Web information retrieval based on ontology

Term mismatch is a common limitation of traditional information retrieval (IR) models where relevance scores are estimated based on exact matching of documents and queries. Typically, good IR model should consider distinct but semantically similar words in the matching process. In this paper, we propose a method to incorporate word embedding (WE) semantic similarities into existing probabilistic IR models for Arabic in order to deal with term mismatch. Experiments are performed on the standard Arabic TREC collection using three neural word embedding models. The results show that extending the existing IR models improves significantly baseline bag-of-words models. Although the proposed extensions significantly outperform their baseline bag-of-words, the difference between the evaluated neural word embedding models is not statistically significant. Moreover, the overall comparison results show that our extensions significantly improve the Arabic WordNet based semantic indexing approach and three recent WE-based IR language models.

Web information retrieval is significantly more challenging than traditional well-controlled, small document collection information retrieval. One main difference between traditional information retrieval and Web information retrieval is the Web's hyperlink structure. This structure has been exploited by several of today's leading Web search engines, particularly Google and Teoma. In this survey paper, we focus on Web information retrieval methods that use eigenvector computations, presenting the three popular methods of HITS, PageRank, and SALSA.

Purpose The purpose of this paper is to provide an integrated semantic information retrieval (IR) solution based on an ontology-improved vector space model for situations where a digital collection is established or curated. It aims to create a retrieval approach which could return the results by meanings rather than by keywords. Design/methodology/approach In this paper, the authors propose a semantic term frequency algorithm to create a semantic vector space model (SeVSM) based on ontology. To support the calculation, a multi-branches tree model is created to represent the ontology and a set of algorithms is developed to operate it. Then, a semantic ontology-based IR system based on the SeVSM model is designed and developed to verify the effectiveness of the proposed model. Findings The experimental study using 30 queries from 15 different domains confirms the effectiveness of the SeVSM and the usability of the proposed system. The results demonstrate that the proposed model and system can be a significant exploration to enhance IR in specific domains, such as a digital library and e-commerce. Originality/value This research not only creates a semantic retrieval model, but also provides the application approach via designing and developing a semantic retrieval system based on the model. Comparing with most of the current related research, the proposed research studies the whole process of realizing a semantic retrieval.

Traditional Information Retrieval (IR) models are based on bag-of-words paradigm, where relevance scores are computed based on exact matching of keywords. Although these models have already achieved good performance, it has been shown that most of dissatisfaction cases in relevance are due to term mismatch between queries and documents. In this paper, we introduce novel method to compute term frequency based on semantic similarities using distributed representations of words in a vector space (Word Embeddings). Our main goal is to allow distinct but semantically related terms to match each other and contribute to the relevance scores. Hence, Arabic documents are retrieved beyond the bag-of-words paradigm based on semantic similarities between word vectors. The results on Arabic standard TREC data sets show significant improvement over the baseline bag-of-words models.

HyRa: An Effective Hybrid Ranking Model

The Information Retrieval (IR) problem can be described as the quest to find the set of relevant information objects corresponding to a given information need, which is represented by a request. IR begins with a user who wishes to satisfy an information need. The information need is typically formulated in the form of a request, denoted by q. The intention is that the request q be as good as possible description of the information need N. The information being queried is modelled as a set of information carriers, or documents. An often used premise in IR is the following: if a given document d is about the request q, then there is a high likelihood that d is relevant with respect to the associated information need. Thus, the IR problem is reduced to deciding the aboutness relation between documents and requests. Many IR models have been developed, and there is a wide variation in how they determine aboutness. A common approach is to equate aboutness with overlap, meaning if there is sufficient overlap between the document and the query, the document is deemed to be about the query. The degree of overlap determined by the retrieval model is usually expressed as a real number between zero (no overlap) and unity (complete overlap). In most models, “sufficient” overlap is not prescribed by the model but by experimentation within a given IR setting. Such experimentation may lead to the conclusion, say, that sufficient overlap is apparent when the overlap measure is greater than α, 0 < α ≤ 1. The value α varies from setting to setting.

Measuring the semantic similarity between phrases and sentences is an important task in natural language processing (NLP) and information retrieval (IR). We compare the quality of the distributional semantic NLP models against phrase-based semantic IR. The evaluation is based on the correlation between human judgements and model scores on a distributional phrase similarity task. We experiment with four NLP and two IR model variants. On the NLP side, models vary over normalization schemes and composition operators. On the IR side, models vary with respect to estimation of the probability of a term being in a document, namely P(t|d) where only term co-occurrence information is used and P(t|d, sim) which incorporates term distributional similarity. A mixture of the two methods is presented and evaluated. For both methods, word meanings are derived from large corpora of data: the BNC and ukWaC. One of the main findings is that grammatical distributional models give better scores than the IR models. This suggests that an IR model enriched with distributional linguistic information performs better in the long standing problem in IR of document retrieval where there is no direct symbolic relationship between query and document concepts.

In recent days, semantic web presents a key role in intelligent retrieval of information system that resolves vocabulary mismatch problem by query expansion process. However, achieving the scalable information retrieval (IR) in semantic web is a challenging issue in a large dataset. The semantic IR problem is addressed by an ontological-based semantic similarity measurement using natural language processing. The two novel algorithms namely syntactic correlation coefficient (SCC) and mapping-based K-nearest neighbour (M-KNN) for semantic similarity measurement is proposed which improves the accuracy of relevant result. The ontological constructs with word sense disambiguation (WSD) algorithm for document repository improves the conceptual relationships, reduces the ambiguities in ontology and improves scalability by intensely analysing the semantic relationship as well as dynamically reconstructing the ontology when numbers of documents are updated. Ranking is done with latent semantic analysis (LSA) after semantic similarity analysis, which improves the retrieved result and reduces the complexity in relevancy. The performance of the system is analysed with respect to different metrics such as processing time, F-measure (0.97), time complexity, precision (0.95), recall (0.98) and space complexity.

This chapter aims to master web mining and Information Retrieval (IR) in the digital age, thus describing the overviews of web mining and web usage mining; the significance of web mining in the digital age; the overview of IR; the concept of Collaborative Information Retrieval (CIR); the evaluation of IR systems; and the significance of IR in the digital age. Web mining can contribute to the increase in profits by selling more products and by minimizing costs. Web mining is the application of data mining techniques to discover the interesting patterns from web data in order to better serve the needs of web-based multifaceted applications. Mining web data can improve the personalization, create the selling opportunities, and lead to more profitable relationships with customers in global business. Web mining techniques can be applied with the effective analysis of the clearly understood business needs and requirements. Web mining builds the detailed customer profiles based on the transactional data. Web mining is used to create the personalized search engines which can recognize the individuals' search queries by analyzing and profiling the web user's search behavior. IR is the process of obtaining relevant information from a collection of informational resources. IR has considerably changed with the expansion of the Internet and the advent of modern and inexpensive graphical user interfaces and mass storage devices. The effective IR system, including an active indexing system, not only decreases the chances that information will be misfiled but also expedites the retrieval of information. Regarding IR utilization, the resulting time-saving benefit increases office efficiency and productivity while decreasing stress and anxiety. Most IR systems provide the advanced searching capabilities that allow users to create the sophisticated queries. The chapter argues that applying web mining and IR has the potential to enhance organizational performance and reach strategic goals in the digital age.

The dynamic nature of document relevance is largely ignored by traditional Information Retrieval (IR) models, which assume that scores (relevance) for documents given an information need are static. In this paper, we formulate a general Dynamical Information Retrieval problem, where we consider retrieval as a stochastic, controllable process. The ranking action continuously controls the retrieval system's dynamics and an optimal ranking policy is found that maximizes the overall users' satisfaction during each period. Through deriving the posterior probability of the documents evolving relevancy from user clicks, we can provide a plug-in framework for incorporating a number of click models, which can be combined with Multi-Armed Bandit theory and Portfolio Theory of IR to create a dynamic ranking rule that takes rank bias and click dependency into account. We verify the versatility of our algorithms in a number of experiments and demonstrate improved performance over strong baselines and as a result significant performance gains have been achieved.

Most research oriented search queries consist of multiple topics belonging to one or more domains of knowledge. The objective of such search queries is to find the relationship or impact that each topic has on the other(s). Though web search engines provide easy means to retrieve information off the web, search engines are mainly key word oriented and do not consider the different topics and relationships between such topics present in the results. This project presents a software solution, `SearchDroid' that acts as an intermediate user between a search engine and the end user, refining search results based on different topics identified in the search query and their presence and relationships depicted in search results. A discourse parsing approach has been used to build discourse structures that represent the rhetorical relations of text in search results; this is used to re-rank results based on topics identified in the search query. The project combines linguistics research under discourse parsing with web information retrieval techniques. The lack of literature combining discourse parsing techniques with web information retrieval has been compensated for by introducing a fresh algorithmic approach. An abstract information retrieval mechanism has been created with the use of discourse parsing techniques, and can be integrated into any web information retrieval approach. The system was evaluated by Linguistic Experts, Technical Experts and End users. All experts agreed that a discourse parsing approach was suitable for addressing the problem at hand and that the project had high research value in the aspect of linking linguistics research with web information retrieval research. Hands-on testing of the system by End Users produced high user acceptance of the proposed system.

Collaboration has been identified as an important aspect in information seeking. People meet to discuss and share ideas and through this interaction an information need is quite often identified. However the process of resolving this information need, through interacting with a search engine and performing a search task, is still an individual activity. We propose an environment which allows users to collaborate to satisfy a shared information need. We discuss ways to divide the search task amongst collaborators and propose the use of relevance feedback, a common information retrieval process, to enable the transfer of knowledge across collaborators during a search session. We describe the process by which co-searchers can collaborate effectively with little redundancy and how we can combine relevance judgements from multiple searchers into a coherent model for synchronous collaborative information retrieval

The rapid development of information technology offers people a broad information-sharing platform. We can get relevant information through information retrieval tools. In order to get higher search efficiency, the information retrieval technologies based on semantic grid are discussed. Based on analysis of the technologies of grid, semantic grid and information retrieval, the architecture of grid information retrieval system is brought out and a common model of grid information retrieval is established. The grid platform based on Globus toolkit is built which is made as the basic development platform of the system, the semantic grid information retrieval system under that platform is realized, and the designed model and algorithm are implemented and the specific experimental tests is realized. The result shows that the system has better retrieval recall factor and pertinency factor than the conventional retrieval methods, and the feasibility of system model is verified.

The Web has become a large repository of documents (or pages) written in many different languages. In this context, traditional information retrieval (IR) techniques cannot be used whenever the user query and the documents being retrieved are in different languages. To address this problem, new cross‐language information retrieval (CLIR) techniques have been proposed. In this work, we describe a method for cross‐language retrieval of medical information. This method combines query terms and related medical concepts obtained automatically through a categorization procedure. The medical concepts are used to create a linguistic abstraction that allows retrieval of information in a language‐independent way, minimizing linguistic problems such as polysemy. To evaluate our method, we carried out experiments using the OHSUMED test collection, whose documents are written in English, with queries expressed in Portuguese, Spanish, and French. The results indicate that our cross‐language retrieval method is as effective as a standard vector space model algorithm operating on queries and documents in the same language. Further, our results are better than previous results in the literature.

The exploding number of computational models produced by Systems Biologists over the last years is an invitation to structure and exploit this new wealth of information. Researchers would like to trace models relevant to specific scientific questions, to explore their biological content, to align and combine them, and to match them with experimental data. To automate these processes, it is essential to consider semantic annotations, which describe their biological meaning. As a prerequisite for a wide range of computational methods, we propose general and flexible similarity measures for Systems Biology models computed from semantic annotations. By using these measures and a large extensible ontology, we implement a platform that can retrieve, cluster, and align Systems Biology models and experimental data sets. At present, its major application is the search for relevant models in the BioModels Database, starting from initial models, data sets, or lists of biological concepts. Beyond similarity searches, the representation of models by semantic feature vectors may pave the way for visualisation, exploration, and statistical analysis of large collections of models and corresponding data.