Discovering semantic aspects of socially constructed knowledge hierarchy to boost the relevance of Web searching

The research intends to boost the relevance of Web search results by classifyingWebsnippet into socially constructed hierarchical search concepts, such as the mostcomprehensive human edited knowledge structure, the Open Directory Project (ODP). Thesemantic aspects of the search concepts (categories) in the socially constructed hierarchicalknowledge repositories are extracted from the associated textual information contributed bysocieties. The textual information is explored and analyzed to construct a category-documentset, which is subsequently employed to represent the semantics of the socially constructedsearch concepts. Simple API for XML (SAX), a component of JAXP (Java API for XMLProcessing) is utilized to read in and analyze the two RDF format ODP data files, structure.rdfand content.rdf. kNN, which is trained by the constructed category-document set, is used tocategorized the Web search results. The categorized Web search results are then ontologicallyfiltered based on the interactions of Web information seekers. Initial experimental resultsdemonstrate that the proposed approach can improve precision by 23.5%

Socializing the Semantic Gap: A Comparative Survey on Image Tag Assignment, Refinement and Retrieval

Where previous reviews on content-based image retrieval emphasize on what can be seen in an image to bridge the semantic gap, this survey considers what people tag about an image. A comprehensive treatise of three closely linked problems, i.e., image tag assignment, refinement, and tag-based image retrieval is presented. While existing works vary in terms of their targeted tasks and methodology, they rely on the key functionality of tag relevance, i.e. estimating the relevance of a specific tag with respect to the visual content of a given image and its social context. By analyzing what information a specific method exploits to construct its tag relevance function and how such information is exploited, this paper introduces a taxonomy to structure the growing literature, understand the ingredients of the main works, clarify their connections and difference, and recognize their merits and limitations. For a head-to-head comparison between the state-of-the-art, a new experimental protocol is presented, with training sets containing 10k, 100k and 1m images and an evaluation on three test sets, contributed by various research groups. Eleven representative works are implemented and evaluated. Putting all this together, the survey aims to provide an overview of the past and foster progress for the near future.Comment: to appear in ACM Computing Survey

Semantic enrichment of knowledge sources supported by domain ontologies

This thesis introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this thesis are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; (ii) development of a method for extending the traditional vector space, using domain ontologies; (iii) development of a method to support ontology learning, based on the discovery of new ontological relations expressed in non-structured information sources; (iv) development of a process to evaluate the semantic enrichment; (v) implementation of a proof-of-concept, named SENSE (Semantic Enrichment kNowledge SourcEs), which enables to validate the ideas established under the scope of this thesis; (vi) publication of several scientific articles and the support to 4 master dissertations carried out by the department of Electrical and Computer Engineering from FCT/UNL. It is worth mentioning that the work developed under the semantic referential covered by this thesis has reused relevant achievements within the scope of research European projects, in order to address approaches which are considered scientifically sound and coherent and avoid “reinventing the wheel”.European research projects - CoSpaces (IST-5-034245), CRESCENDO (FP7-234344) and MobiS (FP7-318452

Exploiting Social Media Sources for Search, Fusion and Evaluation

The web contains heterogeneous information that is generated with different characteristics and is presented via different media. Social media, as one of the largest content carriers, has generated information from millions of users worldwide, creating material rapidly in all types of forms such as comments, images, tags, videos and ratings, etc. In social applications, the formation of online communities contributes to conversations of substantially broader aspects, as well as unfiltered opinions about subjects that are rarely covered in public media. Information accrued on social platforms, therefore, presents a unique opportunity to augment web sources such as Wikipedia or news pages, which are usually characterized as being more formal. The goal of this dissertation is to investigate in depth how social data can be exploited and applied in the context of three fundamental information retrieval (IR) tasks: search, fusion, and evaluation. Improving search performance has consistently been a major focus in the IR community. Given the in-depth discussions and active interactions contained in social media, we present approaches to incorporating this type of data to improve search on general web corpora. In particular, we propose two graph-based frameworks, social anchor and information network, to associate related web and social content, where information sources of diverse characteristics can be used to complement each other in a unified manner. We investigate how the enriched representation can potentially reduce vocabulary mismatch and improve retrieval effectiveness. Presenting social media content to users is valuable particularly for queries intended for time-sensitive events or community opinions. Current major search engines commonly blend results from different search services (or verticals) into core web results. Motivated by this real-world need, we explore ways to merge results from different web and social services into a single ranked list. We present an optimization framework for fusion, where impact of documents, ranked lists, and verticals can be modeled simultaneously to maximize performance. Evaluating search system performance has largely relied on creating reusable test collections in IR. Traditional ways to creating evaluation sets can require substantial manual effort. To reduce such effort, we explore an approach to automating the process of collecting pairs of queries and relevance judgments, using high quality social media, Community Question Answering (CQA). Our approach is based on the idea that CQA services support platforms for users to raise questions and to share answers, therefore encoding the associations between real user information needs and real user assessments. To demonstrate the effectiveness of our approaches, we conduct extensive retrieval and fusion experiments, as well as verify the reliability of the new, CQA-based evaluation test sets

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

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

Alignment of multi-cultural knowledge repositories

The ability to interconnect multiple knowledge repositories within a single framework is a key asset for various use cases such as document retrieval and question answering. However, independently created repositories are inherently heterogeneous, reflecting their diverse origins. Thus, there is a need to align concepts and entities across knowledge repositories. A limitation of prior work is the assumption of high afinity between the repositories at hand, in terms of structure and terminology. The goal of this dissertation is to develop methods for constructing and curating alignments between multi-cultural knowledge repositories. The first contribution is a system, ACROSS, for reducing the terminological gap between repositories. The second contribution is two alignment methods, LILIANA and SESAME, that cope with structural diversity. The third contribution, LAIKA, is an approach to compute alignments between dynamic repositories. Experiments with a suite ofWeb-scale knowledge repositories show high quality alignments. In addition, the application benefits of LILIANA and SESAME are demonstrated by use cases in search and exploration.Die Fähigkeit mehrere Wissensquellen in einer Anwendung miteinander zu verbinden ist ein wichtiger Bestandteil für verschiedene Anwendungsszenarien wie z.B. dem Auffinden von Dokumenten und der Beantwortung von Fragen. Unabhängig erstellte Datenquellen sind allerdings von Natur aus heterogen, was ihre unterschiedlichen Herkünfte widerspiegelt. Somit besteht ein Bedarf darin, die Konzepte und Entitäten zwischen den Wissensquellen anzugleichen. Frühere Arbeiten sind jedoch auf Datenquellen limitiert, die eine hohe Ähnlichkeit im Sinne von Struktur und Terminologie aufweisen. Das Ziel dieser Dissertation ist, Methoden für Aufbau und Pflege zum Angleich zwischen multikulturellen Wissensquellen zu entwickeln. Der erste Beitrag ist ein System names ACROSS, das auf die Reduzierung der terminologischen Kluft zwischen den Datenquellen abzielt. Der zweite Beitrag sind die Systeme LILIANA und SESAME, welche zum Angleich eben dieser Datenquellen unter Berücksichtigung deren struktureller Unterschiede dienen. Der dritte Beitrag ist ein Verfahren names LAIKA, das den Angleich dynamischer Quellen unterstützt. Unsere Experimente mit einer Reihe von Wissensquellen in Größenordnung des Web zeigen eine hohe Qualität unserer Verfahren. Zudem werden die Vorteile in der Verwendung von LILIANA und SESAME in Anwendungsszenarien für Suche und Exploration dargelegt

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model

Structural learning for large scale image classification

To leverage large-scale collaboratively-tagged (loosely-tagged) images for training a large number of classifiers to support large-scale image classification, we need to develop new frameworks to deal with the following issues: (1) spam tags, i.e., tags are not relevant to the semantic of the images; (2) loose object tags, i.e., multiple object tags are loosely given at the image level without their locations in the images; (3) missing object tags, i.e. some object tags are missed due to incomplete tagging; (4) inter-related object classes, i.e., some object classes are visually correlated and their classifiers need to be trained jointly instead of independently; (5) large scale object classes, which requires to limit the computational time complexity for classifier training algorithms as well as the storage spaces for intermediate results. To deal with these issues, we propose a structural learning framework which consists of the following key components: (1) cluster-based junk image filtering to address the issue of spam tags; (2) automatic tag-instance alignment to address the issue of loose object tags; (3) automatic missing object tag prediction; (4) object correlation network for inter-class visual correlation characterization to address the issue of missing tags; (5) large-scale structural learning with object correlation network for enhancing the discrimination power of object classifiers. To obtain enough numbers of labeled training images, our proposed framework leverages the abundant web images and their social tags. To make those web images usable, tag cleansing has to be done to neutralize the noise from user tagging preferences, in particularly junk tags, loose tags and missing tags. Then a discriminative learning algorithm is developed to train a large number of inter-related classifiers for achieving large-scale image classification, e.g., learning a large number of classifiers for categorizing large-scale images into a large number of inter-related object classes and image concepts. A visual concept network is first constructed for organizing enumorus object classes and image concepts according to their inter-concept visual correlations. The visual concept network is further used to: (a) identify inter-related learning tasks for classifier training; (b) determine groups of visually-similar object classes and image concepts; and (c) estimate the learning complexity for classifier training. A large-scale discriminative learning algorithm is developed for supporting multi-class classifier training and achieving accurate inter-group discrimination and effective intra-group separation. Our discriminative learning algorithm can significantly enhance the discrimination power of the classifiers and dramatically reduce the computational cost for large-scale classifier training

Semantic and pragmatic characterization of learning objects

Tese de doutoramento. Engenharia Informática. Universidade do Porto. Faculdade de Engenharia. 201