Information Retrieval Algorithms Research Articles

Behavioral Quantification of Audiomotor Transformations in Improvising and Score-Dependent Musicians.

The historically developed practice of learning to play a music instrument from notes instead of by imitation or improvisation makes it possible to contrast two types of skilled musicians characterized not only by dissimilar performance practices, but also disparate methods of audiomotor learning. In a recent fMRI study comparing these two groups of musicians while they either imagined playing along with a recording or covertly assessed the quality of the performance, we observed activation of a right-hemisphere network of posterior superior parietal and dorsal premotor cortices in improvising musicians, indicating more efficient audiomotor transformation. In the present study, we investigated the detailed performance characteristics underlying the ability of both groups of musicians to replicate music on the basis of aural perception alone. Twenty-two classically-trained improvising and score-dependent musicians listened to short, unfamiliar two-part excerpts presented with headphones. They played along or replicated the excerpts by ear on a digital piano, either with or without aural feedback. In addition, they were asked to harmonize or transpose some of the excerpts either to a different key or to the relative minor. MIDI recordings of their performances were compared with recordings of the aural model. Concordance was expressed in an audiomotor alignment score computed with the help of music information retrieval algorithms. Significantly higher alignment scores were found when contrasting groups, voices, and tasks. The present study demonstrates the superior ability of improvising musicians to replicate both the pitch and rhythm of aurally perceived music at the keyboard, not only in the original key, but also in other tonalities. Taken together with the enhanced activation of the right dorsal frontoparietal network found in our previous fMRI study, these results underscore the conclusion that the practice of improvising music can be associated with enhanced audiomotor transformation in response to aurally perceived music.

DWRank: Learning concept ranking for ontology search

With the recent growth of Linked Data on the Web there is an increased need for knowledge engineers to find ontologies to describe their data. Only limited work exists that addresses the problem of searching and ranking ontologies based on a given query term. In this paper we introduce DWRank, a tw o-staged bi-directional graph walk ranking algorithm for concepts in ontologies. DWRank characterises two features of a concept in an ontology to determine its rank in a corpus, the centrality of the concept to the ontology within which it is defined (HubScore) and the authoritativeness of the ontology in which it is defined (AuthorityScore). DWRank then uses a Learning to Rank approach to learn the feature weights for the two aforementioned ranking strategies. We compare DWRank with state-of-the-art ontology ranking models and traditional information retrieval algorithms. This evaluation shows that DWRank significantly outperforms the best ranking models on a benchmark ontology collection for the majority of the sample queries defined in the benchmark. In addition, we compare the effectiveness of the HubScore part of our algorithm with the state-of-the-art ranking model to determine a concept centrality and show the improved performance of DWRank in this aspect. Finally, we evaluate the effectiveness of the design decisions made for the AuthorityScore method in DWRank to find missing inter-ontology links and present a graph-based analysis of the ontology corpus that shows the increased connectivity of the ontology corpus after extraction of the implicit inter-ontology links.

Supporting academic search tasks through citation visualization and exploration

Despite ongoing advances in information retrieval algorithms, people continue to experience difficulties when conducting online searches within digital libraries. Because their information-seeking goals are often complex, searchers may experience difficulty in precisely describing what they are seeking. Current search interfaces provide limited support for navigating and exploring among the search results and helping searchers to more accurately describe what they are looking for. In this paper, we present a novel visual library search interface, designed with the goal of providing interactive support for common library search tasks and behaviours. This system takes advantage of the rich metadata available in academic collections and employs information visualization techniques to support search results evaluation, forward and backward citation exploration, and interactive query refinement.

Representative Information Retrieval Algorithm Based on PageRank Algorithm and MapReduce Model

Representative information retrieval is widely used in public opinion management, mobile commerce and knowledge management. In the era of big data, the value of representative information extraction is particularly prominent. In order to further explore the application value of representative information extraction, this paper proposes a representative information retrieval method based on PageRank algorithm and MapReduce model (PM-Rep), research on effect of the representative coefficient λ to the extraction scale and the coverage and redundancy of the results, comparative analysis of the advantages and disadvantages of the similar methods. In the parameter experiment, as the λ increases, the scale of the extraction results increases, the coverage degree and the redundancy degree also increases. In the effect experiment, PM-Rep’s coverage is significantly better than Top-k, Heuristic and Random, and the redundancy of PM-Rep is the least. In the efficiency experiment, PM-Rep takes the least time in the four methods, embodies the advantages of PM-Rep method for massive data.

반복적 보정에 의한 파랑정보 추출 기법

반복적 보정에 의한 파랑정보 추출 기법

Comparison of Swarm Intelligence Techniques for Improved Information Retrieval System

Optimization is an important and critical step in the data mining process and it has a huge impact on the success of a data mining process. Selecting a set of feature which is optimal for a given task is a problem which plays an important role in a wide variety of context including pattern recognition, adaptive control and machine learning Clusters are formed of the reduced dataset using Swarm Intelligence Technique algorithms i.e. Particle Swarm Optimization(PSO),Ant Colony Optimization(ACO),Cluster Hypothesis is verified which is the intra cluster distance should be minimum and inter cluster distance should be maximum. Most relevant documents are stored i+n the clusters An Information Retrieval System is used for retrieval of data from the clusters. When user enters a query from a Graphical User Interface, using Information Retrieval algorithm the document is searched and retrieved from the clusters. It is then given as an output to the user

IRAFCA: an O(n) information retrieval algorithm based on formal concept analysis

With the exponential increase in the quantity of information circulating on the Internet, an evolution of information-retrieval systems becomes paramount. Indeed, current approaches for information systems design remain unable to meet the needs of users, either in performance (precision and recall) or response time. In this paper, we propose a new information-retrieval algorithm based on formal concept analysis. The proposed algorithm deals with disjunctive and conjunctive queries. In fact, information retrieval is a direct application of the formal concept analysis (FCA). This makes the adaptation of this theory to this field an easy and intuitive task. In this context, we exploited the theoretical basis provided by the FCA to design an efficient and flexible approach for information retrieval.

Fine-Tuning an Algorithm for Semantic Search Using a Similarity Graph

Given a set of documents and an input query that is expressed in a natural language, the problem of document search is retrieving the most relevant documents. Unlike most existing systems that perform document search based on keyword matching, we propose a method that considers the meaning of the words in the queries and documents. As a result, our algorithm can return documents that have no words in common with the input query as long as the documents are relevant. For example, a document that contains the words "Ford", "Chrysler" and "General Motors" multiple times is surely relevant for the query "car" even if the word "car" never appears in the document. Our information retrieval algorithm is based on a similarity graph that contains the degree of semantic closeness between terms, where a term can be a word or a phrase. Since the algorithms that constructs the similarity graph takes as input a myriad of parameters, in this paper we fine-tune the part of the algorithm that constructs the Wikipedia part of the graph. Specifically, we experimentally fine-tune the algorithm on the Miller and Charles study benchmark that contains 30 pairs of terms and their similarity score as determined by human users. We then evaluate the performance of the fine-tuned algorithm on the Cranfield benchmark that contains 1400 documents and 225 natural language queries. The benchmark also contains the relevant documents for every query as determined by human judgment. The results show that the fine-tuned algorithm produces higher mean average precision (MAP) score than traditional keyword-based search algorithms because our algorithm considers not only the words and phrases in the query and documents, but also their meaning.

Extended Semantic based Boolean Information Retrieval Algorithm for User-driven Query

Extended Semantic based Boolean Information Retrieval Algorithm for User-driven Query

Knowledge base Construction using Hidden Web Retrieval Technique

Relations of algorithms for hidden web-focused information retrieval develop with it. When the stage the information retrieval, a huge difficulty arises and that is the information that one hidden web page can enclose manifold areas with extremely dissimilar information content. Hence the page has to be split into measurement and these parts examined separately for the results to be more precise. Web page segmentation and correlated technique have Therefore this paper suggests a combined approach that creates use of structural features and the visual features. It build a visual DOM tree on which the data records are recognized based on their structural similarity .The structure of these data records are reserved so that personage data items can be group effortlessly and precisely based on their visual features Which hidden web source do we intend at the information indispensable to access the data at the back web form and the type of interface. We proposed algorithm narrative vision based page segmentation (NVIPS) and also comparison DOM tree, VIPS.

Data Mining Based Intelligent Retrieval Algorithm and its Application

Mathematical model of information retrieval algorithm retrieves digital libraries involved, it is very important to design an algorithm to make the best of the books, in order to extract the required information, including the association rules and classification method for from the database predicting the reader and potential use of fast and accurate information. In this paper, the intelligent data retrieval books mining algorithms to analyze, you can study the books of intelligent retrieval application, until the actual retrieval algorithm to solve the model first developed to meet the requirements, design a library of books intelligent information retrieval system .

Wordnet and Ontology Based Query Expansion for Semantic Information Retrieval in Sports Domain

Semantic Search has been a major longing factor from the envisage state of Semantic Web. Information on the Web is growing at a very rapid pace and has become quite voluminous over the past few years. Semantics of the query is not considered in Traditional Search system since it is a mere Keyword based Search. To increase the number of relevant documents retrieved, queries need to be disambiguated by looking at their context. A query expansion algorithm for Semantic Information Retrieval in Sports Domain (SIRSD) is proposed to do Semantic Search to improve search over large document repositories. This algorithm reformulates user queries by using Word Net and domain ontology to improve the returned results. Our proposal is illustrated with sample experiments showing improvements with respect to Traditional Search and providing ground for further research and discussion. SIRSD reduces the issue of semantic interoperability during the user query search. It has been inferred that there will be a higher value of average precision and recall for the Semantic Search system when compared to Traditional Search. The results show its effectiveness in generating a suitable number of query search with an accuracy of 87.1% compared to other competitors of generic search engines.

Intelligent Web Information Retrieval based on User Navigational Patterns

The foremost mission of any information retrieval algorithm is the efficient extraction of user interests. The rapid growth of web data, intense competition and user’s option to choose from several alternatives increases this issue. In this context Web usage mining can provide valuable contributions in terms of ideas and methods, as it fissures useful knowledge from the pattern of user interactions with the Web. The user interest can be identified by analyzing the access pattern of user browse, the web pages they save, collect, or print. These valuable information’s are available in server logs, which can be exploited to satisfy user needs by optimizing the document-retrieval task. This article is a review conducted in the field of web usage mining and its latest works for supporting the research on efficient information retrieval based on user access pattern. This survey analyzes 25 released information retrieval models to find out the major mining techniques applied in them and also to analyze the effect of diverse parameters like feedbacks, time, content, frequency etc in information retrieval. The goal of this survey is to find the best composition of features to be included in an efficient information retrieval model. Using those features a new retrieval model is then proposed. General Terms Information Retrieval, Web Usage Mining.

On Eugene Garfield and Indexing: A Biographical Review

Eugene Garfield was born in 1925, United States. He is a world-wide known American scientist. He is also one of the founders of bibliometrics and scientometrics, an art lover, a multilinguist, an entrepreneur, a musician and a prolific author. Garfield is mostly known around the world for ISI. Thanks to ISI, created by Garfield, researchers from all disciplines have been benefiting from a more systematic, faster, objective, interdisciplinary and extensive system while carrying out their researches and this index has enabled them to track down the origins of a scientific idea and led to creation of other information retrieval algorithms.

Frontiers of Legal Information: The U.S. Law Librarians of the Future*

It is necessary to guess a new probable future for law libraries and librarians. A future that is based on history, yet accounts for new contemporary backgrounds and data. A future that not only considers the widespread accessibility to the Internet and the incredible development of search algorithms for legal information retrieval on user-friendly platforms, but one that also considers the tremendous increase in the amount of digitized materials available to the average patron. Here, I will take an opportunity to look at the past and present and offer what I foresee as the future for U.S. law libraries and librarians.

How everyday language can and will boost effective information retrieval

Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice.We present an information retrieval (IR) algorithm specifically designed to accept everyday language. It integrates two paradigms of information retrieval, previously studied in isolation; one directed mainly at the surface structure of language, the other primarily at the underlying meaning.The integration was achieved by a Markov machine that encodes meaning by its transition graph, and surface structure by the language it generates. A rigorous evaluation of the approach showed, first, that it can compete with the quality of existing language models, second, that it is more effective the more verbose the input, and third, as a consequence, that it is promising for an imminent transition from keyword input, where the onus is on the user to formulate concise queries, to a modality where users can express more freely, more informal, and more natural their need for information in everyday language.

Multidimensional Ontology-Based Information Retrieval for Academic Counseling

Conventional information retrieval can only locate documents containing user specified keywords. Integrating domain ontology with information retrieval extends the keyword-based search to semantic search and thus potentially improves the precision and recall of the document retrieval. In this paper, a set of new multidimensional ontology-based information retrieval algorithms is proposed for searching both specific and related terms. In particular, the relevant data properties of an instance, the relevant concepts, the relevant related concepts, and the related instances of a given user query can be identified from the domain ontology via the multidimensional search. Using the proposed algorithms, an intelligent counselling system which provides 24x7 online academic counselling services is developed. Through an interactive user-interface and domain ontology, the system facilitates students to find desired information by reviewing and refining their query. The article also outlines how to enable ontology-based searching for a conventional website.

A query suggestion workflow for life science IR-systems.

Information Retrieval (IR) plays a central role in the exploration and interpretation of integrated biological datasets that represent the heterogeneous ecosystem of life sciences. Here, keyword based query systems are popular user interfaces. In turn, to a large extend, the used query phrases determine the quality of the search result and the effort a scientist has to invest for query refinement. In this context, computer aided query expansion and suggestion is one of the most challenging tasks for life science information systems. Existing query front-ends support aspects like spelling correction, query refinement or query expansion. However, the majority of the front-ends only make limited use of enhanced IR algorithms to implement comprehensive and computer aided query refinement workflows. In this work, we present the design of a multi-stage query suggestion workflow and its implementation in the life science IR system LAILAPS. The presented workflow includes enhanced tokenisation, word breaking, spelling correction, query expansion and query suggestion ranking. A spelling correction benchmark with 5,401 queries and manually selected use cases for query expansion demonstrate the performance of the implemented workflow and its advantages compared with state-of-the-art systems.

System of Intelligent Library Retrieval Based on Data Mining

Digital Library Retrieval involves the mathematical model of information retrieval algorithms, it is very important to design an algorithm to make the best books to extract the required information to involve the association rules and classification method for predicting the reader from the database and potentially fast and accurate information use problems. In this paper, the intelligent retrieval of books were analyzed using data mining algorithms can be studied books intelligent retrieval application, until practical algorithm to solve the retrieval model first developed fit the requirements and design a library information library books to achieve intelligent retrieval system.

A Query Suggestion Workflow for Life Science IR-Systems

Summary Information Retrieval (IR) plays a central role in the exploration and interpretation of integrated biological datasets that represent the heterogeneous ecosystem of life sciences. Here, keyword based query systems are popular user interfaces. In turn, to a large extend, the used query phrases determine the quality of the search result and the effort a scientist has to invest for query refinement. In this context, computer aided query expansion and suggestion is one of the most challenging tasks for life science information systems. Existing query front-ends support aspects like spelling correction, query refinement or query expansion. However, the majority of the front-ends only make limited use of enhanced IR algorithms to implement comprehensive and computer aided query refinement workflows. In this work, we present the design of a multi-stage query suggestion workflow and its implementation in the life science IR system LAILAPS. The presented workflow includes enhanced tokenisation, word breaking, spelling correction, query expansion and query suggestion ranking. A spelling correction benchmark with 5,401 queries and manually selected use cases for query expansion demonstrate the performance of the implemented workflow and its advantages compared with state-of-the-art systems.