Semantic Information Retrieval System Research Articles

A computational semantic information retrieval model for Vietnamese texts

Semantic information retrieval system for text documents aims at retrieving text documents containing the similar semantic representation to the query. Semantic representation of text can be vector or dependency graph depending on the approach of semantic analysis. This paper proposes a model of semantic information retrieval for Vietnamese to retrieve similar texts to a query. In the proposed system, the semantic analysis is to identify the semantic dependency graph of sentences and the retrieving process computes the relevance of text document with these semantic dependency graphs. For identifying the semantic dependency graph of a sentence, the transformation rules are studied to apply on dependency parse using lexicon ontology for Vietnamese. For ranking retrieval results, the Jaccard-Tanimoto distance is applied to the ranking function. The evaluation shows that the proposed model has higher MAP (0.4045) than MAP of BM25 model (0.3825) and of TF.IDF model (0.3688).

A computational semantic information retrieval model for Vietnamese texts

Semantic information retrieval system for text documents aims at retrieving text documents containing the similar semantic representation to the query. Semantic representation of text can be vector or dependency graph depending on the approach of semantic analysis. This paper proposes a model of semantic information retrieval for Vietnamese to retrieve similar texts to a query. In the proposed system, the semantic analysis is to identify the semantic dependency graph of sentences and the retrieving process computes the relevance of text document with these semantic dependency graphs. For identifying the semantic dependency graph of a sentence, the transformation rules are studied to apply on dependency parse using lexicon ontology for Vietnamese. For ranking retrieval results, the Jaccard-Tanimoto distance is applied to the ranking function. The evaluation shows that the proposed model has higher MAP (0.4045) than MAP of BM25 model (0.3825) and of TF.IDF model (0.3688).

Integrating Extended Gaia Semantic Information Retrieval Model with Android Based System using Native API

In present scenario software industry becomes more advanced. We all know that for developing software system there are many latest technologies available like Agile Software development , Software Agent, Semantic Web ,IOT , Cloud Computing etc. In this paper author tries to provide implementation of Extended GAIA Semantic information Retrieval System. E.G.S.I.R. is basically combination of software agent and semantic web features.

Intelligent ontology based semantic information retrieval using feature selection and classification

Semantic information retrieval provides more relevant information to the user query by performing semantic analysis. In such a scenario, knowledge representation using ontology can provide effective semantic retrieval facility which is more efficient than representation using semantic networks and frames. The existing information retrieval systems have been developed to handle very large volume of data and information stored in text format. On the other hand, the information available in the current web based applications such as Facebook and twitter grow very fast and hence the existing information retrieval systems consume large amount of time for relevant information retrieval. Moreover, most of the existing search engines use syntactic approach for information retrieval and use page ranking algorithms to measure the relevancy score. However, such approach is not able to provide more accurate results in terms of relevancy. Therefore, a new semantic information retrieval system is proposed in this paper which uses feature selection and classification for enhancing the relevancy score which is performed in this work by proposing a new intelligent fuzzy rough set based feature selection algorithm and an intelligent ontology and Latent Dirichlet Allocation based semantic information retrieval algorithm. The main advantages of the proposed algorithms are the increase in relevancy, ability to handle big data and fast retrieval.

A personalised user preference and feature based semantic information retrieval system in semantic web search

A personalised user preference and feature based semantic information retrieval system in semantic web search

A personalised user preference and feature based semantic information retrieval system in semantic web search

In this work, an ontology based Semantic Supported Information Retrieval System (SIRS) is introduced, in which the users give the input query which is determined by the Hypertext Markup Language (HTML) Parser then Probabilistic Latent Semantic Indexing (PLSI) algorithm is utilised to gathered the details in an effective manner. These conversations are preceded, along with the assistance of the field concept of the pre-existing domain ontologies, a mediator thesaurus and determine semantic association amongst them in nature. The proposed work concentrates on resolving the web search issues and also concentrates on resolving the personalised web search. So the SIRS will be correlated to the personalised search with item features, termed as Personalised User Preference and Feature based Semantic Information Retrieval System (PUFSIRS) architecture. PUFSIRS performs according to the particle agent, which accomplishes the SIRS based on the curiosity of the user through Multi-Criteria Particle Swarm Optimisation (MCPSO) for giving user's personal interest. The appropriate details for the semantic query are gathered and categorised based on the pertinence of an MCPSO procedure. The results show that the proposed PUFSIRS architecture can enhance the accuracy and efficiency for gathering the appropriate web-records which are collected from various fields such as food, education, news and healthcare to the current schemes. The result of the proposed PUFSIRS architecture is measured in terms of precision, recall, F-measure, accuracy and processing time.

Towards a hybrid semantic similarity measure to set the conceptual relatedness in a hierarchy

Assessment of semantic similarity between concepts is of great importance in many applications dealing with textual data, such as natural language processing, knowledge acquisition, document semantic annotation and information retrieval systems. Moreover, to extract similar concepts from multiple ontologies, there is a real need to develop a conceptual similarity measure, the intention of finding semantic similarity in a given hierarchy, is to enhance the integration and retrieval of heterogeneous information resources in a more meaningful and accurate way. In this paper we present a hybrid approach for measuring the semantic similarity, in an attempt to address a new issue that focuses on the sensitivity of the similarity measure between concepts in a hierarchy. Based on the notions of both distance and the information content, the measure is expected to provide more consistent and accurate measures.

Implementation of semantic information retrieval system in mobile environment

Implementation of semantic information retrieval system in mobile environment

Semantic Information Retrieval Based on Domain Ontology

Most of the current search engines are based on the keyword search and it returns a large amount of irrelevant information in response to the user query. This is because of the keyword matching does not consider the semantics of the keyword term. Ontology has been increasingly used to efficiently capture the semantics of the information which overcomes the problems associated with traditional keyword search. The importance of ontology is identified in the area of building semantic information retrieval systems for the specific domain and gains greater significance over the tradition keyword search.

A Mobile P2P Semantic Information Retrieval System with Effective Updates

As the technologies advance, mobile peer-to-peer (MP2P) networks or systems become one of the major ways to share resources and information. On such a system, the information retrieval (IR), including the development of scalable infrastructures for indexing, becomes more complicated due to a huge increase on the amount of information and rapid information change. To keep the systems on MP2P networks more reliable and consistent, the index structures need to be updated frequently. For a semantic IR system, the index structure is even more complicated than a classic IR system and generally has higher update cost. The most well-known indexing technique used in semantic IR systems is Latent Semantic Indexing (LSI), of which the index structure is generated by singular value decomposition (SVD). Although LSI performs well, updating the index structure is not easy and time consuming. In an MP2P environment, which is fully distributed and dynamic, the update becomes more challenging. In this work, we consider how to update the sematic index generated by LSI and keep the index consistent in the whole MP2P network. The proposed Concept Space Update (CSU) protocol, based on distributed 2-Phase locking strategy, can effectively achieve the objectives in terms of two measurements: coverage speed and update cost. Using the proposed effective synchronization mechanism with the efficient updates on the SVD, re-computing the whole index on the P2P overlay can be avoided and the consistency can be achieved. Simulated experiments are also performed to validate our analysis on the proposed CSU protocol. The experimental results indicate that CSU is effective on updating the concept space with LSI/SVD index structure in MP2P semantic IR systems.

A personalised semantic and spatial information retrieval system based on user's modelling and accessibility measure

Search personalisation is a multi-criteria decision problem whose objective is to filter out relevant information based on a set of criteria. Web personalisation can be seen as an interdisciplinary field whose objective is to facilitate the interaction between web content and users’ needs. The personalised search focuses on integrating users’ contexts, needs and relevancy criteria in the information retrieval process. This paper presents user’s modelling for personalised spatial and semantic information retrieval. The idea is to provide a user with personalised results based on his model and on the neighbour users’ models. The spatial personalisation search is based on a measure of spatial accessibility, whose objective is to predict and evaluate location relevancy, accessibility as well as associations at the user level. Experiments confirm the effectiveness of our proposal by pointing out the improvement of the personalised search results when compared to a baseline web search.

A novel semantic information retrieval system based on a three-level domain model

This paper presents a methodology and a prototype for extracting and indexing knowledge from natural language documents. The underlying domain model relies on a conceptual level (described by means of a domain ontology), which represents the domain knowledge, and a lexical level (based on WordNet), which represents the domain vocabulary. A stochastic model (the ME-2L-HMM2, which mixes – in a novel way – HMM and maximum entropy models) stores the mapping between such levels, taking into account the linguistic context of words. Not only does such a context contain the surrounding words; it also contains morphologic and syntactic information extracted using natural language processing tools. The stochastic model is then used, during the document indexing phase, to disambiguate word meanings. The semantic information retrieval engine we developed supports simple keyword-based queries, as well as natural language-based queries. The engine is also able to extend the domain knowledge, discovering new and relevant concepts to add to the domain model. The validation tests indicate that the system is able to disambiguate and extract concepts with good accuracy. A comparison between our prototype and a classic search engine shows that the proposed approach is effective in providing better accuracy.

Personalized Semantic Retrieval and Summarization of Web Based Documents

The current retrieval methods are essentially based on the string-matching approach lacking of semantic information and can’t understand the user's query intent and interest very well. These methods do regard as the personalization of the users. Semantic retrieval techniques are performed by interpreting the semantic of keywords. Using the text summarization allows a user to get a sense of the content of a full-text, or to know its information content, without reading all sentences within the full-text. In this paper, a semantic personalized information retrieval (IR) system is proposed, oriented to the exploitation of Semantic Web technology and WordNet ontology to support semantic IR capabilities in Web documents. In a proposed system, the Web documents are represented in concept vector model using WordNet. Personalization is used in a proposed system by building user model (UM). Text summarization in a proposed system is based on extracting the most relevant sentences from the original document to form a summary using WordNet. The examination of the proposed system is performed by using three experiments that are based on relevance based evaluation. The results of the experiment shows that the proposed system, which is based on Semantic Web technology, can improve the accuracy and effectiveness for retrieving relevant Web documents.

Performance assessment of a semantic information retrieval system using stagnant and active images

Videos, images, graphics and text are the main multimedia data types. Generally, one document includes two or more data types because each data type enriches the meaning of document. For this reason, the documents become more complex for querying when compared with only text querying. To overcome this difficulty, some new information retrieval systems have been developed. In this study, an object action concept (OAC) based semantic information retrieval system for text and image data by using Fuzzy ontology was considered. Assessment of the performance of the retrieval systems is highly important. The purpose of this study is to assess the performance of an object action concept based semantic information retrieval system when using stagnant and active images. When considering the precision recall, precision and rnorm parameters, it is clear that the performance of the system for the stagnant images is higher than that for the active images. Key words: Fuzzy domain ontology, image and text retrieval, semantic querying.

Agricultural Policy-Oriented Ontology-Based Semantic Information Retrieval

Research on architecture of ontology-based information semantic representation and Retrieval is done. As a case study, a prototype for agricultural policy-oriented ontology-based semantic information retrieval system (APOSIRS) is established. Ontology plays a role that providing a shared terminology and supporting for the retrieval process. The architecture allows APOSIRS-based applications to perform automatic semantic information Retrieval of agricultural policy text at more length: automatic and dynamic semantic annotation of unstructured and semi-structured content, semantically-enabled information extraction, indexing, retrieval, as well as ontology management, such as querying and modifying the underlying ontology and knowledge bases. Main components of this architecture have been implemented and their results are reported.

Agent-Community-based P2P semantic MyPortal information retrieval system architecture

In this paper, we propose a conceptual architecture for a personal semantic Web information retrieval system. It incorporates semantic Web, Web service, P2P and multi-agent technologies to enable n...

Improving life sciences information retrieval using semantic web technology

The ability to retrieve relevant information is at the heart of every aspect of research and development in the life sciences industry. Information is often distributed across multiple systems and recorded in a way that makes it difficult to piece together the complete picture. Differences in data formats, naming schemes and network protocols amongst information sources, both public and private, must be overcome, and user interfaces not only need to be able to tap into these diverse information sources but must also assist users in filtering out extraneous information and highlighting the key relationships hidden within an aggregated set of information. The Semantic Web community has made great strides in proposing solutions to these problems, and many efforts are underway to apply Semantic Web techniques to the problem of information retrieval in the life sciences space. This article gives an overview of the principles underlying a Semantic Web-enabled information retrieval system: creating a unified abstraction for knowledge using the RDF semantic network model; designing semantic lenses that extract contextually relevant subsets of information; and assembling semantic lenses into powerful information displays. Furthermore, concrete examples of how these principles can be applied to life science problems including a scenario involving a drug discovery dashboard prototype called BioDash are provided.

자동화된 통합 프레임워크를 위한 시맨틱 웹 기반의 정보 검색 시스템

자동화된 통합 프레임워크를 위한 시맨틱 웹 기반의 정보 검색 시스템

User models and world models for data, information, and knowledge

In this article we identify the need for a new theory of data, information, and knowledge. A model is developed that distinguishes between data as directly observable facts, information as structured collections of data, and knowledge as methods of using information. The model is intended to support a wide range of information systems. In the article we develop the use of the model for a semantic information retrieval system using the concept of semantic categories. The likely benefits of this are discussed, though as yet no detailed evaluation has been conducted.