Query expansion by relying on the structure of knowledge bases

Query expansion techniques aim at improving the results achieved by a user's query by means of introducing new expansion terms, called expansion features. Expansion features introduce new concepts that are semantically related with the concepts in the user's query and that allow retrieving documents that otherwise would be not. Thus, the challenge is to select those expansion features that are capable of improving the results the most. A bad choice of expansion features may be counterproductive. In this thesis, we use an external source of information, a Knowledge Base (KB), as source expansion features. A knowledge base consists of a set of entries, each of which represent a concept and has, at least, a name, which can be used as expansion feature. The techniques framed in this family have become more popular due to the increase of available data, as, for example, Wikipedia. Particularly, we focus on exploiting those KB whose entries are linked to each other, conforming a graph of entries. To the best of our knowledge, most of the techniques framed on the KB family rely on some kind of text analysis, such as explicit semantic analysis, or are based on other existing query expansion techniques such as pseudo relevance feedback. However, the underlying net-work structure of KBs has been barely exploited. In this thesis, we show that the structure can be used to identify reliable expansion feature for the query expansion process. Thus, we design a novel expansion technique, Structural Query Expansion (SQE). For SQE to benefit from the particular structures of KBs, we propose a methodology to identify the structural characteristics that, given a query, allow identifying those nodes in the KB that are good candidates to be used as source of expansion features, called from now on expansion nodes. The methodology consists in building a ground truth that connects each query from a query set with those nodes of the KB that when used to extract the expansion features allow achieving the best results in terms of precision, we call the set of those nodes, expansion query graph. Then, we compare the expansion query graph of each query to find shared characteristics. SQE materializes the revealed characteristics into a set of structural motifs. In the particular case of Wikipedia, we have found two motifs called triangular and square. In the former, the query node and the expansion node are doubly linked and the expansion node belongs to, at least, the same categories as the query node. In the latter, the query node and the expansion node also are doubly linked and their categories are connected somehow. These motifs are used to, given a query and its query nodes, identify all the expansion nodes which are used as source of expansion features. Notice that we have designed this technique to be orthogonal to others because is fully decoupled from the search process and does not depend on the particular collection of documents. We have tested our techniques with three different datasets to avoid any kind of overfitting. The results are shown to be consistent among the three of them. Also, the results which are validated with statistical significance tests, show that SQE is capable to achieve up to 150% improvement in the precision. Finally, we show the performance of our technique which runs in sub-second times (358.23ms at maximum) which makes it feasible for a real query expansion system. This is especially relevant because, to the best of our knowledge, the performance is an aspect that is being ignored in most of the works and, thus, it is difficult to know whether they can be include in real systems or not.Les tècniques d'expansió de consultes tenen com a objecte millorar els resultats obtinguts per la consulta d'un usuari a partir de la introducció de termes d'expansió, anomenat característiques d'expansió. Les característiques d'expansió introdueixen nous conceptes que estan relacionats semànticament amb els conceptes de la consulta de l'usuari i que permeten obtenir documents que d'altra manera no es podrien obtenir. Per tant, el repte és seleccionar les característiques d'expansió que són capaces de millorar al màxim els resultats, doncs una mala elecció pot ser contra-productiva. En aquesta tesis, utilitzem una font externa d'informació, una Base de Coneixement (KB), com a font de característiques d'expansió. Una KB és un conjunt d'entrades, cadascuna de les quals representa un concepte i que té, com a mínim, un nom, que és susceptible de ser usat com a característica d'expansió. Les tècniques emmarcades en aquesta família han esdevingut populars degut al creixement de la informació disponible, per exemple, Wikipedia. Particularment, nosaltres en centrem en utilitzar aquelles KB les entrades de les quals estan relacionades entre si, conformant d'aquesta manera, un graf d'entrades. Segons les nostres informacions, la majora de les tècniques emmarcades en aquesta família utilitzen algun tipus d'anàlisi lingüístic, o estan basades en d'altres tècniques com relevance feedback. Ara bé, la estructura subjacent de la xarxa gairebé no s'ha utilitzat. En aquesta tesis, mostrem que la estructura es pot fer servir per identificar característiques d'expansió fiables pel procés d'expansió de consultes. De fet, proposem una tècnica d'expansió novell, Structural Query Expansion (SQE), que la explota. Perquè SQE pugui beneficiar-se de les particularitats estructurals de les KBs, hem proposat també una metodologia per revelar les característiques estructurals que, donada una consulta, permeten identificar aquells nodes que són una bona font de característiques d'expansió, els anomenats, nodes d'expansió. Aquesta metodologia consisteix en construir un ground truth que relaciona una conjunt de consultes amb el seu optimal expansion query graph. L'optimal expansion query graph és el conjunt de nodes d'expansió que quan s'utilitzen com a font de característiques d'expansió, permeten obtenir els millors resultats en termes de precisió. Un cop tenim els optimal expansion query graphs, els comparem entre si per a buscar característiques compartides. SQE materialitza aquestes característiques en un conjunt de motius estructurals. En el cas de Wikipedia hem trobat 2 motius: el triangular i el quadràtic. En els dos casos el node de la consulta ha d'estar doblement lincat amb el node d'expansió. En el triangular, les categories del node d'expansió ha de pertànyer, com a mínim, a les mateixes categories que el node de la consulta, mentre que en el quadràtic tan sols cal que les categories del node de la consulta i el d'expansió estiguin relacionades. Aquest motius s'utilitzen per, donada una consulta, identificar tots els seus nodes d'expansió. Hem dissenyat aquesta tècnica com una tècnica ortogonal a d'altres ja que està desacoblada del procés de cerca i no depèn de la col·lecció de documents. Hem provar la nostra tècnica amb 3 jocs de dades diferents per a evitar qualsevol tipus d'especialització. Els resultats són consistents entre els tres. Hem validat els resultats amb testos de significança estadística obtenint millores del 150% en la precisió. Finalment, pel que fa el rendiment de la nostra proposta, mostrem que s'executa en mil·lisegons, i això la fa susceptible de ser utilitzada en sistemes d'expansió reals. Això és especialment rellevant perquè, segons les nostres informacions, aquest és un aspecte que s'ignora en la literatura i, per tant, és difícil de saber la viabilitat de les propostes que existeixen en entorns reals

TempME: Towards the Explainability of Temporal Graph Neural Networks via Motif Discovery

Temporal graphs are widely used to model dynamic systems with time-varying interactions. In real-world scenarios, the underlying mechanisms of generating future interactions in dynamic systems are typically governed by a set of recurring substructures within the graph, known as temporal motifs. Despite the success and prevalence of current temporal graph neural networks (TGNN), it remains uncertain which temporal motifs are recognized as the significant indications that trigger a certain prediction from the model, which is a critical challenge for advancing the explainability and trustworthiness of current TGNNs. To address this challenge, we propose a novel approach, called Temporal Motifs Explainer (TempME), which uncovers the most pivotal temporal motifs guiding the prediction of TGNNs. Derived from the information bottleneck principle, TempME extracts the most interaction-related motifs while minimizing the amount of contained information to preserve the sparsity and succinctness of the explanation. Events in the explanations generated by TempME are verified to be more spatiotemporally correlated than those of existing approaches, providing more understandable insights. Extensive experiments validate the superiority of TempME, with up to 8.21% increase in terms of explanation accuracy across six real-world datasets and up to 22.96% increase in boosting the prediction Average Precision of current TGNNs.Comment: Accepted at NeurIPS 2023, Camera Ready Versio

Enrich Data: millorant les cerques a la web

Forma part del Projecte Europeu finançat per TetracomEnrich Data té com objectiu principal dissenyar i desenvolupar un sistema que contribueixi en millorar les webs i alhora l'experiència dels usuaris. Per fer-ho ens proposem un sistema que expandeixi les consultes dels usuaris aconseguint així que obtinguin uns millors resultats.Enrich Data aims to design and develop a system that contributes to improving websites as well as users' experiences. In order to do so, we want to develop a system that expands users queries so that they obtain better results

Structure Selection from Streaming Relational Data

Statistical relational learning techniques have been successfully applied in a wide range of relational domains. In most of these applications, the human designers capitalized on their background knowledge by following a trial-and-error trajectory, where relational features are manually defined by a human engineer, parameters are learned for those features on the training data, the resulting model is validated, and the cycle repeats as the engineer adjusts the set of features. This paper seeks to streamline application development in large relational domains by introducing a light-weight approach that efficiently evaluates relational features on pieces of the relational graph that are streamed to it one at a time. We evaluate our approach on two social media tasks and demonstrate that it leads to more accurate models that are learned faster

A survey of sRNA families in α-proteobacteria

We have performed a computational comparative analysis of six small non-coding RNA (sRNA) families in α-proteobacteria. Members of these families were first identified in the intergenic regions of the nitrogen-fixing endosymbiont S. meliloti by a combined bioinformatics screen followed by experimental verification. Consensus secondary structures inferred from covariance models for each sRNA family evidenced in some cases conserved motifs putatively relevant to the function of trans-encoded base-pairing sRNAs i.e., Hfq-binding signatures and exposed anti Shine-Dalgarno sequences. Two particular family models, namely αr15 and αr35, shared own sub-structural modules with the Rfam model suhB (RF00519) and the uncharacterized sRNA family αr35b, respectively. A third sRNA family, termed αr45, has homology to the cis-acting regulatory element speF (RF00518). However, new experimental data further confirmed that the S. meliloti αr45 representative is an Hfq-binding sRNA processed from or expressed independently of speF, thus refining the Rfam speF model annotation. All the six families have members in phylogenetically related plant-interacting bacteria and animal pathogens of the order of the Rhizobiales, some occurring with high levels of paralogy in individual genomes. In silico and experimental evidences predict differential regulation of paralogous sRNAs in S. meliloti 1021. The distribution patterns of these sRNA families suggest major contributions of vertical inheritance and extensive ancestral duplication events to the evolution of sRNAs in plant-interacting bacteria

Span-core Decomposition for Temporal Networks: Algorithms and Applications

When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this paper we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores \emph{span-cores}. For a temporal network defined on a discrete temporal domain $T$, the total number of time intervals included in $T$ is quadratic in $|T|$, so that the total number of span-cores is potentially quadratic in $|T|$ as well. Our first main contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the \emph{maximal span-cores}, i.e., span-cores that are not dominated by any other span-core by both their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of \emph{temporal community search}, where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain $T$. We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm.Comment: ACM Transactions on Knowledge Discovery from Data (TKDD), 2020. arXiv admin note: substantial text overlap with arXiv:1808.0937