Concept based Web Information Retrieval

2005

It is well known that ontologies will become a key piece, as they allow making the semantics of Semantic Web content [16] explicit. In spite of the big advantages that the Semantic Web promises, there are still several problems to solve. Those concerning ontologies include their availability, development and evolution. In the area of information retrieval, the dimension of document vectors plays an important role. Firstly, with higher index dimensions the indexing structures suffer from the "curse of dimensionality" and their efficiency rapidly decreases. Secondly, we may not use exact words when looking for a document, thus we miss some relevant documents. LSI is a numerical method, which discovers latent semantics in documents by creating concepts from existing terms. In this paper we present a basic method of mapping LSI concepts on given ontology (WordNet), used both for retrieval recall improvement and dimension reduction. We offer experimental results for this method on a subset of

Information Processing & Management, 2005

Traditional index weighting approaches for information retrieval from texts depend on the term frequency based analysis of the text contents. A shortcoming of these indexing schemes, which consider only the occurrences of the terms in a document, is that they have some limitations in extracting semantically exact indexes that represent the semantic content of a document. To address this issue, we developed a new indexing formalism that considers not only the terms in a document, but also the concepts. In this approach, concept clusters are defined and a concept vector space model is proposed to represent the semantic importance degrees of lexical items and concepts within a document. Through an experiment on the TREC collection of Wall Street Journal documents, we show that the proposed method outperforms an indexing method based on term frequency (TF), especially in regard to the few highest-ranked documents. Moreover, the index term dimension was 80% lower for the proposed method than for the TF-based method, which is expected to significantly reduce the document search time in a real environment.

— Retrieving proper information from internet is a huge task due to the high amount of information available there. Identifying the individual concepts according to the queries is time consuming. To retrieve documents, keyword based retrieval method was used before. Using this type searching, the relationship between associated keywords can't be identified. If the same concept is described by different keywords, inaccurate and improper results will be retrieved. Concept based retrieval methods are the solution for this scenario. This gives the benefit of getting semantic relationships among concepts in finding relevant documents. Irrelevant documents can be eliminated by detecting conceptual mismatches, which is another benefit obtained from this. The main challenges identified are the ambiguity occurring due to multiple nature of words for the same concepts. Semantic analysis can reveal the conceptual relationships among words in a given document. In this paper the potential of concept-based information access via semantic analysis is explored with the help of a lexical database called WordNet. The mechanism is applied in the selected text documents and extracting the Synonym, Hyponym, Hypernym of each word from WordNet. The ranking will be calculated after checking the frequency rate of each word in the input documents and a hierarchy model will be generated according to the ranking.

Traditional information retrieval systems rely on keywords to index documents and queries. In such systems, documents are retrieved based on the number of shared keywords with the query. This lexical-focused retrieval leads to inaccurate and incomplete results when different keywords are used to describe the documents and queries. Semantic-focused retrieval approaches attempt to overcome this problem by relying on concepts rather than on keywords to indexing and retrieval. The goal is to retrieve documents that are semantically relevant to a given user query. This paper addresses this issue by proposing a solution at the indexing level. More precisely, we propose a novel approach for semantic indexing based on concepts identified from a linguistic resource. In particular, our approach relies on the joint use of WordNet and WordNetDomains lexical databases for concept identification. Furthermore, we propose a semantic-based concept weighting scheme that relies on a novel definition of concept centrality. The resulting system is evaluated on the TIME test collection. Experimental results show the effectiveness of our proposition over traditional IR approaches.

Series in Machine Perception and Artificial Intelligence, 2004

Retrieving relevant information is a crucial component of cased -based reasoning systems for Internet applications such as search engines. The task is to use user-defined queries to retrieve useful information according to certain measures. Even though techniques exist for locating exact matches, finding relevant partial matches might be a problem. The objective of this paper is to develop an intelligent computer system with some deductive capabilities to conceptually cluster, match and rank pages based on predefined linguistic formulations and rules defined by experts or based on a set of known homepages. The Conceptual Fuzzy Set (CFS) model will be used for intelligent information and knowledge retrieval through conceptual matching of both text and links (here defined as "Concept"). The selected query doesn't need to match the decision criteria exactly, which gives the system a more human-like behavior. The model can be used for intelligent information and knowledge retrieval through Web-connectivity -based clustering. We will also present the integration of our technology into commercial search engines such as Google ™ as a framework that can be used to integrate our model into any other commercial search engines, or

2017 4th International Conference on New Media Studies (CONMEDIA)

Now finding relevant information is absolutely needed by users of the information. Actually, finding relevant information is more difficult than just looking for information. The presence information retrieval (IR) system is a system for automatically searching the relevant information to the user needs, which is expressed in the required query, and this query will be used as an input and processed by IR system and then will get relevant documents to the required query. One of methods of finding relevant information by query is term weighting by both query and document. The commonly used methods are the term weighting of local and global weighting, while the local term weighting algorithms that are used such as TF, TF logarithmic, binary TF and augmented TF and global weighting is IDF, IDFP, IDFB. In this research, besides of comparison of term weighting algorithms, it is also proposed new improvement in term weighting algorithms by adding global weight value that changes base 10 lo...

The diverse and huge volume of information available over networks makes it difficult for the user to find their accurate information need. Previous information retrieval system mostly uses keywords to retrieve and index the documents, which may return inaccurate result when different keywords are employed to express the same concept in the document and queries presented by the user. However, to overcome the problem of retrieving accurate information on the Web intelligently, current information retrieval approaches need to be improved. Concept-based information retrieval are playing major role in retrieving meaningful information intelligently. In this paper, we propose a novel concept-based approach using Wikipedia-based Explicit Semantic Analysis and Ontology. We propose approach that first specifies the given context of query words semantically by using Ontology, which contains Wikipedia-based concept to query words disambiguation, and then extracts corresponding concepts for each word based on the given context. As a result the retrieved documents will be more relevant to the user query and Concept-based information retrieval systems performance will improve.

2009

This paper 1 presents a method, SemSim, for the semantic search and retrieval of digital resources (DRs) that have been previously annotated. The annotation is performed by using a set of characterizing concepts, referred to as features, selected from a reference ontology. The proposed semantic search method requires that the features in the ontology are weighted. The weight represents the probability that a resource is annotated with the associated feature. The SemSim method operates in three stages. In the first stage, the similarity between concepts (consim) is computed by using their weights. In the second stage, the concept weights are used to derive the semantic similarity (semsim) between a user request and the DRs. In the last stage, the answer is returned in the form of a ranked list. An experiment aimed at assessing the proposed method against a few among the most popular competing solutions is given. 2

1999

Most engines used for searching information resources via the Internet employ the Boolean Retrieval Model. Two main drawbacks of this model are that users have difficulty to precisely formulate their concept (or, topic) of interest using Boolean logic and the resulting output is not ranked. We propose to address both these problems by employing a Concept-based Retrieval Model, where a concept is defined by a set of production rules and the rule-base is represented as a rule-base tree. Features of a prototype developed at USL, referred to as the Concept-Set Structuring System (CS 3 ), which includes a graphical interface for defining and refining rule-base trees and for converting them into equivalent sets of conjunctions, called Minimal Term Sets (MTSs), are described. By submitting MTSs generated for a concept to an existing search engine and by reordering the returned results according to the importance of MTSs they satisfy, the CS 3 prototype enhances the capabilities of the underlying search engine. Results that demonstrate the use of the prototype, coupled with DOE Information-Bridge, will be presented.

2013

This paper presents a functional approach towards the problem domain of Information Retrieval System built upon a narration based search text. The presented system retrieves documents from the background collection by extracting the domain specific significant keywords and other relevant phrases from a given narrative search text. The narrative search text can be a description or scenario which poses a great difficulty in the problem domain to retrieve the relevant document sets with efficiency and accuracy from the background data repository. We have adopted a different approach where the significant keywords are extracted from the narration text to form a search query and alternative sets of queries are also formulated by expanding the search query from a Conceptual Semantic Network built for the purpose. Inclusion of the Conceptual Semantic Network and WordNet synonym sets for the search query expansion plays an important role in the retrieval mechanism. Experiments were carried ...