Relevant bibliographies by topics / Semantic search engine

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Semantic search engine'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Semantic search engine"

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Manjula, D., and T. V. Geetha. "Semantic Search Engine." Journal of Information & Knowledge Management 03, no. 01 (March 2004): 107–17. http://dx.doi.org/10.1142/s0219649204000729.

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Currently existing search engines index documents only by words and as a result, when a query can be interpreted in different senses, the irrelevant results are obtained in the midst of relevant results. A semantic search engine is proposed here which indexes documents both by words and senses and as a result tries to avoid the irrelevant results. The "crawler" traverses the worldwide web and the normalized documents are sent to the disambiguator module, which identifies the top few sense(s) of ambiguous words by employing a weighted disambiguation algorithm. The documents are then indexed by the words and the senses. The query is also disambiguated in a similar manner and retrieval is performed by matching both the sense and the word. The performance of the semantic search engine is compared against traditional word based indexing and also against the commercial search engines like Google, Yahoo, Hotbot and Lycos. The results show an impressive precision for the semantic search engine compared to other engines, particularly for ambiguous queries.
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Jayavel, Senthil, Margret Anouncia, and Abhinav Kapoor. "Semantic Search Engine." International Journal of Recent Contributions from Engineering, Science & IT (iJES) 1, no. 2 (November 2, 2013): 19. http://dx.doi.org/10.3991/ijes.v1i2.2711.

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Laddha, Shilpa S., and Pradip M. Jawandhiya. "Semantic Search Engine." Indian Journal of Science and Technology 10, no. 21 (February 1, 2017): 1–6. http://dx.doi.org/10.17485/ijst/2017/v10i23/115568.

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Shah, Dharmish, Jheel Somaiya, and Sindhu Nair. "Fuzzy Semantic Search Engine." International Journal of Computer Applications 107, no. 15 (December 18, 2014): 25–27. http://dx.doi.org/10.5120/18829-0272.

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Unadkat, Rimpal. "Survey Paper on Semantic Web." International Journal of Advanced Pervasive and Ubiquitous Computing 7, no. 4 (October 2015): 13–17. http://dx.doi.org/10.4018/ijapuc.2015100102.

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The World Wide Web (WWW) allows the people to share the information (data) from the large database repositories globally. The tremendous growth in the volume of data and with the terrific growth of number of web pages, traditional search engines now days are not appropriate and not suitable anymore. Search engine is the most important tool to discover any information in World Wide Web. Semantic Search Engine is born of traditional search engine to overcome the above problem. However, to overcome this problem in search engines to retrieve meaningful information intelligently, semantic web technologies are playing a major role. In this paper the authors present survey on the role of search engines in intelligent web, Background, Challenges and some issues.
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Lupiani-Ruiz, Eduardo, Ignacio García-Manotas, Rafael Valencia-García, Francisco García-Sánchez, Dagoberto Castellanos-Nieves, Jesualdo Tomás Fernández-Breis, and Juan Bosco Camón-Herrero. "Financial news semantic search engine." Expert Systems with Applications 38, no. 12 (November 2011): 15565–72. http://dx.doi.org/10.1016/j.eswa.2011.06.003.

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Beneventano, Domenico, and Sonia Bergamaschi. "Provenance-Aware Semantic Search Engines Based On Data Integration Systems." International Journal of Organizational and Collective Intelligence 4, no. 2 (April 2014): 1–30. http://dx.doi.org/10.4018/ijoci.2014040101.

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Search engines are common tools for virtually every user of the Internet and companies, such as Google and Yahoo!, have become household names. Semantic Search Engines try to augment and improve traditional Web Search Engines by using not just words, but concepts and logical relationships. Given the openness of the Web and the different sources involved, a Web Search Engine must evaluate quality and trustworthiness of the data; a common approach for such assessments is the analysis of the provenance of information. In this paper a relevant class of Provenance-aware Semantic Search Engines, based on a peer-to-peer, data integration mediator-based architecture is described. The architectural and functional features are an enhancement with provenance of the SEWASIE semantic search engine developed within the IST EU SEWASIE project, coordinated by the authors. The methodology to create a two level ontology and the query processing engine developed within the SEWASIE project, together with provenance extension are fully described.
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Lee, In-K., Seo-H. Son, and Soon-H. Kwon. "Knowledge-based Semantic Meta-Search Engine." Journal of Korean Institute of Intelligent Systems 14, no. 6 (October 1, 2004): 737–44. http://dx.doi.org/10.5391/jkiis.2004.14.6.737.

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Ajitha, P., and G. Gunasekaran. "Semantic Based Intuitive Topic Search Engine." International Review on Computers and Software (IRECOS) 9, no. 12 (December 31, 2014): 1964. http://dx.doi.org/10.15866/irecos.v9i12.4811.

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El-Sayed, Maged. "SEARCHSENSE: A SEMANTIC META SEARCH ENGINE." International Journal of Intelligent Computing and Information Sciences 18, no. 1 (January 1, 2018): 15–25. http://dx.doi.org/10.21608/ijicis.2018.15907.

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Dissertations / Theses on the topic "Semantic search engine"

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Narayan, Nitesh. "Advanced Intranet Search Engine." Thesis, Mälardalen University, School of Innovation, Design and Engineering, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-9408.

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Information retrieval has been a prevasive part of human society since its existence.With the advent of internet and World wide Web it became an extensive area of researchand major foucs, which lead to development of various search engines to locate the de-sired information, mostly for globally connected computer networks viz. internet.Butthere is another major part of computer network viz. intranet, which has not seen muchof advancement in information retrieval approaches, in spite of being a major source ofinformation within a large number of organizations.Most common technique for intranet based search engines is still mere database-centric. Thus practically intranets are unable to avail the benefits of sophisticated tech-niques that have been developed for internet based search engines without exposing thedata to commercial search engines.In this Master level thesis we propose a ”state of the art architecture” for an advancedsearch engine for intranet which is capable of dealing with continuously growing sizeof intranets knowledge base. This search engine employs lexical processing of doc-umetns,where documents are indexed and searched based on standalone terms or key-words, along with the semantic processing of the documents where the context of thewords and the relationship among them is given more importance.Combining lexical and semantic processing of the documents give an effective ap-proach to handle navigational queries along with research queries, opposite to the modernsearch engines which either uses lexical processing or semantic processing (or one as themajor) of the documents. We give equal importance to both the approaches in our design,considering best of the both world.This work also takes into account various widely acclaimed concepts like inferencerules, ontologies and active feedback from the user community to continuously enhanceand improve the quality of search results along with the possibility to infer and deducenew knowledge from the existing one, while preparing for the advent of semantic web.

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Xian, Yikun, and Liu Zhang. "Semantic Search with Information Integration." Thesis, Linnéuniversitetet, Institutionen för datavetenskap, fysik och matematik, DFM, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-13832.

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Since the search engine was first released in 1993, the development has never been slow down and various search engines emerged to vied for popularity. However, current traditional search engines like Google and Yahoo! are based on key words which lead to results impreciseness and information redundancy. A new search engine with semantic analysis can be the alternate solution in the future. It is more intelligent and informative, and provides better interaction with users.        This thesis discusses the detail on semantic search, explains advantages of semantic search over other key-word-based search and introduces how to integrate semantic analysis with common search engines. At the end of this thesis, there is an example of implementation of a simple semantic search engine.
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Wieser, Christoph. "Building a semantic search engine with games and crowdsourcing." Diss., Ludwig-Maximilians-Universität München, 2014. http://nbn-resolving.de/urn:nbn:de:bvb:19-169754.

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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 für 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 Fähigkeiten eher beschränkt, die Absichten von Suchenden zu erkennen. Tatsächlich erweitert die Berücksichtigung von Charakteristika von Suchenden beträchtlich beides, die Menge an zu analysierenden Daten und die Dimensionalität des Such-Problems. Fest etablierte Suchmaschinen fokussieren deswegen stark auf allgemeine Suche, also "Suche für alle", nicht auf spezialisierte Suche, also "Suche für wenige". Diese Arbeit berichtet von Techniken, die adaptiert oder konzipiert, eingesetzt und getestet wurden, um eine semantische Suchmaschine für den sehr speziellen Kontext von Kunstwerken aufzubauen. Im Gegensatz beispielsweise zur Interpretation von Röntgenbildern ist die Interpretation von Kunstwerken weit weg davon gänzlich automatisiert werden zu können. Deswegen basiert die Interpretation von Kunstwerken auf menschlichen Berechnungen, also Software-basiertes Sammeln von menschlichen Beiträgen. Der Ansatz, über den in dieser Arbeit berichtet wird, beruht auf sogenannten "Games With a Purpose" oder GWAPs die folgendes sammeln: Zwanglose Spiele bieten einen Anreiz für eine potenziell unbeschränkte Gemeinde von Menschen, mit Ihrer Wertschätzung von Kunstwerken beizutragen. Geeignete Anreize zu entwerfen in weniger trivial als es zuerst scheinen mag. Ein Ökosystem von Spielen wird benötigt, um Metadaten gedacht für Kunstwerke zu sammeln. Ein Spiel erzeugt Daten, die als Eingabe für 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, schärft 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 Singulärwertzerlegung (SVD) höherer Ordnung. Präziser werden die Daten und Metadaten über 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 Singulärwertzerlegung höherer Ordnung dieses Tensors wird zuerst zur Rauschreduktion verwendet nach der Methode der sogenannten Latenten Semantischen Analyse (LSA). Diese Arbeit berichtet auch über die Anwendung einer LSA höherer Ordnung. Der parallele Algorithmus für Singulärwertzerlegungen höherer Ordnung, der für LSA höherer Ordnung verwendet wird, und seine Implementierung wurden validiert an einer verwandten aber von der semantischen Suche unabhängig angestrebten Anwendung: Bildkompression. Diese Arbeit berichtet von überraschend guter Kompression, die mit Singulärwertzerlegung höherer Ordnung erzielt werden kann. Neben Matrix-SVD-basierten Kompressionsverfahren für 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 Qualität von komprimierten Bildern und einer signifikant geringeren des benötigten Speicherplatzes. Singulärwertzerlegung höherer Ordnung ist extrem zeitaufwändig, was parallele Berechnung verlangt. Deswegen war ein Schritt in Richtung Aufbau einer semantischen Suchmaschine für 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 erklärt und motiviert wird. Ab nun wird ein Super-Computer benötigt. Diese Arbeit berichtet über 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 für 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 führt, die zu einem verteilten Super-Computer zusammengeschlossen werden können.
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Hawkins, Brian M. "Developing a modular framework for implementing a semantic search engine." Thesis, Monterey, California : Naval Postgraduate School, 2009. http://edocs.nps.edu/npspubs/scholarly/theses/2009/Sep/09Sep%5FHawkins.pdf.

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Thesis (M.S. in Computer Science)--Naval Postgraduate School, September 2009.
Thesis Advisor(s): Martell, Craig. "September 2009." Description based on title screen as viewed on November 6, 2009. Author(s) subject terms: Semantic Search, Modular Search Engine, object-oriented programming, Java, UML. Includes bibliographical references (p. 77-78). Also available in print.
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Gkoutzis, Konstantinos. "A Semantic Web based search engine with X3D visualisation of queries and results." Thesis, University of Plymouth, 2013. http://hdl.handle.net/10026.1/1595.

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The Semantic Web project has introduced new techniques for managing information. Data can now be organised more efficiently and in such a way that computers can take advantage of the relationships that characterise the given input to present more relevant output. Semantic Web based search engines can quickly educe exactly what is needed to be found and retrieve it while avoiding information overload. Up until now, search engines have interacted with their users by asking them to look for words and phrases. We propose the creation of a new generation Semantic Web search engine that will offer a visual interface for queries and results. To create such an engine, information input must be viewed not merely as keywords, but as specific concepts and objects which are all part of the same universal system. To make the manipulation of the interconnected visual objects simpler and more natural, 3D graphics are utilised, based on the X3D Web standard, allowing users to semantically synthesise their queries faster and in a more logical way, both for them and the computer.
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Wieser, Christoph Verfasser], and François [Akademischer Betreuer] [Bry. "Building a semantic search engine with games and crowdsourcing / Christoph Wieser. Betreuer: François Bry." München : Universitätsbibliothek der Ludwig-Maximilians-Universität, 2014. http://d-nb.info/1051777127/34.

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Chatra, Raveesh Sandeep. "Using the Architectural Tradeoff Analysis Method to Evaluate the Software Architecture of a Semantic Search Engine: A Case Study." The Ohio State University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=osu1376916217.

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Wächter, Thomas. "Semi-automated Ontology Generation for Biocuration and Semantic Search." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-64838.

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Background: In the life sciences, the amount of literature and experimental data grows at a tremendous rate. In order to effectively access and integrate these data, biomedical ontologies – controlled, hierarchical vocabularies – are being developed. Creating and maintaining such ontologies is a difficult, labour-intensive, manual process. Many computational methods which can support ontology construction have been proposed in the past. However, good, validated systems are largely missing. Motivation: The biocuration community plays a central role in the development of ontologies. Any method that can support their efforts has the potential to have a huge impact in the life sciences. Recently, a number of semantic search engines were created that make use of biomedical ontologies for document retrieval. To transfer the technology to other knowledge domains, suitable ontologies need to be created. One area where ontologies may prove particularly useful is the search for alternative methods to animal testing, an area where comprehensive search is of special interest to determine the availability or unavailability of alternative methods. Results: The Dresden Ontology Generator for Directed Acyclic Graphs (DOG4DAG) developed in this thesis is a system which supports the creation and extension of ontologies by semi-automatically generating terms, definitions, and parent-child relations from text in PubMed, the web, and PDF repositories. The system is seamlessly integrated into OBO-Edit and Protégé, two widely used ontology editors in the life sciences. DOG4DAG generates terms by identifying statistically significant noun-phrases in text. For definitions and parent-child relations it employs pattern-based web searches. Each generation step has been systematically evaluated using manually validated benchmarks. The term generation leads to high quality terms also found in manually created ontologies. Definitions can be retrieved for up to 78% of terms, child ancestor relations for up to 54%. No other validated system exists that achieves comparable results. To improve the search for information on alternative methods to animal testing an ontology has been developed that contains 17,151 terms of which 10% were newly created and 90% were re-used from existing resources. This ontology is the core of Go3R, the first semantic search engine in this field. When a user performs a search query with Go3R, the search engine expands this request using the structure and terminology of the ontology. The machine classification employed in Go3R is capable of distinguishing documents related to alternative methods from those which are not with an F-measure of 90% on a manual benchmark. Approximately 200,000 of the 19 million documents listed in PubMed were identified as relevant, either because a specific term was contained or due to the automatic classification. The Go3R search engine is available on-line under www.Go3R.org.
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Aluc, Gunes. "Design And Implementation Of An Ontology Extraction Framework And A Semantic Search Engine Over Jsr-170 Compliant Content Repositories." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610665/index.pdf.

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A Content Management System (CMS) is a software application for creating, publishing, editing and managing content. The future step in content management system development is building intelligence over existing content resources that are heterogeneous in nature. Intelligence collected at the knowledge base can later on be used for executing semantic queries. Expressing the relations among content resources with ontological formalisms is therefore the key to implementing such semantic features. In this work, a methodology for the semantic lifting of JSR-170 compliant content repositories to ontologies is devised. The fact that in the worst case JSR-170 enforces no particular structural restrictions on the content model poses a technical challenge both for the initial build-up and further synchronization of the knowledge base. To address this problem, some recurring structural patterns in JSR-170 compliant content repositories are exploited. The value of the ontology extraction framework is assessed through a semantic search mechanism that is built on top of the extracted ontologies. The work in this thesis is complementary to the &ldquo
Interactive Knowledge Stack for small to medium CMS/KMS providers (IKS)&rdquo
project funded by the EC (FP7-ICT-2007-3).
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Arlitsch, Kenning Verfasser], Michael [Gutachter] Seadle, and Vivien [Gutachter] [Petras. "Semantic Web Identity of academic organizations : search engine entity recognition and the sources that influence Knowledge Graph Cards in search results / Kenning Arlitsch ; Gutachter: Michael Seadle, Vivien Petras." Berlin : Humboldt Universität zu Berlin, Philosophische Fakultät I, 2017. http://d-nb.info/1124893482/34.

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Books on the topic "Semantic search engine"

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Yannis, Tzitzikas, and SpringerLink (Online service), eds. Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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Pariser, Eli. The Filter Bubble: What the Internet is Hiding From You. New York: Penguin Press, 2011.

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Google Semantic Search. Pearson Education (US), 2013.

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Shroff, Gautam. The Intelligent Web. Oxford University Press, 2013. http://dx.doi.org/10.1093/oso/9780199646715.001.0001.

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As we use the Web for social networking, shopping, and news, we leave a personal trail. These days, linger over a Web page selling lamps, and they will turn up at the advertising margins as you move around the Internet, reminding you, tempting you to make that purchase. Search engines such as Google can now look deep into the data on the Web to pull out instances of the words you are looking for. And there are pages that collect and assess information to give you a snapshot of changing political opinion. These are just basic examples of the growth of "Web intelligence", as increasingly sophisticated algorithms operate on the vast and growing amount of data on the Web, sifting, selecting, comparing, aggregating, correcting; following simple but powerful rules to decide what matters. While original optimism for Artificial Intelligence declined, this new kind of machine intelligence is emerging as the Web grows ever larger and more interconnected. Gautam Shroff takes us on a journey through the computer science of search, natural language, text mining, machine learning, swarm computing, and semantic reasoning, from Watson to self-driving cars. This machine intelligence may even mimic at a basic level what happens in the brain.
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Sacco, Giovanni Maria, and Yannis Tzitzikas. Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience. Springer, 2012.

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Sacco, Giovanni Maria, and Yannis Tzitzikas. Dynamic Taxonomies and Faceted Search: Theory, Practice, and Experience. Springer, 2010.

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Pariser, Eli. Filter Bubble: What the Internet Is Hiding from You. Penguin Books, Limited, 2012.

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Pariser, Eli. Filter Bubble: Wie wir im Internet entmündigt werden. Hanser, Carl GmbH + Co., 2012.

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Book chapters on the topic "Semantic search engine"

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Farouk, Mamdouh, Mitsuru Ishizuka, and Danushka Bollegala. "Graph Matching Based Semantic Search Engine." In Metadata and Semantic Research, 89–100. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14401-2_8.

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Moreau, Benjamin, Patricia Serrano-Alvarado, Matthieu Perrin, and Emmanuel Desmontils. "A License-Based Search Engine." In The Semantic Web: ESWC 2019 Satellite Events, 130–35. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32327-1_26.

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Usbeck, Ricardo, Michael Röder, Peter Haase, Artem Kozlov, Muhammad Saleem, and Axel-Cyrille Ngonga Ngomo. "Requirements to Modern Semantic Search Engine." In Communications in Computer and Information Science, 328–43. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45880-9_25.

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Gómez-Pérez, Jose Manuel, Richard Benjamins, Mercedes Blázquez, Jesús Contreras, María Jesús Fernández, Diego Patón, and Luis Rodrigo. "Intelligent Search Engine For Online Access To Municipal Services." In The Semantic Web, 75–93. Boston, MA: Springer US, 2007. http://dx.doi.org/10.1007/978-0-387-48531-7_4.

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España-Bonet, Cristina, Juliane Stiller, Roland Ramthun, Josef van Genabith, and Vivien Petras. "Query Translation for Cross-Lingual Search in the Academic Search Engine PubPsych." In Metadata and Semantic Research, 37–49. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14401-2_4.

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Waitelonis, Jörg, Nadine Ludwig, and Harald Sack. "Use What You Have: Yovisto Video Search Engine Takes a Semantic Turn." In Semantic Multimedia, 173–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23017-2_12.

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Pandiarajan, Sudhakar, V. M. Yazhmozhi, and P. Praveen kumar. "Semantic Search Engine Using Natural Language Processing." In Lecture Notes in Electrical Engineering, 561–71. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07674-4_53.

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Costumero, Roberto, Consuelo Gonzalo, and Ernestina Menasalvas. "TIDA: A Spanish EHR Semantic Search Engine." In Advances in Intelligent Systems and Computing, 235–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07581-5_28.

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Yu, Liyang. "A Search Engine That Supports Rich Snippets." In A Developer’s Guide to the Semantic Web, 737–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-662-43796-4_17.

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Rodrigo, L., V. R. Benjamins, J. Contreras, D. Patón, D. Navarro, R. Salla, M. Blázquez, P. Tena, and I. Martos. "A Semantic Search Engine for the International Relation Sector." In The Semantic Web – ISWC 2005, 1002–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11574620_71.

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Conference papers on the topic "Semantic search engine"

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Lv, Chaoqing, Takashi Kobayashi, Kiyoshi Agusa, Kun Wu, and Qing Zhu. "Image Semantic Search Engine." In 2009 First International Workshop on Database Technology and Applications, DBTA. IEEE, 2009. http://dx.doi.org/10.1109/dbta.2009.148.

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Manon, Quintana. "Inbenta Semantic Search Engine." In WIMS '16: International Conference on Web Intelligence, Mining and Semantics. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2912845.2912877.

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Demartini, Gianluca, and Stefan Siersdorfer. "Dear search engine: what's your opinion about...?" In the 3rd International Semantic Search Workshop. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1863879.1863883.

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Kassim, Junaidah Mohamed, and Mahathir Rahmany. "Introduction to Semantic Search Engine." In 2009 International Conference on Electrical Engineering and Informatics (ICEEI). IEEE, 2009. http://dx.doi.org/10.1109/iceei.2009.5254709.

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Chandrashekar, B. H., and G. Shobha. "Semantic domain specific search engine." In 2nd International Conference on Computer and Automation Engineering (ICCAE 2010). IEEE, 2010. http://dx.doi.org/10.1109/iccae.2010.5451718.

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Gerasimov, Nikita, Maxim Mozgovoy, and Alexey Lagunov. "Semantic sentence structure search engine." In 2014 Federated Conference on Computer Science and Information Systems. IEEE, 2014. http://dx.doi.org/10.15439/2014f343.

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Getahun, Fekade, and Genet Asefa. "Towards amharic semantic search engine." In MEDES '15: The 7th International Conference on Management of computational and collective IntElligence in Digital EcoSystems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2857218.2857235.

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Sheu, Phillip C. Y., Shu Wang, Qi Wang, Ke Hao, and Ray Paul. "Semantic Computing, Cloud Computing, and Semantic Search Engine." In 2009 IEEE International Conference on Semantic Computing (ICSC). IEEE, 2009. http://dx.doi.org/10.1109/icsc.2009.51.

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Khusro, Shah, and Irfan Ullah. "Towards a semantic book search engine." In 2016 International Conference on Open Source Systems & Technologies (ICOSST). IEEE, 2016. http://dx.doi.org/10.1109/icosst.2016.7838586.

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Vasilateanu, Andrei, Nicolae Goga, and Alin Moldoveanu. "Semantic report search engine — Questor." In 2014 18th International Conference on System Theory, Control and Computing (ICSTCC). IEEE, 2014. http://dx.doi.org/10.1109/icstcc.2014.6982404.

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Reports on the topic "Semantic search engine"

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Hawkins, Brian M. Developing a Modular Framework for Implementing a Semantic Search Engine. Fort Belvoir, VA: Defense Technical Information Center, February 2010. http://dx.doi.org/10.21236/ada555393.

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Arlitsch, Kenning. Data set supporting the dissertation Semantic Web Identity in Academic Organizations: Search engine entity recognition and the sources that influence Knowledge Graph Cards in search results. Montana State University ScholarWorks, 2016. http://dx.doi.org/10.15788/m2f590.

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