Academic literature on the topic 'Semantic web based search engines'

Paper The goal of search engines is to return accurate and complete results. Satisfying concrete user information needs becomes more and more difficult because of inability in it complete explicit specification and short comes of keyword-based searching and indexing. General search engines have indexed millions of web resources and often return thousands of results to the user query (most of them often inadequate). To increase result’s precession, users sometimes choose search engines, specialized in searching concrete domain, personalized or semantic search. A grand variety of specialized search engines may be found (and used) in the internet, but no one may guarantee finding of existing in the web and needed for the concrete user resources. In this paper we present our research on building a meta-search engine that uses domain and user profile ontologies, as well as information (or metadata), directly extracted from web sites to improve search result quality. We state main requirements to the search engine for students, PHD students and scientists, propose a conceptual model and discuss approaches of it practical realization. Our prototype metasearch engine first perform interactive semantic query refinement and then, using refined query, it automatically generate several search queries, sends them to different digital libraries and web search engines, augments and ranks returned results, using ontologically represented domain and user metadata. For testing our model, we develop domain ontologies in the electronic domain. We will use ontological terminology representation to propose recommendations for query disambiguation, and to ensure knowledge for reranking the returned results. We also present some partial initial implementations query disambiguation strategies and testing results.

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.

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.

Semantic web works on producing machine readable data and aims to deal with large amount of data. The most important tool to access the data which exist in web is the search engine. Traditional search engines are insufficient in the face of the amount of data that consists in the existing web pages. Semantic search engines are extensions to traditional engines and overcome the difficulties faced by them. This paper summarizes semantic web, concept of traditional and semantic search engines and infrastructure. Also semantic search approaches are detailed. A summary of the literature is provided by touching on the trends. In this respect, type of applications and the areas worked for are considered. Based on the data for two different years, trend on these points are analyzed and impacts of changes are discussed. It shows that evaluation on the semantic web continues and new applications and areas are also emerging. Multimedia retrieval is a newly scope of semantic. Hence, multimedia retrieval approaches are discussed. Text and multimedia retrieval is analyzed within semantic search.

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

The billions of databases, worldwide, provide an increasing amount of information to the people. As a result, the researchers have to seek knowledge about the resources, which were generically known as the search engines. One such search technique that is popularly used is the semantic search technique which improves the search accuracy by determining the purpose of the search along with the contextual meaning of the terms which appeared in the data space or the web for generating accurate results. Many search engines exist today, which makes it difficult to collect useful data. In this paper, many types of research depended which prepared to describe the Semantic Search and Semantic Web techniques. Various types of semantic search engines are investigated and the differences between the Semantic Search and Semantic Search keywords are determined. Additionally, the benefits of using Semantic Search were highlighted. The literature review and the findings of the case study helped in understanding the new constructs. These researches also determined the relationship between the new and the previously existing constructs based on their perspective regarding the extension of Bedny's activity theory with regards to the SEO promotion techniques. They added the functional consequences of extending Bedny's activity theory faced by the promotion managers. The researchers have summarised the history of semantic search and its global position in search engine generation. The researchers also highlighted the role played by the search engines in the semantic search and smart web technologies.

Recently search services have been developed rapidly especially when the social internet appeared. It can help web users easily find their documents. So that it is very difficult to find a best search method. This paper aims to enhance the quality of the search engines results and this can be done by adding a second level category search that is able to search for the keyword and its synonyms, which enables the search engines to get more users queries related results. The proposed method showed promising results that will open further research directions

In the current technology driven world, information retrieval activities are in almost every aspect of daily, as society uses popular web search engines like Google, Yahoo!, Live Search, Ask, and so forth to obtain helpful information. Often, these popular search engines look for and obtain key information; however, not all of the retrieved items are relevant in context to the search target a. Thus, it is left for the user to filter out unwanted information, using only a few information items left from the search results. These popular web search engines use a first generation search service based on “static keywords”, which require the users to know exactly what they want to search and enter the right keywords. This approach puts the user at a disadvantage. In this paper, the authors investigate and design a dynamic, question-answer search engine that enables searching by attributes for more precise and relevant information in Electronic Medical Record (EMR) field.

Search engine is critical in people’s daily life because it determines the information quality people obtain through searching. Fierce competition for the ranking in search engines is not conducive to both users and search engines. Existing research mainly studies the content and links of websites. However, none of these techniques focused on semantic analysis of link and anchor text for detection. In this paper, we propose a web spam detection method by extracting novel feature sets from the homepage source code and choosing the random forest (RF) as the classifier. The novel feature sets are extracted from the homepage’s links, hypertext markup language (HTML) structure, and semantic similarity of content. We conduct experiments on the WEBSPAM-UK2007 and UK-2011 dataset using a five-fold cross-validation method. Besides, we design three sets of experiments to evaluate the performance of the proposed method. The proposed method with novel feature sets is compared with different indicators and has better performance than other methods with a precision of 0.929 and a recall of 0.930. Experiment results show that the proposed model could effectively detect web spam.

The authors investigate user requirements regarding the interface design for semantic multimedia search and retrieval based on a prototypical implementation of a search engine for multimedia content on the web. Thus, unlike existing image or video search engines, they are interested in true multimedia content combining different media assets into multimedia documents like PowerPoint presentations and Flash files. In a user study with 20 participants, the authors conducted a formative evaluation based on the think-aloud method and semi-structured interviews in order to obtain requirements to a future web search engine for multimedia content. The interviews are complemented by a paper-and-pencil questionnaire to obtain quantitative information. As a result, the authors elicit requirements to a web search engine for multimedia content. Among them, scalability and personalization of the presented information are identified as the main goals. Based on the requirements, they present mockups demonstrating the user interface of a future multimedia search and retrieval engine.

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.

O ReQuest é um motor de pesquisa para buscas semânticas em domínios específicos. Oferece aos seus utilizadores a possibilidade de realizarem pesquisas integrando ontologias e ficheiros de descrição de recursos (RDF). O ReQuest foi criado para permitir avaliar comparativamente os resultados obtidos em pesquisas semânticas com os das pesquisas em recuperacão de informacão clássica. Os resultados da avaliacão por utilizadores do ReQuest no domínio da informacão noticiosa revelaram que a Web Semântica permite melhorar as pesquisas. ReQuest is a semantic search engine for specialized domains. It offers searches based on ontologies and resource description files (RDF). ReQuest was built to evaluate semantic searches against classic Information Retrieval searches. The results of a user survey in the newsdomain showed that the Semantic Web can improve searches

Dissertação para obtenção do Grau de Mestre em Engenharia Informática<br>The volume of digital information is increasingly larger and even though organiza-tions are making more of this information available, without the proper tools users have great difficulties in retrieving documents about subjects of interest. Good infor-mation retrieval mechanisms are crucial for answering user information needs. Nowadays, search engines are unavoidable - they are an essential feature in docu-ment management systems. However, achieving good relevancy is a difficult problem particularly when dealing with specific technical domains where vocabulary mismatch problems can be prejudicial. Numerous research works found that exploiting the lexi-cal or semantic relations of terms in a collection attenuates this problem. In this dissertation, we aim to improve search results and user experience by inves-tigating the use of potentially connected Web vocabularies in information retrieval en-gines. In the context of open Web-based SKOS vocabularies we propose a query expan-sion framework implemented in a widely used IR system (Lucene/Solr), and evaluated using standard IR evaluation datasets. The components described in this thesis were applied in the development of a new search system that was integrated with a rapid applications development tool in the context of an internship at Quidgest S.A.<br>Fundação para a Ciência e Tecnologia - ImTV research project, in the context of the UTAustin-Portugal collaboration (UTA-Est/MAI/0010/2009); QSearch project (FCT/Quidgest)

During the last decade, the Internet has reinvented the music industry. Physical media have evolved towards online products and services. As a consequence of this transition, online music corpora have reached a massive scale and are constantly being enriched with new documents. At the same time, a great quantity of cultural heritage content remains undisclosed because of the lack of metadata to describe and contextualize it. This has created a need for music retrieval and discovery technologies that allow users to interact with all these music repositories efficiently and effectively. Music Information Retrieval (MIR) is the research field that studies methods and tools for improving such interaction as well as access to music documents. Most of the research works in MIR focuses on content-based approaches, which exploit the analysis of the audio signal of a song to extract significant descriptors of the music content. These content descriptors may be processed and used in different application scenarios, such as retrieval, recommendation, dissemination, musicology analysis, and so on. The thesis explores novel automatic (content-based) methodologies for music retrieval which are based on semantic textual descriptors, acoustic similarity, and a combination of the two; we show empirically how the proposed approaches lead to efficient and competitive solutions with respect to other alternative state-of-the-art strategies. Part of the thesis focuses on music discovery systems, that is search engines where users do not look for a specific song or artist, but may have some general criteria they wish to satisfy. These criteria are commonly expressed in the form of tags, that is short phrases that capture relevant characteristics of the songs, such as genre, instrumentation, emotions, and so on. Because of the scale of current collections, manually assigning tags to songs is becoming an infeasible task; for this reason the automatic tagging of music content is now considered a core challenge in the design of fully functional music retrieval systems. State-of-the-art content-based systems for music annotation (which are usually called auto-taggers) model the acoustic patterns of the songs associated with each tag in a vocabulary through machine learning approaches. Based on these tag models, auto-taggers generate a vector of tag weights when annotating a new song. This vector may be interpreted as a semantic multinomial (SMN), that is a distribution characterizing the relevance of each tag to a song, which can be used for music annotation and retrieval. A first original contribution reported in the thesis aims at improving state-of-the-art auto-taggers by considering tag co-occurrences. While a listener may derive semantic associations for audio clips from direct auditory cues (e.g. hearing “bass guitar”) as well as from context (e.g. inferring “bass guitar” in the context of a “rock” song), auto-taggers ignore this context. Indeed, although contextual relationships correlate tags, many state-of-the-art auto-taggers model tags independently. We present a novel approach for improving automatic music annotation by modeling contextual relationships between tags. A Dirichlet mixture model (DMM) is proposed as a second, additional stage in the modeling process to supplement any auto-tagging system that generates a semantic multinomial over a vocabulary of tags. For each tag in the vocabulary, a DMM captures the broader context defined by the tag by modeling tag co-occurrence patterns in the SMNs of songs associated with the tag. When annotating songs, the DMMs refine SMN annotations by leveraging contextual evidence. Experimental results demonstrate the benefits of combining a variety of auto-taggers with this generative context model; it generally outperforms other approaches to context modeling as well. The use of tags alone allows for efficient and effective music retrieval mechanisms; however, automatic tagging strategies may lead to noisy representations that may negatively affect the effectiveness of retrieval algorithms. Yet, search and discovery operations across music collections can be also carried out matching users interests or exploiting acoustic similarity. One major issue in music information retrieval is how to combine such noisy and heterogeneous information sources in order to improve retrieval effectiveness. At this aim, the thesis explores a statistical retrieval framework based on combining tags and acoustic similarity through a hidden Markov model. The retrieval mechanism relies on an application of the Viterbi algorithm which highlights the sequence of songs that best represents a user query. The model is presented for improving state-of-the-art music search and discovery engines by delivering more relevant ranking lists. In fact, through an empirical evaluation we show how the proposed model leads to better performances than retrieval approaches which rank songs according to individual information sources alone or which use a combination of them. Additionally, the high generality of the framework makes it suitable for other media as well, such as images and videos. Besides music discovery, the thesis challenges also the problem of music identification, the goal which is to match different recordings of the same songs (i.e. finding covers of a given query). At this aim we present two novel music descriptors based on the harmonic content of the audio signals. Their main purpose is to provide a compact representation which is likely to be shared by different performances of the same music score. At the same time, they also aim at reducing the storage requirements of the music representation as well as enabling efficient retrieval over large music corpora. The effectiveness of these two descriptors, combined in a single scalable system, has been tested for classical music identification, which is probably the applicative scenario that mostly needs automatic strategies for labeling unknown recordings. Scalability is guaranteed by an index-based pre-retrieval step which handles music features as textual words; in addition, precision in the identification is brought by alignment carried out through an application of hidden Markov models. Results with a collection of more than ten thousand recordings have been satisfying in terms of efficiency and effectiveness.<br>Nell’ultimo decennio l’avvento di Internet ha reinventato l’industria musicale, in particolare i supporti fisici si sono evoluti verso prodotti e servizi reperibili online. Questa transizione ha portato le collezioni musicali disponibili su Internet ad avere dimensioni enormi e in continua crescita, a causa del quotidiano inserimento di nuovo contenuto musicale. Allo stesso tempo, una buona parte dei documenti musicali tipici del patrimonio culturale rimane inaccessibile, a causa della mancanza di dati che li descrivano e li contestualizzino. Tutto ciò evidenzia la necessità di nuove tecnologie che permettano agli utenti di interagire con tutte queste collezioni musicali in modo effettivo ed efficiente. Il reperimento d’informazioni musicali (i.e. MIR) è il settore di ricerca che studia le tecniche e gli strumenti per migliorare sia questa interazione, sia l’accesso ai documenti musicali. La maggior parte della ricerca effettuata nel MIR riguarda tecniche automatiche basate sul contenuto (i.e. content-based), le quali analizzano il segnale audio di una canzone ed estraggono dei descrittori, che ne caratterizzano, appunto, il contenuto. Questi descrittori possono essere elaborati ed utilizzati in varie applicazioni: motori di ricerca, divulgazione, analisi musicologa e così via. La tesi presenta dei modelli originali content-based per motori di ricerca musicali di vario genere, che si basano, sia su descrittori semantici testuali e su similarità acustica, sia su una loro combinazione. Attraverso esperimenti pratici, dimostreremo come i modelli proposti ottengano prestazioni efficienti e competitive se confrontate con alcuni dei sistemi alternativi presenti nello stato dell’arte. Una buona parte della tesi si concentra sui sistemi di music discovery, ovvero motori di ricerca nei quali gli utenti non cercano una canzone o un’artista specifico, ma hanno perlopiù un criterio generale che vogliono soddisfare. Questi criteri di ricerca sono in genere espressi sottoforma di tag, ovvero annotazioni che caratterizzano gli aspetti rilevanti delle canzoni (e.g. genere, strumenti, emozioni). A causa delle dimensioni raggiunte ormai dalle varie collezioni, l’assegnazione manuale dei tag alle canzoni è però diventata un’operazione impraticabile. Per questa ragione, i modelli che assegnano i tag in modo automatico sono diventati dei punti chiave nella progettazione dei motori di ricerca musicale. I sistemi content-based per l’assegnazione automatica di tag (i.e. auto-tagger) generalmente si basano su approcci di machine learning, che modellano le caratteristiche audio delle canzoni associate ad un certo tag. Questi modelli sono poi utilizzati per annotare le nuove canzoni generando un vettore di pesi, uno per ogni tag nel vocabolario, che misurano la rilevanza che ogni tag ha per quella canzone (i.e. SMN). Un primo contributo originale della tesi ha l’obiettivo di migliorare lo stato dell’arte degli auto-tagger, modellando le co-occorrenze tra i tag. Infatti mentre una persona può associare tag a una canzone sia direttamente (e.g. ascolta lo strumento“basso”), sia dal contesto (e.g. intuisce“basso” sapendo che la canzone `e di genere “rock”), gli auto-tagger diversamente ignorano questo contesto. Infatti, nonostante le relazioni contestuali correlino i tag, la maggior parte degli auto-tagger modella ogni tag in modo indipendente. Il nostro sistema pertanto cerca di migliorare l’assegnazione automatica di tag, modellando le relazioni contestuali che occorrono tra i vari tag di un vocabolario. Per far questo utilizziamo un modello di misture di Dirichlet (DMM) al fine di migliorare qualsiasi auto-tagger che genera delle SMN. Per ogni tag nel vocabolario, una DMM è usata per catturare le co-occorrenze con gli altri tag nelle SMN delle canzoni associate con quel tag. Quando una nuova canzone è annotata, il DMM rifinisce le SMN prodotte da un auto-tagger sfruttando le sue caratteristiche contestuali. I risultati sperimentali dimostrano i benefici di combinare vari auto-tagger con le DMM; in aggiunta, i risultati migliorano rispetto anche a quelli ottenuti con modelli contestuali alternativi dello stato dell’arte. L’uso dei tag permette di costruire efficienti ed effettivi motori di ricerca musicali; tuttavia le strategie automatiche per l’assegnazione di tag a volte ottengono rappresentazioni non precise che possono influenzare negativamente le funzioni di reperimento. Al tempo stesso, le ricerca di documenti musicali può essere anche fatta confrontando gli interessi degli utenti o sfruttando le similarit`a acustiche tra le canzoni. Uno dei principali problemi aperti nel MIR è come combinare tutte queste diverse informazioni per migliorare le funzioni di ricerca. Ponendosi questo obiettivo, la tesi propone un modello di reperimento statistico basato sulla combinazione tra i tag e la similarità acustica mediante un modello di Markov nascosto. Il meccanismo di ricerca si basa su un’applicazione dell’algoritmo di Viterbi, il quale estrae dal modello la sequenza di canzoni che meglio rappresenta la query. L’obiettivo è di migliorare lo stato dell’arte dei sistemi di ricerca musicale e, in particolare, di music discovery fornendo all’utente liste di canzoni maggiormente rilevanti. Gli esperimenti infatti mostrano come il modello proposto risulta migliore sia di algoritmi che ordinano le canzoni utilizzando un’informazione sola, sia di quelli che le combinano in modo diverso. In aggiunta, l’alta generalità a del modello lo rende adatto anche ad altri settori multimediali, come le immagini e i video. In parallelo con i sistemi di music discovery, la tesi affronta anche il problema di identificazione musicale (i.e. music identification), il cui obiettivo è quello di associare tra loro diverse registrazioni audio che condividono lo stesso spartito musicale (i.e. trovare le versioni cover di una certa query). In funzione di questo, la tesi presenta due descrittori che si basano sulla progressione armonica della musica. Il loro scopo principale è quello di fornire una rappresentazione compatta del segnale audio che possa essere condivisa dalle canzoni aventi lo stesso spartito musicale. Al tempo stesso, mirano anche a ridurre lo spazio di memoria occupato e a permettere operazioni di ricerca efficienti anche in presenza di grandi collezioni. La validità dei due descrittori è stata verificata per l’identificazione di musica classica, ovvero lo scenario che maggiormente necessita di strategie automatiche per la gestione di registrazioni audio non catalogate. La scalabilità del sistema è garantita da una pre-ricerca basata su un indice che gestisce i descrittori musicali come fossero parole di un testo; in aggiunta, la precisione dell’identificazione è aumentata mediante un’operazione di allineamento eseguita utilizzando i modelli di Markov nascosti. I risultati sperimentali ottenuti con una collezione di più di diecimila registrazioni audio sono stati soddisfacenti sia da un punto di vista di efficienza sia di efficacia.

Semantic Web Identity kennzeichnet den Zustand, in dem ein Unternehmen von Suchmaschinen als Solches erkannt wird. Das Abrufen einer Knowledge Graph Card in Google-Suchergebnissen für eine akademische Organisation wird als Indikator für SWI nominiert, da es zeigt, dass Google nachprüfbare Tatsachen gesammelt hat, um die Organisation als Einheit zu etablieren. Diese Anerkennung kann wiederum die Relevanz ihrer Verweisungen an diese Organisation verbessern. Diese Dissertation stellt Ergebnisse einer Befragung der 125 Mitgliedsbibliotheken der Association of Research Libraries vor. Die Ergebnisse zeigen, dass diese Bibliotheken in den strukturierten Datensätzen, die eine wesentliche Grundlage des Semantic Web sind und Faktor bei der Erreichung der SWI sind, schlecht vertreten sind. Der Mangel an SWI erstreckt sich auf andere akademische Organisationen, insbesondere auf die unteren Hierarchieebenen von Universitäten. Ein Mangel an SWI kann andere Faktoren von Interesse für akademische Organisationen beeinflussen, einschließlich der Fähigkeit zur Gewinnung von Forschungsförderung, Immatrikulationsraten und Verbesserung des institutionellen Rankings. Diese Studie vermutet, dass der schlechte Zustand der SWI das Ergebnis eines Versagens dieser Organisationen ist, geeignete Linked Open Data und proprietäre Semantic Web Knowledge Bases zu belegen. Die Situation stellt eine Gelegenheit für akademische Bibliotheken dar, Fähigkeiten zu entwickeln, um ihre eigene SWI zu etablieren und den anderen Organisationen in ihren Institutionen einen SWI-Service anzubieten. Die Forschung untersucht den aktuellen Stand der SWI für ARL-Bibliotheken und einige andere akademische Organisationen und beschreibt Fallstudien, die die Wirksamkeit dieser Techniken zur Verbesserung der SWI validieren. Die erklärt auch ein neues Dienstmodell der SWI-Pflege, die von anderen akademischen Bibliotheken für ihren eigenen institutionellen Kontext angepasst werden.<br>Semantic Web Identity (SWI) characterizes an entity that has been recognized as such by search engines. The display of a Knowledge Graph Card in Google search results for an academic organization is proposed as an indicator of SWI, as it demonstrates that Google has gathered enough verifiable facts to establish the organization as an entity. This recognition may in turn improve the accuracy and relevancy of its referrals to that organization. This dissertation presents findings from an in-depth survey of the 125 member libraries of the Association of Research Libraries (ARL). The findings show that these academic libraries are poorly represented in the structured data records that are a crucial underpinning of the Semantic Web and a significant factor in achieving SWI. Lack of SWI extends to other academic organizations, particularly those at the lower hierarchical levels of academic institutions, including colleges, departments, centers, and research institutes. A lack of SWI may affect other factors of interest to academic organizations, including ability to attract research funding, increase student enrollment, and improve institutional reputation and ranking. This study hypothesizes that the poor state of SWI is in part the result of a failure by these organizations to populate appropriate Linked Open Data (LOD) and proprietary Semantic Web knowledge bases. The situation represents an opportunity for academic libraries to develop skills and knowledge to establish and maintain their own SWI, and to offer SWI service to other academic organizations in their institutions. The research examines the current state of SWI for ARL libraries and some other academic organizations, and describes case studies that validate the effectiveness of proposed techniques to correct the situation. It also explains new services that are being developed at the Montana State University Library to address SWI needs on its campus, which could be adapted by other academic libraries.

The main purpose of this research is to find an effective way to personalise information searching on the Internet using middleware search agents, namely, Personalised Search Agents (PSA). The PSA acts between users and search engines, and applies new and existing techniques to mine and exploit relevant and personalised information for users. Much research has already been done in developing personalising filters, as a middleware technique which can act between user and search engines to deliver more personalised results. These personalising filters, apply one or more of the popular techniques for search result personalisation, such as the category concept, learning from user actions and using metasearch engines. By developing the PSA, these techniques have been investigated and incorporated to create an effective middleware agent for web search personalisation. In this thesis, a conceptual model for the Personalised Search Agent is developed, implemented by developing a prototype and benchmarked the prototype against existing web search practices. System development methodology which has flexible and iterative procedures that switch between conceptual design and prototype development was adopted as the research methodology. In the conceptual model of the PSA, a multi-layer client server architecture is used by applying generalisation-specialisation features. The client and the server are structurally the same, but differ in the level of generalisation and interface. The client handles personalising information regarding one user whereas the server effectively combines the personalising information of all the clients (i.e. its users) to generate a global profile. Both client and server apply the category concept where user selected URLs are mapped against categories. The PSA learns the user relevant URLs both by requesting explicit feedback and by implicitly capturing user actions (for instance the active time spent by the user on a URL). The PSA also employs a keyword-generating algorithm, and tries different combinations of words in a user search string by effectively combining them with the relevant category values. The core functionalities of the conceptual model for the PSA, were implemented in a prototype, used to test the ideas in the real word. The result was benchmarked with the results from existing search engines to determine the efficiency of the PSA over conventional searching. A comparison of the test results revealed that the PSA is more effective and efficient in finding relevant and personalised results for individual users and possesses a unique user sense rather than the general user sense of traditional search engines. The PSA, is a novel architecture and contributes to the domain of knowledge web information searching, by delivering new ideas such as active time based user relevancy calculations, automatic generation of sensible search keyword combinations and the implementation of a multi-layer agent architecture. Moreover, the PSA has high potential for future extensions as well. Because it captures highly personalised data, data mining techniques which employ case-based reasoning make the PSA a more responsive, more accurate and more effective tool for personalised information searching.

The purpose of this study was to understand the infrastructure that states have built for leadership-relevant professional learning by identifying the long-term leadership development initiatives supported by states and analyzing their purposes, target audiences, and scope. We conducted a scan between March and May of 2023 using search engines, state department of education websites, and other web-based documents. We found that at least 26 states support ongoing statewide leadership development initiatives to build the knowledge and skills of in-service leaders. This report provides examples of different states’ initiatives related to each of these functions. It also briefly addresses the federal, state, and local funding sources that states draw on to develop and sustain these initiatives.