The quest for information retrieval on the semantic web

Semantic search has been one of the motivations of the Semantic Web since it was envisioned. We propose a model for the exploitation of ontology-based KBs to improve search over large document repositories. The retrieval model is based on an adaptation of the classic vector-space model, including an annotation weighting algorithm, and a ranking algorithm. Semantic search is combined with keyword-based search to achieve tolerance to KB incompleteness. Our proposal has been tested on corpora of significant size, showing promising results with respect to keyword-based search, and providing ground for further analysis and research

Automatic Annotation and Semantic Search from Protégé

Semantic search has been one of the major envisioned benefits of the Semantic Web since its emergence in the late 90’s [1]. Our demo shows a proposal towards this goal. One way to view a semantic search engine is as a tool that gets formal queries (e.g. in RDQL, RQL, SPARQL, or the like) from a client, executes them against an ontology-based knowledge base, and returns tuples of ontology values (resources) that satisfy the query [2]. While this conception of semantic search brings enormous advantages already, our work aims at taking a step beyond this. In our view of Information Retrieval in the Semantic Web, a search engine returns documents, rather than (or in addition to) exact values, in response to user queries. The engine should rank the documents, according to concept-based relevance criteria. The overall retrieval process is illustrated in Figure 1 (see [3] for more details of our research)

Recuperación de información en la Web Semántica

Artículo FINALISTA del I Premio NováticaLa búsqueda semántica ha sido una de las motivaciones principales de la Web Semántica desde sus inicios. En este artículo proponemos un modelo para la explotación de bases de conocimiento orientadas a ontologías para mejorar la búsqueda en grandes repositorios documentales. El modelo de recuperación se basa en una adaptación del modelo vectorial clásico, con un método para la asignación de pesos a la anotación semántica de documentos, y un algoritmo de ranking o clasificación. La búsqueda semántica se combina con una búsqueda basada en palabras clave para conseguir una tolerancia a la incompletitud de las bases de conocimiento. Nuestra propuesta se ha probado en corpus de escala significativa, con resultados prometedores respecto de la búsqueda por palabra clave, y abriendo campo para el análisis y la exploración

SW @ SPAIN

Executive Summary: This report provides information about the current state of the art in Semantic Web research in Spain. The contents of this report are mainly based on the information that was gathered during the workshop "Ontologías y Web Semántica 2005", which was held in Santiago de Compostela in November 2005, and the contributions that members from most of the research groups working in Semantic-Webrelated areas in Spain have provided. The field of Semantic Web is quite widespread in Spain, with important groups developing methods, techniques and tools to support different areas: Ontological Engineering, semantic extraction and annotation from heterogeneous sources, semantic search engines, personalisation and Semantic Web Services

Self-tuning Personalized Information Retrieval in an Ontology-Based Framework

Reliability is a well-known concern in the field of personalization technologies. We propose the extension of an ontology-based retrieval system with semantic-based personalization techniques, upon which automatic mechanisms are devised that dynamically gauge the degree of personalization, so as to benefit from adaptivity but yet reduce the risk of obtrusiveness and loss of user control. On the basis of a common domain ontology KB, the personalization framework represents, captures and exploits user preferences to bias search results towards personal user interests. Upon this, the intensity of personalization is automatically increased or decreased according to an assessment of the imprecision contained in user requests and system responses before personalization is applied

Concept-based Multimedia Information Retrieval System using Ontology Search in Cultural Heritage

The richness of Cultural Heritage and Natural History is abundant. Many of the cultural heritage collection in Library, National Archive, and Museum in the form physical object or digital format in a different type of media (text, image, audio and video). One cultural heritage object can have the relationship with other objects in different media format and do not mention query term explicitly. Using the various media format causes problems in search. A monolithic search engine like Google, Bing, Google Image, Youtube, or Findsounds only retrieve one media format. Besides, the search result of the existing search engine is less relevant and incomplete in searching cultural heritage. Several multimedia information retrieval techniques used in building the relationship using ontology like ontology based search, content-based search with ontology and hybrid search with ontology. This paper proposes Concept-based Multimedia Information Retrieval System (MIRS) with ontology using Indonesia’s cultural heritage dataset to increase relevance and completeness of the system. Concept-based MIRS using manually built thesauri or by extracting latent word relationship and concept from the Ontology that provides definition and formal structure for describing the implicit and explicit concepts and its relationship in cultural heritage documentation. Ontology-based Semantic similarity measure is defined which measure the semantic relationship between document based on the likeness of their meaning. The search results indicate that the document being retrieved becomes highly relevant, more complete, enrich the keyword and in varying media formats when is compared to existing search engine results such google, bing, google image, youtube and findsounds in specific domain

Rab-KAMS: A reproducible knowledge management system with visualization for preserving Rabbit Farming and Production Knowledge

The sudden rise in rural-to-urban migration has been a key challenge threatening food security and most especially the survival of Rabbit Farming and Production (RFP) in Sub-Saharan Africa. Currently, significant knowledge of RFP is going into extinction as evident by the drastic fall in commercial rabbit farming and production indices. Hence, the need for a system to proactively preserve RFP knowledge for future potential farmers cannot be overemphasized. To this end, knowledge archiving and management are key concepts of ensuring long-term digital storage of conceptual blueprints and specifications of systems, methods and frameworks with capacity for future updates while making such information readily accessible to relevant stakeholders on demand. Therefore, a reproducible Rabbit production' Knowledge Archiving and Management System (Rab-KAMS) is developed in this paper. A 3-staged approach was adopted to develop the Rab-KAMS. This include a knowledge gathering and conceptualization stage; a knowledge revision stage to validate the authenticity and relevance of the gathered knowledge for its intended purpose and a prototype design stage adopting the use of unified modelling language conceptual workflows, ontology graphs and frame system. For seamless accessibility and ubiquitous purposes, the design was implemented into a mobile application having interactive end-users' interfaces developed using XML and Java in Android 3.0.2 Studio development environment while adopting the V-shaped software development model. The qualitative evaluation results obtained for Rab-KAMS based on users' rating and reviews indicate a high level of acceptability and reliability by the users. It also indicates that relevant RFP knowledge were correctly captured and provided in a user-friendly manner. The developed Rab-KAMS could offer seamless acquisition, representation, organization and mining of new and existing verified knowledge about RFP and in turn contributing to food security