Relevance-Based Retrieval on Hidden-Web Text Databases without Ranking Support

Entity-oriented search has revolutionized search engines. In the era of Google Knowledge Graph and Microsoft Satori, users demand an effortless process of search. Whether they express an information need through a keyword query, expecting documents and entities, or through a clicked entity, expecting related entities, there is an inherent need for the combination of corpora and knowledge bases to obtain an answer. Such integration frequently relies on independent signals extracted from inverted indexes, and from quad indexes indirectly accessed through queries to a triplestore. However, relying on two separate representation models inhibits the effective cross-referencing of information, discarding otherwise available relations that could lead to a better ranking. Moreover, different retrieval tasks often demand separate implementations, although the problem is, at its core, the same. With the goal of harnessing all available information to optimize retrieval, we explore joint representation models of documents and entities, while taking a step towards the definition of a more general retrieval approach. Specifically, we propose that graphs should be used to incorporate explicit and implicit information derived from the relations between text found in corpora and entities found in knowledge bases. We also take advantage of this framework to elaborate a general model for entity-oriented search, proposing a universal ranking function for the tasks of ad hoc document retrieval (leveraging entities), ad hoc entity retrieval, and entity list completion. At a conceptual stage, we begin by proposing the graph-of-entity, based on the relations between combinations of term and entity nodes. We introduce the entity weight as the corresponding ranking function, relying on the idea of seed nodes for representing the query, either directly through term nodes, or based on the expansion to adjacent entity nodes. The score is computed based on a series of geodesic distances to the remaining nodes, providing a ranking for the documents (or entities) in the graph. In order to improve on the low scalability of the graph-of-entity, we then redesigned this model in a way that reduced the number of edges in relation to the number of nodes, by relying on the hypergraph data structure. The resulting model, which we called hypergraph-of-entity, is the main contribution of this thesis. The obtained reduction was achieved by replacing binary edges with n -ary relations based on sets of nodes and entities (undirected document hyperedges), sets of entities (undirected hyperedges, either based on cooccurrence or a grouping by semantic subject), and pairs of a set of terms and a set of one entity (directed hyperedges, mapping text to an object). We introduce the random walk score as the corresponding ranking function, relying on the same idea of seed nodes, similar to the entity weight in the graph-of-entity. Scoring based on this function is highly reliant on the structure of the hypergraph, which we call representation-driven retrieval. As such, we explore several extensions of the hypergraph-of-entity, including relations of synonymy, or contextual similarity, as well as different weighting functions per node and hyperedge type. We also propose TF-bins as a discretization for representing term frequency in the hypergraph-of-entity. For the random walk score, we propose and explore several parameters, including length and repeats, with or without seed node expansion, direction, or weights, and with or without a certain degree of node and/or hyperedge fatigue, a concept that we also propose. For evaluation, we took advantage of TREC 2017 OpenSearch track, which relied on an online evaluation process based on the Living Labs API, and we also participated in TREC 2018 Common Core track, which was based on the newly introduced TREC Washington Post Corpus. Our main experiments were supported on the INEX 2009 Wikipedia collection, which proved to be a fundamental test collection for assessing retrieval effectiveness across multiple tasks. At first, our experiments solely focused on ad hoc document retrieval, ensuring that the model performed adequately for a classical task. We then expanded the work to cover all three entity-oriented search tasks. Results supported the viability of a general retrieval model, opening novel challenges in information retrieval, and proposing a new path towards generality in this area.

Stating Boolean queries is a difficult task for many novice computer users, and yet the Boolean query is the standard method of requesting data in information retrieval systems. There is thus a need to improve the human-system interface for this kind of application. A graphical query language is presented for an information retrieval system. The language allows the user to state elementary queries by means of small images representing catalog cards. The Boolean combination of elementary queries is intuitively achieved by positioning the cards on the screen. This approach lets people use familiar concepts while attempting to achieve better utilization of the information retrieval system.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

In Information Retrieval it is well-known that the complexity of processing boolean queries depends on the size of the intermediate results, which could be huge (and are typically on disk) even though the size of the final result may be quite small. In the case of inverted files the most time consuming operation is the merging or intersection of the list of occurrences [1]. We propose, the Keyword tree (K-tree) and forest, efficient structures to handle boolean queries in keyword-based information retrieval. Extensive simulations show that K-tree is orders-of-magnitude faster (i.e., far fewer I/O's) for boolean queries than the usual approach of merging the lists of occurrences and incurs only a small overhead for single keyword queries. The K-tree can be efficiently parallelized as well. The construction cost of K-tree is comparable to the cost of building inverted files.

Ranking is likely the most important process of an information retrieval (IR) system that will be used to evaluate and measure the effectiveness of an IR system. This paper aims to produce the implementation of fuzzy logic controller of Mamdani-type fuzzy inference system for defining the ranking function by using the BM25 model in the Malay IR system that also includes the Malay Stemmer. The result of the ranking function then will be compared to the result of vector space model that is also applied in Malay IR system and be evaluated using relevant document by the hadith expert. The results showed that FBMIR has slightly outperformed vector space model on three topic set of query results such as 'Iman', 'Ilmu' and 'Wuduk' on the precision at rank 10 and the percentage of no relevant document in the top ten retrieved measures.

Both phrases and Boolean queries have a long history in information retrieval, particularly in commercial systems. In previous work, Boolean queries have been used as a source of phrases for a statistical retrieval model, This work, like the majority of research on phrases, resulted in little improvement in retrieval effectiveness, In this paper, we describe an approach where phrases identified in natural language queries are used to build structured queries for a probabilistic retrieval model. Our results show that using phrases in this way can improve performance, and that phrases that are automatically extracted from a natural language query perform nearly as well as manually selected phrases.

In recent years, the ever-increasing quantities of entities in large knowledge bases on the Web, such as DBpedia, Freebase and YAGO, pose new challenges but at the same time open up new opportunities for intelligent information access. These knowledge bases (KBs) have become valuable resources in many research areas, such as natural language processing (NLP) and information retrieval (IR). Recently, almost every major commercial Web search engine has incorporated entities into their search process, including Google’s Knowledge Graph, Yahoo!’s Web of Objects and Microsoft’s Satori Graph/Bing Snapshots. The goal is to bridge the semantic gap between natural language text and formalized knowledge. Within the context of globalization, multilingual and cross-lingual access to information has emerged as an issue of major interest. Nowadays, more and more people from different countries are connecting to the Internet, in particular the Web, and many users can understand more than one language. While the diversity of languages on the Web has been growing, for most people there is still very little content in their native language. As a consequence of the ability to understand more than one language, users are also interested in Web content in other languages than their mother tongue. There is an impending need for technologies that can help in overcoming the language barrier for multilingual and cross-lingual information access. In this thesis, we face the overall research question of how to allow for semantic-aware and cross-lingual processing of Web documents and user queries by leveraging knowledge bases. With the goal of addressing this complex problem, we provide the following solutions: (1) semantic annotation for addressing the semantic gap between Web documents and knowledge; (2) semantic search for coping with the semantic gap between keyword queries and knowledge; (3) the exploitation of cross-lingual semantics for overcoming the language barrier between natural language expressions (i.e., keyword queries and Web documents) and knowledge for enabling cross-lingual semantic annotation and search. We evaluated these solutions and the results showed advances beyond the state-of-the-art. In addition, we implemented a framework of cross-lingual semantic annotation and search, which has been widely used for cross-lingual processing of media content in the context of our research projects.

Clinical knowledge systems (CKS) facilitate healthcare practitioners to make the best decisions at the point of care by having access to most recent knowledge. However, because of the overabundance of clinical research articles, it is time consuming to add new content manually to any CKS such as UpToDate <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">©</sup> and to ensure that it is consistent with evidence. For this reason, many CKS such as Mayo Clinic's AskMayoExpert use expert-based (and not evidence-based) knowledge learned from peer-to-peer communication. The main motivation of this work is to explore methods for assigning references automatically to expert-written content. The system employs information retrieval (IR) techniques to retrieve related sentences from MEDLINE abstracts as evidence candidates and then uses a machine learning model trained with physician's evaluation score on certain journals for re-ranking. Finally, the system utilizes citation counts of individual articles for further refining the ranking. A preliminary evaluation using 59 UpToDate sentences and respective citations as gold standard showed that the median ranking improved 11 folds after adding the journal and citation relevance metrics on the top of baseline that only uses the semantic relevance metric. The system seems promising and ready for trials in real use-case scenarios with experts.

Retrieval of reliable relevant information is the major concern in medical information retrieval. Among all factors that affect the performance of retrieval system, ranking function is the most important factor. The retrieved documents from large document collection are arranged in the decreasing order of their relevance score by ranking function. A neuro-fuzzy system-based hybrid ranking function (HRF) is proposed in this paper. The proposed ranking function considers weight of document and query with respect to keyword as input features and gives relevance score between document and query as output. Experiments are performed on OHSUMED and PMC benchmark medical document corpus by using 15 experimental queries. The experimental results prove that the proposed HRF performs better when compared with fuzzy logic-based ranking function (FRF) and conventional statistical Euclidean distance-based ranking function (ERF) and cosine similarity-based ranking function (CRF) in terms of precision, recall and F-measure.

A generic ranking function discovery framework by genetic programming for information retrieval

The performance of Information Retrieval Systems (IRSs) is usually measured using two different criteria, precision and recall. In such a way, the problem of tuning an IRS may be considered as a multi-objective optimization problem. In this contribution, we focus on the automatic learning of Boolean queries in IRSs by means of multi-objective evolutionary techniques. We present a comparative study of four multi-objective evolutionary optimization techniques of general-purpose (NSGA-II, SPEA2 and two MOGLS) to learn Boolean queries.

In this article, our interest is focused on the automatic learning of Boolean queries in information retrieval systems (IRSs) by means of multi‐objective evolutionary algorithms considering the classic performance criteria, precision and recall. We present a comparative study of four well‐known, general‐purpose, multi‐objective evolutionary algorithms to learn Boolean queries in IRSs. These evolutionary algorithms are the Nondominated Sorting Genetic Algorithm (NSGA‐II), the first version of the Strength Pareto Evolutionary Algorithm (SPEA), the second version of SPEA (SPEA2), and the Multi‐Objective Genetic Algorithm (MOGA).

As enormous volume of electronic data increased gradually, searching as well as retrieving essential info from the internet is extremely difficult task. Normally, the Information Retrieval (IR) systems present info dependent upon the user’s query keywords. At present, it is insufficient as large volume of online data and it contains less precision as the system takes syntactic level search into consideration. Furthermore, numerous previous search engines utilize a variety of techniques for semantic based document extraction and the relevancy between the documents has been measured using page ranking methods. On the other hand, it contains certain problems with searching time. With the intention of enhancing the query searching time, the research system implemented a Modified Firefly Algorithm (MFA) adapted with Intelligent Ontology and Latent Dirichlet Allocation based Information Retrieval (IOLDAIR) model. In this recommended methodology, the set of web documents, Face book comments and tweets are taken as dataset. By means of utilizing Tokenization process, the dataset pre-processing is carried out. Strong ontology is built dependent upon a lot of info collected by means of referring via diverse websites. Find out the keywords as well as carry out semantic analysis with user query by utilizing ontology matching by means of jaccard similarity. The feature extraction is carried out dependent upon the semantic analysis. After that, by means of Modified Firefly Algorithm (MFA), the ideal features are chosen. With the help of Fuzzy C-Mean (FCM) clustering, the appropriate documents are grouped and rank them. At last by using IOLDAIR model, the appropriate information’s are extracted. The major benefit of the research technique is the raise in relevancy, capability of dealing with big data as well as fast retrieval. The experimentation outcomes prove that the presented method attains improved performance when matched up with the previous system.

Experimental comparison of navigation in a Galois lattice with conventional information retrieval methods

Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors).< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Natural Language Processing (NLP) techniques are believed to hold the potential to assist bag-of-words Information Retrieval (IR) in terms of retrieval accuracy. In this paper, we report a natural language based IR approach where the common syntactic structures between documents and the query is regarded to as a query-dependent feature for documents. Specifically, a is proposed for query terms, which can be seen as a weight to model the degree of term's involvement in the common syntactic structures. This structural weight is used together with the TF-IDF weighting scheme, which results in a new ranking function. The accumulation of this structural weight of all the query terms in the new ranking function will be seen as a measure of how much a document and a query share the common syntactic structures. The experimental results show that by using this ranking function, significant improvements in the retrieval performance are achieved.