2,062 research outputs found
Artificial Intelligence methodologies to early predict student outcome and enrich learning material
L'abstract è presente nell'allegato / the abstract is in the attachmen
27 pawns ready for action: A multi-indicator methodology and evaluation of thesaurus management tools from a LOD perspective
Purpose – The purpose of this paper is to propose a methodology for assessing thesauri and other controlled vocabularies management tools that can represent content using the Simple Knowledge Organization System (SKOS) data model, and their use in a Linked Open Data (LOD) paradigm. It effectively analyses selected set of tools in order to prove the validity of the method.
Design/methodology/approach – A set of 27 criteria grouped in five evaluation indicators is proposed and applied to ten vocabulary management applications which are compliant with the SKOS data model. Previous studies of controlled vocabulary management software are gathered and analyzed, to compare the evaluation parameters used and the results obtained for each tool.
Findings – The results indicate that the tool that obtains the highest score in every indicator is Poolparty. The second and third tools are, respectively, TemaTres and Intelligent Theme Manager, but scoring lower in most of the evaluation items. The use of a broad set of criteria to evaluate vocabularies management tools gives satisfactory results. The set of five indicators and 27 criteria proposed here represents a useful evaluation system in the selection of current and future tools to manage vocabularies.
Research limitations/implications – The paper only assesses the ten most important/well know software tools applied for thesaurus and vocabulary management until October 2016. However, the evaluation criteria could be applied to new software that could appear in the future to create/manage SKOS vocabularies in compliance with LOD standards.
Originality/value – The originality of this paper relies on the proposed indicators and criteria to evaluate vocabulary management tools. Those criteria and indicators can be valuable also for future software that might appear. The indicators are also applied to the most exhaustive and qualified list of this kind of tools. The paper will help designers, information architects, metadata librarians, and other staff involved in the design of digital information systems, to choose the right tool to manage their vocabularies in a LOD/vocabulary scenario
Biologically Motivated Distributed Designs for Adaptive Knowledge Management
We discuss how distributed designs that draw from biological network
metaphors can largely improve the current state of information retrieval and
knowledge management of distributed information systems. In particular, two
adaptive recommendation systems named TalkMine and @ApWeb are discussed in more
detail. TalkMine operates at the semantic level of keywords. It leads different
databases to learn new and adapt existing keywords to the categories recognized
by its communities of users using distributed algorithms. @ApWeb operates at
the structural level of information resources, namely citation or hyperlink
structure. It relies on collective behavior to adapt such structure to the
expectations of users. TalkMine and @ApWeb are currently being implemented for
the research library of the Los Alamos National Laboratory under the Active
Recommendation Project. Together they define a biologically motivated
information retrieval system, recommending simultaneously at the level of user
knowledge categories expressed in keywords, and at the level of individual
documents and their associations to other documents. Rather than passive
information retrieval, with this system, users obtain an active, evolving
interaction with information resources.Comment: To appear in Design Principles for the Immune System and Other
Distributed Autonomous Systems. i. Cohen and L. Segel (Eds.). Oxford
University Pres
HypIR: Hypertext Based Information Retrieval
Information Retrieval (IR), which is also known as text or document retrieval, is the process of locating and retrieving docri)nents that are relevant to the user queries. In
hypertext environments, docuinent databases are organized as a network of nodes which are interconnected by various types of links. This study introduces a hypertext-based text retrieval system, HypIR. In HypIR, the sentantic relationships ainong docuinents are obtained using a clustering algorithm. A new approach providing the advantages of system maps and history list is introduced to prevent the user fiotn being lost in the IR hivperspace. The paper presents the underlying concepts and iinplementation details. HypIR is based on the object-oriented paradigm and its execution platforin is HyperCard
COOPERATIVE QUERY ANSWERING FOR APPROXIMATE ANSWERS WITH NEARNESS MEASURE IN HIERARCHICAL STRUCTURE INFORMATION SYSTEMS
Cooperative query answering for approximate answers has been utilized in various problem domains. Many challenges in manufacturing information retrieval, such as: classifying parts into families in group technology implementation, choosing the closest alternatives or substitutions for an out-of-stock part, or finding similar existing parts for rapid prototyping, could be alleviated using the concept of cooperative query answering. Most cooperative query answering techniques proposed by researchers so far concentrate on simple queries or single table information retrieval. Query relaxations in searching for approximate answers are mostly limited to attribute value substitutions. Many hierarchical structure information systems, such as manufacturing information systems, store their data in multiple tables that are connected to each other using hierarchical relationships - "aggregation", "generalization/specialization", "classification", and "category". Due to the nature of hierarchical structure information systems, information retrieval in such domains usually involves nested or jointed queries. In addition, searching for approximate answers in hierarchical structure databases not only considers attribute value substitutions, but also must take into account attribute or relation substitutions (i.e., WIDTH to DIAMETER, HOLE to GROOVE). For example, shape transformations of parts or features are possible and commonly practiced. A bar could be transformed to a rod. Such characteristics of hierarchical information systems, simple query or single-relation query relaxation techniques used in most cooperative query answering systems are not adequate. In this research, we proposed techniques for neighbor knowledge constructions, and complex query relaxations. We enhanced the original Pattern-based Knowledge Induction (PKI) and Distribution Sensitive Clustering (DISC) so that they can be used in neighbor hierarchy constructions at both tuple and attribute levels. We developed a cooperative query answering model to facilitate the approximate answer searching for complex queries. Our cooperative query answering model is comprised of algorithms for determining the causes of null answer, expanding qualified tuple set, expanding intersected tuple set, and relaxing multiple condition simultaneously. To calculate the semantic nearness between exact-match answers and approximate answers, we also proposed a nearness measuring function, called "Block Nearness", that is appropriate for the query relaxation methods proposed in this research
A New Approach to Information Extraction in User-Centric E-Recruitment Systems
In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users’ profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs’ portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration
Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks.
Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion.
Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data
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