Ranking Archived Documents for Structured Queries on Semantic Layers

Archived collections of documents (like newspaper and web archives) serve as important information sources in a variety of disciplines, including Digital Humanities, Historical Science, and Journalism. However, the absence of efficient and meaningful exploration methods still remains a major hurdle in the way of turning them into usable sources of information. A semantic layer is an RDF graph that describes metadata and semantic information about a collection of archived documents, which in turn can be queried through a semantic query language (SPARQL). This allows running advanced queries by combining metadata of the documents (like publication date) and content-based semantic information (like entities mentioned in the documents). However, the results returned by such structured queries can be numerous and moreover they all equally match the query. In this paper, we deal with this problem and formalize the task of "ranking archived documents for structured queries on semantic layers". Then, we propose two ranking models for the problem at hand which jointly consider: i) the relativeness of documents to entities, ii) the timeliness of documents, and iii) the temporal relations among the entities. The experimental results on a new evaluation dataset show the effectiveness of the proposed models and allow us to understand their limitation

Building and exploiting context on the web

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The Southeastern Librarian v 62, no. 3 (Fall 2014) Complete Issue

Complete issue of The Southeastern Librarian, vol. 62, no. 3

Temporal models for mining, ranking and recommendation in the Web

Due to their first-hand, diverse and evolution-aware reflection of nearly all areas of life, heterogeneous temporal datasets i.e., the Web, collaborative knowledge bases and social networks have been emerged as gold-mines for content analytics of many sorts. In those collections, time plays an essential role in many crucial information retrieval and data mining tasks, such as from user intent understanding, document ranking to advanced recommendations. There are two semantically closed and important constituents when modeling along the time dimension, i.e., entity and event. Time is crucially served as the context for changes driven by happenings and phenomena (events) that related to people, organizations or places (so-called entities) in our social lives. Thus, determining what users expect, or in other words, resolving the uncertainty confounded by temporal changes is a compelling task to support consistent user satisfaction. In this thesis, we address the aforementioned issues and propose temporal models that capture the temporal dynamics of such entities and events to serve for the end tasks. Specifically, we make the following contributions in this thesis: (1) Query recommendation and document ranking in the Web - we address the issues for suggesting entity-centric queries and ranking effectiveness surrounding the happening time period of an associated event. In particular, we propose a multi-criteria optimization framework that facilitates the combination of multiple temporal models to smooth out the abrupt changes when transitioning between event phases for the former and a probabilistic approach for search result diversification of temporally ambiguous queries for the latter. (2) Entity relatedness in Wikipedia - we study the long-term dynamics of Wikipedia as a global memory place for high-impact events, specifically the reviving memories of past events. Additionally, we propose a neural network-based approach to measure the temporal relatedness of entities and events. The model engages different latent representations of an entity (i.e., from time, link-based graph and content) and use the collective attention from user navigation as the supervision. (3) Graph-based ranking and temporal anchor-text mining inWeb Archives - we tackle the problem of discovering important documents along the time-span ofWeb Archives, leveraging the link graph. Specifically, we combine the problems of relevance, temporal authority, diversity and time in a unified framework. The model accounts for the incomplete link structure and natural time lagging in Web Archives in mining the temporal authority. (4) Methods for enhancing predictive models at early-stage in social media and clinical domain - we investigate several methods to control model instability and enrich contexts of predictive models at the “cold-start” period. We demonstrate their effectiveness for the rumor detection and blood glucose prediction cases respectively. Overall, the findings presented in this thesis demonstrate the importance of tracking these temporal dynamics surround salient events and entities for IR applications. We show that determining such changes in time-based patterns and trends in prevalent temporal collections can better satisfy user expectations, and boost ranking and recommendation effectiveness over time

Knowledge-Based Techniques for Scholarly Data Access: Towards Automatic Curation

Accessing up-to-date and quality scientific literature is a critical preliminary step in any research activity. Identifying relevant scholarly literature for the extents of a given task or application is, however a complex and time consuming activity. Despite the large number of tools developed over the years to support scholars in their literature surveying activity, such as Google Scholar, Microsoft Academic search, and others, the best way to access quality papers remains asking a domain expert who is actively involved in the field and knows research trends and directions. State of the art systems, in fact, either do not allow exploratory search activity, such as identifying the active research directions within a given topic, or do not offer proactive features, such as content recommendation, which are both critical to researchers. To overcome these limitations, we strongly advocate a paradigm shift in the development of scholarly data access tools: moving from traditional information retrieval and filtering tools towards automated agents able to make sense of the textual content of published papers and therefore monitor the state of the art. Building such a system is however a complex task that implies tackling non trivial problems in the fields of Natural Language Processing, Big Data Analysis, User Modelling, and Information Filtering. In this work, we introduce the concept of Automatic Curator System and present its fundamental components.openDottorato di ricerca in InformaticaopenDe Nart, Dari

Slow Design through Fast Technology: The Application of Socially Reflective Design Principles to Modern Mediated Technologies

abstract: This thesis describes research into the application of socially reflective, or "Slow", design principles to modern mediated systems, or "Fast" technology. The "information overload" caused by drastic changes in the nature of human communications in the last decade has become a serious problem, with many human-technology interactions creating mental confusion, personal discomfort and a sense of disconnection. Slow design principles aim to help create interactions that avoid these problems by increasing interaction richness, encouraging engagement with local communities, and promoting personal and communal reflection. Three major functional mediated systems were constructed to examine the application of Slow principles on multiple scales: KiteViz, Taskville and Your ____ Here. Each system was designed based on a survey of current research within the field and previous research results. KiteViz is a visually metaphorical display of Twitter activity within a small group, Taskville is a workplace game designed to support collaboration and group awareness in an enterprise, and Your ____ Here is a physical-digital projection system that augments built architecture with user-submitted content to promote discussion and reflection. Each system was tested with multiple users and user groups, the systems were evaluated for their effectiveness in supporting each of the tenets of Slow design, and the results were collected into a set of key findings. Each system was considered generally effective, with specific strengths varying. The thesis concludes with a framework of five major principles to be used in the design of modern, highly-mediated systems that still apply Slow design principles: design for fundamental understanding, handle complexity gracefully, Slow is a process of evolution and revelation, leverage groups and personal connections to encode value, and allow for participation across a widely distributed range of scales.Dissertation/ThesisM.S.D. Design 201

UNDERSTANDING THE SOCIAL AND COGNITIVE EXPERIENCES OF CHILDREN INVOLVED IN TECHNOLOGY DESIGN PROCESSES

Technology has become ubiquitous not only in the lives of adults, but also in the lives of children. For every technology, there is a process by which it is designed. In many cases, children are involved in these design processes. This study examined the social and cognitive experiences of children who were integrally involved in a technology design process in partnership with adults. This research study employed a Vygotskian lens with a case study research method, to understand the cognitive and social experiences of child technology design partners over a one-year period of design and partnership. Artifact analysis, participant observation, and interviews were used to collect and analyze data. Results from this study demonstrated that children involved in technology design process in partnership with adults experienced social and cognitive experiences which fall into the areas of relationships, enjoyment, confidence, communication, collaboration, skills, and content