Abstract A very large number of historical manuscript collections are available in image formats and require extensive manual processing in order to search through them. So, we propose and build a search engine for automatically storing, indexing and efficiently searching the manuscript images. Firstly, a handwritten text recognition technique is used to convert the images into textual representations. In the next steps, we apply the named entity recognition and historical knowledge graph to build a semantic search model, which can understand the user’s intent in the query and the contextual meaning of concepts in documents, to return correctly the transcriptions and their corresponding images for users.

To mitigate the problem of over-dependence of a pseudo-relevance feedback algorithm on the top-M document set, we make use of a set of equivalence classes of queries rather than one single query. These query equivalents are automatically constructed either from a) a knowledge base of prior distributions of terms with respect to the given query terms, or b) iteratively generated from a relevance model of term distributions in the absence of such priors. These query variants are then used to estimate the retrievability of each document with the hypothesis that documents that are more likely to be retrieved at top-ranks for a larger number of these query variants are more likely to be effective for relevance feedback. Results of our experiments show that our proposed method is able to achieve substantially better precision at top-ranks (e.g. higher [email protected] and [email protected] values) for ad-hoc IR and points-of-interest (POI) recommendation tasks.

Trip-qualifiers, such as 'trip-type' (vacation, work etc.), 'accompanied-by' (e.g., solo, friends, family etc.) are potentially useful sources of information that could be used to improve the effectiveness of POI recommendation in a current context (with a given set of these constraints). Using such information is not straight forward because a user's text reviews about the POIs visited in the past do not explicitly contain such annotations (e.g., a positive review about a pub visit does not contain the information on whether the user was with friends or alone, on a business trip or vacation). We propose to use a small set of manually compiled knowledge resource to predict the associations between the review texts in a user profile and the likely trip contexts. We demonstrate that incorporating this information within an IR-based relevance modeling framework significantly improves POI recommendation.

‘Big Data’ can mean something quite different in the context of Humanities. The way Humanities scholars frame their inquiries often leverages collections that are an order of magnitude smaller than the full, industrial scale, there is significant value to be found in the Humanistic sense of ‘Big’. In particular, the variety of the data, and the richness of the explorations, means that high-quality knowledge systems are required. More meaning is needed than the surface analytics often demonstrated in other ‘Big’ scenarios. This paper examines how a specific collection related to the 1916 Rising in Ireland was analyzed. The result was a process to extract entities that underpinned a highly-effective personalized knowledge-driven exploration of that collection by users. It demonstrates the mutual benefit of natural language at scale with rich humanistic inquiry to communicate improved experiences for a much broader range of users than would otherwise be possible.

This paper introduces the main characteristics of the digital cultural collections that constitute the use cases presently in use in the CULTURA environment. A section on related work follows giving an account on efforts on the management of digital annotations that are pertinent and that have been considered. Afterwards the innovative annotation features of the CULTURA portal for digital humanities are described; those features are aimed at improving the interaction of non-specialist users and general public with digital cultural heritage content. The annotation functions consist of two modules: the FAST annotation service as back-end and the CAT Web front-end integrated in the CULTURA portal. The annotation features have been, and are being, tested with different types of users and useful feedback is being collated, with the overall aim of generalising the approach to diverse document collections and not only the area of cultural heritage.

The Exploration, Navigation and Retrieval of Information in Cultural Heritage Workshop (ENRICH 2013) offers a forum to 1) discuss the challenges and opportunities in Information Retrieval research in the area of Cultural Heritage; 2) encourage collaboration between researchers engaged in work in this specialist area of Information Retrieval, and to foster the formation of a research community; and 3) identify a set of actions which the community should undertake to progress the research agenda. The workshop will foster a new stream of Information Retrieval research and support the design of search tools that can help end-users fully exploit the wonderful Cultural Heritage material that is available across the globe.

Personalisation on the web is mostly confined to websites of online content providers. The main drawback of this approach is the missing consideration of the users previous cross-site browsing experience resulting in an often fragmented browsing experience. This paper introduces a service driven architecture for user-centric personalisation in online cross-site tasks. The goal of the architecture is twofold: (1) to provide non-intrusive personalised recommendations to the user by not interfering in their browsing freedom and (2) to introduce cross-site personalisation in a non-invasive manner, thus not interfering with the design and functionality of the overall website. We introduce the proposed architecture and its applicability across separately hosted open-source Web-based Content Management Systems (WCMS). We also discuss encouraging results of an initial task-based experiment concentrating on the user's perception of non-intrusive personalisation within and across different websites.