ImageSieve: Exploratory search of museum archives with named entity-based faceted browsing

Over the last few years, faceted search emerged as an attractive alternative to the traditional "text box" search and has become one of the standard ways of interaction on many e-commerce sites. However, these applications of faceted search are limited to domains where the objects of interests have already been classified along several independent dimensions, such as price, year, or brand. While automatic approaches to generate faceted search interfaces were proposed, it is not yet clear to what extent the automatically-produced interfaces will be useful to real users, and whether their quality can match or surpass their manually-produced predecessors. The goal of this paper is to introduce an exploratory search interface called ImageSieve, which shares many features with traditional faceted browsing, but can function without the use of traditional faceted metadata. ImageSieve uses automatically extracted and classified named entities, which play important roles in many domains (such as news collections, image archives, etc.). We describe one specific application of ImageSieve for image search. Here, named entities extracted from the descriptions of the retrieved images are used to organize a faceted browsing interface, which then helps users to make sense of and further explore the retrieved images. The results of a user study of ImageSieve demonstrate that a faceted search system based on named entities can help users explore large collections and find relevant information more effectively

Semantic Faceted Search: Safe and Expressive Navigation in RDF Graphs

Faceted search and querying are the two main paradigms to search the Semantic Web. Querying languages, such as SPARQL, oer expressive means for searching knowledge bases, but they are dicult to use. Query assistants help users to write well-formed queries, but they do not prevent empty results. Faceted search supports exploratory search, i.e., guided navigation that returns rich feedbacks to users, and prevents them to make navigation steps that lead to empty results (dead-ends). However, faceted search systems do not oer the same expressiveness as query languages. We introduce semantic faceted search, the combination of an expressive query language and faceted search to reconcile the two paradigms. The query language is basically SPARQL, but with a syntax that extends Turtle with disjunction and negation, and that better ts in a faceted search interface: LISQL. We formalize the navigation of faceted search as a navigation graph, where nodes are queries, and navigation links are query transformations. We prove that this navigation graph is safe (no dead-end), and complete (every query that is not a dead-end can be reached by navigation). That formalization itself is a contribution to faceted search. A prototype, Camelis 2, has been implemented, and a usability evaluation with graduate students demonstrated that semantic faceted search retains the ease-of-use of faceted search, and enables most users to build complex queries with little training

Using Document Indexers for Faceted Search in Dataspaces

Efficient information retrieval is essential to enrich user experience when searching for documents in dataspaces. With the continued growth in the volume and complexity of documents, the efficient information retrieval for searches has become increasingly challenging. To improve users’ search experience, faceted search combines direct keyword search methods with faceted browsing using a predefined set of categories (facets). This paper studies a faceted search approach that integrates dynamic facets generation with search. To further enhance the faceted search, alternative indexers based on pre-defined ontology for data repositories within dataspaces are evaluated in terms of execution time and data size. Experimental results suggest that combining the proposed faceted search with appropriate indexers improves search performance enhancing user experience

Collaborative personalised dynamic faceted search

Information retrieval systems are facing challenges due to the overwhelming volume of available information online. It leads to the need of search features that have the capability to provide relevant information for searchers. Dynamic faceted search has been one of the potential tools to provide a list of multiple facets for searchers to filter their contents. However, being a dynamic system, some irrelevant or unimportant facets could be produced. To develop an effective dynamic faceted search, personalised facet selection is an important mechanism to create an appropriate personalised facet list. Most current systems have derived the searchers' interests from their own profiles. However, interests from the past may not be adequate to predict current interest due to human information-seeking behaviour. Incorporating current interests from other people's opinions to predict the interests of individual person is an alternative way to develop personalisation which is called Collaborative approach. This research aims to investigate the incorporation of a Collaborative approach to personalise facet selection. This study introduces the Artificial Neural Network (ANN)-based collaborative personalisation architecture framework and Relation-aware Collaborative AutoEncoder model (RCAE) with embedding methodology for modelling and predicting the interests in multiple facets. The study showed that incorporating collaborative approach into the proposed framework for facet selection is capable to enhance the performance of personalisation model in facet selection in comparison to the state-of-the-art techniques

Extracting conclusion sections from PubMed abstracts for rapid key assertion integration in biomedical research

Key assertions are extracted from “conclusions” sections of PubMed abstracts and
converted into Semantic Web / Linked Data format. The results are made accessible via
files, a SPARQL endpoint, and a faceted search interface. Conclusion sections are
identified as valuable resources for machine-augmented key assertion identification and
integration in the biomedical domain. Results are discussed and opportunities for future
work and cooperation are highlighted.
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Analisis dan Implementasi Semantic Faceted Search dalam Kategori Musik dengan Database YAGO

Di era informasi ini, kita perlu mengetahui bagaimana cara untuk merepresentasikan, mengakses, dan menggunakan informasi. Salah satu pendekatan yang dapat digunakan untuk mencari informasi adalah faceted search. Faceted search merupakan sebuah teknik pencarian informasi dengan mengklasifikasikan setiap informasi dalam beberapa dimensi eksplisit bernama facet. Walaupun faceted search sudah menjadi teknologi komersial yang umum digunakan, model tradisional memberikan beberapa kendala dalam cara merepresentasikan faceted metadata dan memformulasikan kueri pencarian. Model faceted search konvensional mengasumsi bahwa dokumen-dokumen tidak berhubungan dengan satu sama lain, sehingga terdapat kendala ketika pencarian yang dilakukan membutuhkan hubungan antara dua atau lebih dokumen. Penelitian ini mencoba untuk membangun prototipe dari faceted search yang dibantu dengan teknologi semantic web untuk mengatasi kendala ini. Prototipe ini dibangun dengan database YAGO3 yang dihubungkan dengan ontologi DBpedia sebagai basis pengetahuan. Setelah prototipe selesai dibangun, pengujian prototipe dilakukan dalam hal keakurasian pencarian yang dapat dilakukan. Hasil dari pengujian yang dilakukan memperlihatkan bahwa SemFacet dapat dijadikan sebagai solusi terhadap kendala-kendala yang dihadapi oleh model faceted search konvensional dan memiliki akurasi yang cukup tinggi dalam memberikan hasil pencarian

Semantic Categorization Of Online Video

As internet users are increasing day by day, the users of video-sharing site are also increasing. Video-sharing is becoming more and more popular in e-learing, but the current famous websites like youtube are not structured when it come to serving the purpose of providing educational videos for preschool and high school students. There is a need to fill building more educationally focused video site, where the content is more structured, easy to use, support both direct search and browsing, and follow a particular curriculum for preschool and high school students. This report discuss the issues like categorization and search interface of these sites and propose alternatives to existing ones out there. In this project, I have built an educational website for preschool, high school, and college level students concentrating on improved categorization and search interface of the site. This report provides detail description of my system and the results of comparison between my site and youtube. supraj