposed classification and ranking methods, we present a novel hierarchical classification framework for mining academic topics from university webpages. We build an academic topic hierarchy based on the commonly accepted Wikipedia academic disciplines. Based on this hierarchy, we train a hierarchical classifier and apply it to mine academic topics. According to our comprehensive analysis, the academic topics mined by our method are reasonable and consistent with the real-world topic distribution in universities. Finally, we combine all the proposed techniques together and implement the SEEU search engine. According to two usability studies conducted in the ECE and the CS departments at our university, SEEU is favored by the majority of participants. To conclude, the main contribution of this thesis is a novel search engine, called SEEU, for universities. We discuss the challenges toward building SEEU and propose effective machine learning and data mining methods to tackle them. With extensive experiments on well-known benchmark datasets and real-world university webpage datasets, we demonstrate that our system is very effective. In addition, two usability studies of SEEU in our university show that SEEU has a great promise for university search
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