Faceted Search

Knowledge graphs such as Yago and Freebase have become a powerful asset for enhancing search, and are being intensively used in both academia and industry. Many existing knowledge graphs are either available as Linked Open Data, or they can be exported as RDF datasets enhanced with background knowledge in the form of an OWL 2 ontology. Faceted search is the de facto approach for exploratory search in many online applications, and has been recently proposed as a suitable paradigm for querying RDF repositories. In this paper, we provide rigorous theoretical underpinnings for faceted search in the context of RDF-based knowledge graphs enhanced with OWL 2 ontologies. We identify well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establish the computational complexity of answering such queries. We also study the problem of updating faceted interfaces, which is critical for guiding users in the formulation of meaningful queries during exploratory search. We have implemented our approach in a fully-fledged faceted search system, SemFacet, which we have evaluated over the Yago knowledge graph.

The NRAO faced performance and usability issues after releasing a single-search-box ("Google-like") web application to query data across all NRAO telescope archives. Running queries with several relations across multiple databases proved to be very expensive in compute resources. An investigation for a better platform led to Solr and Blacklight, a solution stack which allows in-house development to focus on in-house problems. Solr is an Apache project built on Lucene to provide a modern search server with a rich set of features and impressive performance. Blacklight is a web user interface (UI) for Solr primarily developed by libraries at the University of Virginia and Stanford University. Though Blacklight targets libraries, it is highly adaptable for many types of search applications which benefit from the faceted searching and browsing, minimal configuration, and flexible query parsing of Solr and Lucene. The result: one highly reused codebase provides for millisecond response times and a flexible UI. Not just for observational data, NRAO is rolling out Solr and Blacklight across domains of library databases, telescope proposals, and more -- in addition to telescope data products, where integration with the Virtual Observatory is on-going.

""As map-based visualizations of documents become more ubiquitous, there is a greater need for them to support intellectual and creative high-level cognitive activities with collections of non-cartographic materials — documents. This dissertation concerns the conceptualization of map-based visualizations as tools for sensemaking and collection understanding. As such, map-based visualizations would help people use georeferenced documents to develop understanding, gain insight, discover knowledge, and construct meaning.

This dissertation explores the role of graphical representations (such as maps, Kohonen maps, pie charts, and other) and interactions with them for developing map-based visualizations capable of facilitating sensemaking activities such as collection understanding. While graphical representations make document collections more perceptually and cognitively accessible, interactions allow users to adapt representations to users’ contextual needs. By interacting with representations of documents or collections and being able to construct representations of their own, people are better able to make sense of information, comprehend complex structures, and integrate new information into their existing mental models. In sum, representations and interactions may reduce cognitive load and consequently expedite the overall time necessary for completion of sensemaking activities, which typically take much time to accomplish.

The dissertation proceeds in three phases. The first phase develops a conceptual framework for translating ontological properties of collections to representations and for supporting visual tasks by means of graphical representations. The second phase concerns the cognitive benefits of interaction. It conceptualizes how interactions can help people during complex sensemaking activities. Although the interactions are explained on the example of a prototype built with Google Maps, they are independent of Google Maps and can be applicable to various other technologies. The third phase evaluates the utility, analytical capabilities and usability of the additional representations when users interact with a visualization prototype – VIsual COLlection EXplorer. The findings suggest that additional representations can enhance understanding of map-based visualizations of library collections: specifically, they can allow users to see trends, gaps, and patterns in ontological properties of collections.""

The newer version of VICOLEX is available at: http://abuchel.physics.uwo.ca/~obuchel/VICOLEX/map4.php

A case study about the automatic creation of faceted class numbers for special topics starting from data available on the semantic web is described. The aim is to investigate two questions: 1) Is the automatic, quick, cost-effective, large-scale production of a Schedule of Colon Classification class numbers for literary classics possible?, and 2) More generally: which are the requirements for an automatic production of class numbers of a faceted scheme of classification? What kind of research is necessary to develop such a process? The case study focuses on data extracted from data.bnf.fr and on their reuse to get correct and complete Colon Classification class numbers for a sample of Italian literature writers (born from 1800 to 1900). The process of creation of class numbers starting from data identification and extraction, through their cleanup, transformation and translation in classified notation by means of Open Refine is presented. Case study results and their relevance for starting questions are discussed and research questions for possible future developments are identified.

Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and
heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems
operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing
with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the
availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints
for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly
scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for
information abstraction and summarization. Further, they must take into account different user-defined exploration scenarios
and user preferences. In this work, we present a generic model for personalized multilevel exploration and analysis over large
dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently
constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale
model. We define two versions of this structure, that adopt different data organization approaches, well-suited to exploration and
analysis context. In the proposed structure, statistical computations can be efficiently performed on-the-fly. Considering different
exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction
and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough
theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based
prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets. Finally, we
provide a performance evaluation and a empirical user study employing real datasets.

The research aim was to develop an understanding of information need characteristics for word co-occurrence-based search result filters (facets). No prior research has been identified into what enterprise searchers may find useful for exploratory search and why. Various word co-occurrence techniques were applied to results from sample queries performed on industry membership content. The results were used in an international survey of 54 practising petroleum engineers from 32 organizations. Subject familiarity, job role, personality and query specificity are possible causes for survey response variation. An information needs model is presented: Broad, Rich, Intriguing, Descriptive, General, Expert and Situational (BRIDGES). This may help professionals to more effectively meet their information needs and stimulate new needs, improving a system’s ability to facilitate serendipity. This research has implications for faceted search in enterprise search and digital library deployments.

For some years now, the faceted exploratory search has faced tremendous challenges to devise a simple, instantly understandable, and usable user interface to explore the vast amount of data and information available on the Internet. In our previous research, we have indicated certain burdens with some of the existing approaches. Therefore, in this research, we are trying to uncover the attributes that influence users to use the faceted feature of the exploratory search technique. In this paper, we presented several key elements of existing search engine systems, the best of their major features were explored, and guidelines that will help to improve the usability of faceted exploratory search have been suggested and offers a further understanding of the fundamental characteristics of the faceted search, which shows the motivations to use the search, also presented the main parts of a pluggable search system.

In this paper we present a project regarding the possible use of multiple and interconnected OWL ontologies (GO!, HiCo, and Proles) in order to explore the semantic content of heterogeneous digital collections (a digital library, a full-text scholarly edition, and a relational database) in the cultural heritage domain (Geolat, Vespasiano da Bisticci Letters, and Zeri photo archive). The aim is to discover knowledge by revealing, through facets, possible latent connections – or even contradictory statements – between data, moving from person, places and dates in an event-centric dimension determined by a contextoriented perspective.

Applying best available evidences to clinical decision making requires medical research sharing and (re)using. Recently, computer assisted medical decision making is taking advantage of Semantic Web technologies. In particular, the power of ontologies allows to share medical research and to provide suitable support to the physician's practices. This paper describes a system, named ODINO (Ontological Disease kNOwledge), aimed at supporting medical decision making through semantic based modeling of medical knowledge base. The system defines an ontology model able to represent relations between medical disease and its symptomatology in a qualitative manner by using fuzzy labels. Medical knowledge is defined according with physician experts members of INMP (National Institute for Health Migration and Poverty). The main aim of ODINO is to provide an effective user interface by using ontologies and controlled vocabularies and by allowing faceted search of diseases. In particular, this work mashes the capabilities of Description Logic reasoners and information retrieval techniques in order to answer to physician's requests. Some experimental results are given in the field of dermatological diseases.

El éxito de la iniciativa de los datos abiertos enlazados ha incrementado el volumen de información disponible en la Web. Sin embargo, el contenido Web publicado siguiendo esta iniciativa, no puede ser consumido por los usuarios que desconocen las tecnologías de la Web semántica (RDF, SPARQL, Ontologías, etc.), debido a que se requiere la comprensión de la estructura, naturaleza y forma de consultar los datos, lo cual resulta complejo para los usuarios no técnicos. En este trabajo se describe el desarrollo de una aplicación Web que utiliza componentes de la arquitectura de la información y paradigmas de búsqueda, aplicados a la tarea de consumir datos enlazados por usuarios no técnicos. Estos datos se encuentran disponibles a través de un Endpoint (interfaz de acceso a una base de datos de tripletas RDF) el cual puede ser consultado vía HTTP desde un navegador Web. La propuesta de solución en desarrollo permite la búsqueda a texto completo sobre un conjunto de datos bibliográficos y...

Today's digital libraries (DLs) archive vast amounts of information
in the form of text, videos, images, data measurements,
etc. User access to DL content can rely on similarity
between metadata elements, or similarity between the data
itself (content-based similarity). We consider the problem of
exploratory search in large DLs of time-oriented data. We
propose a novel approach for overview-rst exploration of
data collections based on user-selected metadata properties.
In a 2D layout representing entities of the selected property
are laid out based on their similarity with respect to the underlying
data content. The display is enhanced by compact
summarizations of underlying data elements, and forms the
basis for exploratory navigation of users in the data space.
The approach is proposed as an interface for visual exploration,
leading the user to discover interesting relationships
between data items relying on content-based similarity between
data items and their respective metadata labels. We
apply the method on real data sets from the earth observation
community, showing its applicability and usefulness.

In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial efforts and users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages and present different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a preliminary analysis that shows that semantic enrichment of tweets is essential for faceted search on Twitter and that there is essential need for adaptive faceted search on Twitter. Furthermore, we propose an evaluation methodology that allows us to automatically evaluate the quality of adaptive faceted search on Twitter without requiring expensive user studies.

In the last few years, Twitter has become a powerful tool for publishing and discussing information. Yet, content exploration in Twitter requires substantial efforts and users often have to scan information streams by hand. In this paper, we approach this problem by means of faceted search. We propose strategies for inferring facets and facet values on Twitter by enriching the semantics of individual Twitter messages and present different methods, including personalized and context-adaptive methods, for making faceted search on Twitter more effective. We conduct a preliminary analysis that shows that semantic enrichment of tweets is essential for faceted search on Twitter and that there is essential need for adaptive faceted search on Twitter. Furthermore, we propose an evaluation methodology that allows us to automatically evaluate the quality of adaptive faceted search on Twitter without requiring expensive user studies.

Applying best available evidences to clinical decision making requires medical research sharing and (re)using. Recently, computer assisted medical decision making is taking advantage of Semantic Web technologies. In particular, the power of ontologies allows to share medical research and to provide suitable support to the physician's practices. This paper describes a system, named ODINO (Ontological Disease kNOwledge), aimed at supporting medical decision making through semantic based modeling of medical knowledge base. The system defines an ontology model able to represent relations between medical disease and its symptomatology in a qualitative manner by using fuzzy labels. Medical knowledge is defined according with physician experts members of INMP (National Institute for Health Migration and Poverty). The main aim of ODINO is to provide an effective user interface by using ontologies and controlled vocabularies and by allowing faceted search of diseases. In particular, thi...

We introduce collaborative tagging and faceted search on structured P2P systems. Since a trivial and brute force mapping of an entire folksonomy over a DHT-based system may reduce scalability, we propose an approximated graph maintenance approach. Evaluations on real data coming from Last.fm prove that such strategies reduce vocabulary noise (i.e., representation's overfitting phenomena) and hotspots issues.