Vector Space Model Approach Research Articles

Vector space model, term frequency-inverse document frequency with linear search, and object-relational mapping Django on hadith data search

For Muslims, the Hadith ranks as the secondary legal authority following the Quran. This research leverages hadith data to streamline the search process within the nine imams’ compendium using the vector space model (VSM) approach. The primary objective of this research is to enhance the efficiency and effectiveness of the search process within Hadith collections by implementing pre-filtering techniques. This study aims to demonstrate the potential of linear search and Django object-relational mapping (ORM) filters in reducing search times and improving retrieval performance, thereby facilitating quicker and more accurate access to relevant Hadiths. Prior studies have indicated that VSM is efficient for large data sets because it assigns weights to every term across all documents, regardless of whether they include the search keywords. Consequently, the more documents there are, the more protracted the weighting phase becomes. To address this, the current research pre-filters documents prior to weighting, utilizing linear search and Django ORM as filters. Testing on 62,169 hadiths with 20 keywords revealed that the average VSM search duration was 51 seconds. However, with the implementation of linear and Django ORM filters, the times were reduced to 7.93 and 8.41 seconds, respectively. The recall@10 rates were 79% and 78.5%, with MAP scores of 0.819 and 0.814, accordingly.

Journal Classification Using Cosine Similarity Method on Title and Abstract with Frequency-Based Stopword Removal

Classification of economic journal articles has been done using the VSM (Vector Space Model) approach and the Cosine Similarity method. The results of previous studies are considered to be less optimal because Stopword Removal was carried out by using a dictionary of basic words (tuning). Therefore, the omitted words limited to only basic words. This study shows the improved performance accuracy of the Cosine Similarity method using frequency-based Stopword Removal. The reason is because the term with a certain frequency is assumed to be an insignificant word and will give less relevant results. Performance testing of the Cosine Similarity method that had been added to frequency-based Stopword Removal was done by using K-fold Cross Validation. The method performance produced accuracy value for 64.28%, precision for 64.76 %, and recall for 65.26%. The execution time after pre-processing was 0, 05033 second.

Application of Parallel Vector Space Model for Large-Scale DNA Sequence Analysis

With rapid advancement in the field of bioinformatics and computational biology, the collected DNA dataset is growing exponentially, doubling after every 18 months. Due to large-scale and complex structure of the DNA dataset, the analysis of DNA sequence is becoming computationally a challenging issue in bioinformatics field and computational biology. Fast algorithms, capable of analyzing large-scale DNA sequence, are now required in the field of bioinformatics. This paper presents a novel Parallel Vector Space Model (PVSM) approach that supports the analysis of large-scale DNA sequence by taking advantages of multi-core system. The proposed approach is built on top of modified Vector Space Model (VSM). In order to evaluate the performance of PVSM, the proposed technique is extensively evaluated using varied size of DNA sequences in the context of computational efficiency and accuracy. The performance of PVSM is compared with sequential modified VSM. The sequential VSM is implemented on a single processor whereas, the proposed method is initially parallelized on 4 processors and subsequently on 12 processors. The experimental results show that the PVSM performed better than the sequential VSM. The proposed method achieved approximately 2× speedup compared with sequential approach, without affecting the accuracy level. Moreover, the proposed PVSM is highly scalable with an increase in the number of processing cores and support the analysis of large-scale DNA sequences.

Latent association rule cluster based model to extract topics for classification and recommendation applications

The quality of any text mining technique is highly dependent on the features that are used to represent the document collection. A classical form of document representation is the vector space model (VSM), according to which the documents are represented as vectors of weights that correspond to the features of the documents. The bag-of-words model is the most popular VSM approach due to its simplicity and general applicability, but this model does not include term dependency and has a high dimensionality. In the literature, several models for document representation have been proposed in order to capture the dependency of terms. Among them, the topic model representation is one of the most interesting approaches - since it describes the collection of documents in a way that reveals their internal structure and the interrelationships therein, and also provides a dimensionality reduction. However, even for topic models, the efficient extraction of information concerning the relations among terms for document representation is still a major research challenge. In order to address this issue, we proposed thelatent association rule cluster based model (LARCM). The LARCM is a non-probabilistic topic model that makes use of association rule clustering to build a document representation with low dimensionality in such a way that each feature (i.e., topic) is comprised of information concerning relations among the terms. We evaluated the interpretability of the topics obtained by using our proposed model against the ones provided by the traditional latent dirichlet allocation (LDA) model and the LDA model using a document representation that includes correlated terms (i.e., bag-of-related-words). The experimental results indicated that the LARCM provides topics with better interpretability than the LDA models. Additionally, we used the topics obtained by the LARCM in two different applications: text classification and page recommendation. With respect to text classification, the topics were used to improve document collection representation. Concerning page recommendation, topics were used as contextual information in context-aware recommender systems. Results have shown that the topics provided by the LARCM can be used to improve both applications.

Monitoring Class Activity and Predicting Student Performance Using Moodle Action Log Data

Purpose – This paper proposes a novel approach for processing course log data obtained from Moodle-based blended courses in order to visualize patterns of student activity within the online environment and to determine whether these log data can be used to predict student academic performance.Method – Logs of student activities were summarized and processed using the Vector Space Model approach. This resulted in a novel vector-based form of representation which can be used to map students’ activity in a latent activity space given a set of activity dimensions. An enriched form of this representation was also generated by processing the DateTime and IP address metadata for the purpose of developing classification/predictive model of students’ performance. Results – The activity space coupled with a one-hot vector representation for each unique activity dimension can be used to visualize the differences in level and type of activity preferences of students. Experiments using several machine learning algorithms indicate that the generated model can modestly distinguish between sets of activities that lead to High, Low, or Failed performances.Conclusion – The development of easily interpretable graphics that can depict trends in student activity is a useful tool for instructors handling blended courses. It can provide constant monitoring of course progression with minimal effort and enable instructors to determine whether and how the environment actually affects student performance.Recommendations – Further work on refining the process applied to the data is recommended. The log data should be time-sliced and processed to determine whether and how the student’s level and type of activity changes over time. More powerful machine learning classification techniques shouldalso be tested to determine whether it can improve the classification accuracy.Research Implications – These types of visualizations and predictive models could be used to monitor the student or class which requires immediate and specific pedagogical adjustments.

Document Clustering Using an Ontology-Based Vector Space Model

This paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched Semantic Vectors, using the classical vector space model approach extended with ontological support. One of the primary research challenges addressed here relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word-based information in the document. This research explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents. The relevant achievements pursued by this work are the following: (i) conceptualization of a model that enables the semantic enrichment of knowledge sources supported by domain experts; and (ii) implementation of a proof-of-concept, named SENSE (Semantic Enrichment knowledge Sources).

Management of Knowledge Sources Supported by Domain Ontologies: Building and Construction Case Studys

SummaryThis paper introduces a novel conceptual framework to support the creation of knowledge representations based on enriched semantic vectors, using the classical vector space model approach extended with ontological support. This work is focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target and knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The test bed for the assessment of the approach comes from the Building and Construction sector, which is challenged with significant problems for exchanging, sharing and integrating information among actors. Semantic gaps or lack of meaning definition at the conceptual and technical levels, for example, are problems fundamentally originated through the employment of representations to map the ‘world’ into models in an endeavour to anticipate other actors’ views, vocabulary and even motivations. One of the primary research challenges addressed in this work relates to the process of formalization and representation of document contents, where most existing approaches are limited and only take into account the explicit, word‐based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched through incorporation of implicit information derived from the complex relationships (semantic associations) modelled by domain ontologies with the addition of information presented in documents, by providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed. Copyright © 2015 John Wiley & Sons, Ltd.

A noun-based approach to feature location using time-aware term-weighting

ContextFeature location aims to identify the source code location corresponding to the implementation of a software feature. Many existing feature location methods apply text retrieval to determine the relevancy of the features to the text data extracted from the software repositories. One of the preprocessing activities in text retrieval is term-weighting, which is used to adjust the importance of a term within a document or corpus. Common term-weighting techniques may not be optimal to deal with text data from software repositories due to the origin of term-weighting techniques from a natural language context. ObjectiveThis paper describes how the consideration of when the terms were used in the repositories, under the condition of weighting only the noun terms, can improve a feature location approach. MethodWe propose a feature location approach using a new term-weighting technique that takes into account how recently a term has been used in the repositories. In this approach, only the noun terms are weighted to reduce the dataset volume and avoid dealing with dimensionality reduction. ResultsAn empirical evaluation of the approach on four open-source projects reveals improvements to the accuracy, effectiveness and performance up to 50%, 17%, and 13%, respectively, when compared to the commonly-used Vector Space Model approach. The comparison of the proposed term-weighting technique with the Term Frequency-Inverse Document Frequency technique shows accuracy, effectiveness, and performance improvements as much as 15%, 10%, and 40%, respectively. The investigation of using only noun terms, instead of using all terms, in the proposed approach also indicates improvements up to 28%, 21%, and 58% on accuracy, effectiveness, and performance, respectively. ConclusionIn general, the use of time in the weighting of terms, along with the use of only the noun terms, makes significant improvements to a feature location approach that relies on textual information.

Facilitating knowledge sharing and reuse in building and construction domain: an ontology-based approach

This paper brings a contribution focused on collaborative engineering projects where knowledge plays a key role in the process. Collaboration is the arena, engineering projects are the target, knowledge is the currency used to provide harmony into the arena since it can potentially support innovation and, hence, a successful collaboration. The building and construction domain is challenged with significant problems for exchanging, sharing and integrating information between actors. For example, semantic gaps or lack of meaning definition at the conceptual and technical level, are problems fundamentally created through the employment of representations to map the `world' into models in an endeavour to anticipate different actors' views, vocabulary, and objectives. One of the primary research challenges addressed in this work is the process of formalization and representation of document content, where most existing approaches are limited in their capability and only take into account the explicit, word-based information in the document. The research described in this paper explores how traditional knowledge representations can be enriched by incorporation of implicit information derived from the complex relationships (the Semantic Associations) modelled by domain ontologies combined with the information presented in documents, thereby providing a baseline for facilitating knowledge interpretation and sharing between humans and machines. The paper introduces a novel conceptual framework for representation of knowledge sources, where each knowledge source is semantically represented (within its domain of use) by a Semantic Vector. This work contributes to the enrichment of Semantic Vectors, using the classical vector space model approach extended with ontological support, employing ontology concepts and their relations in the enrichment process. The test bed for the assessment of the approach is the Building and Construction industry, using an appropriate B&C domain Ontology. Preliminary results were collected using a clustering algorithm for document classification, which indicates that the proposed approach does improve the precision and recall of classifications. Future work and open issues are also discussed.

Leveraging concept-based approaches to identify potential phyto-therapies

The potential of plant-based remedies has been documented in both traditional and contemporary biomedical literature. Such types of text sources may thus be sources from which one might identify potential plant-based therapies (“phyto-therapies”). Concept-based analytic approaches have been shown to uncover knowledge embedded within biomedical literature. However, to date there has been limited attention towards leveraging such techniques for the identification of potential phyto-therapies. This study presents concept-based analytic approaches for the retrieval and ranking of associations between plants and human diseases. Focusing on identification of phyto-therapies described in MEDLINE, both MeSH descriptors used for indexing and MetaMap inferred UMLS concepts are considered. Furthermore, the identification and ranking consider both direct (i.e., plant concepts directly correlated with disease concepts) and inferred (i.e., plant concepts associated with disease concepts based on shared signs and symptoms) relationships. Based on the two scoring methodologies used in this study, it was found that a Vector Space Model approach outperformed probabilistic reliability based inferences. An evaluation of the approach is provided based on therapeutic interventions catalogued in both ClinicalTrials.gov and NDF-RT. The promising findings from this feasibility study highlight the challenges and applicability of concept-based analytic strategies for distilling phyto-therapeutic knowledge from text based knowledge sources like MEDLINE.

Using semi-structured data for assessing research paper similarity

The task of assessing the similarity of research papers is of interest in a variety of application contexts. It is a challenging task, however, as the full text of the papers is often not available, and similarity needs to be determined based on the papers’ abstract, and some additional features such as their authors, keywords, and the journals in which they were published. Our work explores several methods to exploit this information, first by using methods based on the vector space model and then by adapting language modeling techniques to this end. In the first case, in addition to a number of standard approaches we experiment with the use of a form of explicit semantic analysis. In the second case, the basic strategy we pursue is to augment the information contained in the abstract by interpolating the corresponding language model with language models for the authors, keywords and journal of the paper. This strategy is then extended by revealing the latent topic structure of the collection using an adaptation of Latent Dirichlet Allocation, in which the keywords that were provided by the authors are used to guide the process. Experimental analysis shows that a well-considered use of these techniques significantly improves the results of the standard vector space model approach.

A vector space model approach to identify genetically related diseases

The relationship between diseases and their causative genes can be complex, especially in the case of polygenic diseases. Further exacerbating the challenges in their study is that many genes may be causally related to multiple diseases. This study explored the relationship between diseases through the adaptation of an approach pioneered in the context of information retrieval: vector space models. A vector space model approach was developed that bridges gene disease knowledge inferred across three knowledge bases: Online Mendelian Inheritance in Man, GenBank, and Medline. The approach was then used to identify potentially related diseases for two target diseases: Alzheimer disease and Prader-Willi Syndrome. In the case of both Alzheimer Disease and Prader-Willi Syndrome, a set of plausible diseases were identified that may warrant further exploration. This study furthers seminal work by Swanson, et al. that demonstrated the potential for mining literature for putative correlations. Using a vector space modeling approach, information from both biomedical literature and genomic resources (like GenBank) can be combined towards identification of putative correlations of interest. To this end, the relevance of the predicted diseases of interest in this study using the vector space modeling approach were validated based on supporting literature. The results of this study suggest that a vector space model approach may be a useful means to identify potential relationships between complex diseases, and thereby enable the coordination of gene-based findings across multiple complex diseases.

A conceptual representation of documents and queries for information retrieval systems by using light ontologies

This article presents a vector space model approach to representing documents and queries, based on concepts instead of terms and using WordNet as a light ontology. Such representation reduces information overlap with respect to classic semantic expansion techniques. Experiments carried out on the MuchMore benchmark and on the TREC-7 and TREC-8 Ad-Hoc collections demonstrate the effectiveness of the proposed approach.

A Mixed Method Approach for Efficient Component Retrieval from a Component Repository

A continuing challenge for software designers is to develop efficient and cost-effective software implementations. Many see software reuse as a potential solution; however, the cost of reuse tends to outweigh the potential benefits. The costs of software reuse include establishing and maintaining a library of reusable components, searching for applicable components to be reused in a design, as well as adapting components toward a proper implementation. In this context, a new method is suggested here for component classification and retrieval which consists of K-nearest Neighbor (KNN) algorithm and Vector space Model Approach. We found that this new approach gives a higher accuracy and precision in component selection and retrieval process compared to the existing formal approaches.

Exploring the feasibility and accuracy of Latent Semantic Analysis based text mining techniques to detect similarity between patent documents and scientific publications

In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detected—and hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents—in this case, patents and publications—might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n = 6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that text mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed.

Exploring the Feasibility and Accuracy of Latent Semantic Analysis Based Text Mining Techniques to Detect Similarity between Patent Documents and Scientific Publications

In this study, we examine and validate the use of existing text mining techniques (based on the vector space model and latent semantic indexing) to detect similarities between patent documents and scientific publications. Clearly, experts involved in domain studies would benefit from techniques that allow similarity to be detected - and hence facilitate mapping, categorization and classification efforts. In addition, given current debates on the relevance and appropriateness of academic patenting, the ability to assess content-relatedness between sets of documents - in this case, patents and publications - might become relevant and useful. We list several options available to arrive at content based similarity measures. Different options of a vector space model and latent semantic indexing approach have been selected and applied to the publications and patents of a sample of academic inventors (n=6). We also validated the outcomes by using independently obtained validation scores of human raters. While we conclude that mixt mining techniques can be valuable for detecting similarities between patents and publications, our findings also indicate that the various options available to arrive at similarity measures vary considerably in terms of accuracy: some generally accepted text mining options, like dimensionality reduction and LSA, do not yield the best results when working with smaller document sets. Implications and directions for further research are discussed.

Similarity of Semantic Relations

There are at least two kinds of similarity. Relational similarity is correspondence between relations, in contrast with attributional similarity, which is correspondence between attributes. When two words have a high degree of attributional similarity, we call them synonyms. When two pairs of words have a high degree of relational similarity, we say that their relations are analogous. For example, the word pair mason:stone is analogous to the pair carpenter:wood. This article introduces Latent Relational Analysis (LRA), a method for measuring relational similarity. LRA has potential applications in many areas, including information extraction, word sense disambiguation, and information retrieval. Recently the Vector Space Model (VSM) of information retrieval has been adapted to measuring relational similarity, achieving a score of 47% on a collection of 374 college-level multiple-choice word analogy questions. In the VSM approach, the relation between a pair of words is characterized by a vector of frequencies of predefined patterns in a large corpus. LRA extends the VSM approach in three ways: (1) The patterns are derived automatically from the corpus, (2) the Singular Value Decomposition (SVD) is used to smooth the frequency data, and (3) automatically generated synonyms are used to explore variations of the word pairs. LRA achieves 56% on the 374 analogy questions, statistically equivalent to the average human score of 57%. On the related problem of classifying semantic relations, LRA achieves similar gains over the VSM.

A feature mining based approach for the classification of text documents into disjoint classes

This paper proposes a new approach for classifying text documents into two disjoint classes. The new approach is based on extracting patterns, in the form of two logical expressions, which are defined on various features (indexing terms) of the documents. The pattern extraction is aimed at providing descriptions (in the form of two logical expressions) of the two classes of positive and negative examples. This is achieved by means of a data mining approach, called One Clause At a Time (OCAT), which is based on mathematical logic. The application of a logic-based approach to text document classification is critical when one wishes to be able to justify why a particular document has been assigned to one class versus the other class. This situation occurs, for instance, in declassifying documents that have been previously considered important to national security and thus are currently being kept as secret. Some computational experiments have investigated the effectiveness of the OCAT-based approach and compared it to the well-known vector space model (VSM). These tests also have investigated finding the best indexing terms that could be used in making these classification decisions. The results of these computational experiments on a sample of 2897 text documents from the TIPSTER collection indicate that the first approach has many advantages over the VSM approach for solving this type of text document classification problem. Moreover, a guided strategy for the OCAT-based approach is presented for deciding which document one needs to consider next while building the training example sets.

A vector space model approach to production planning for a multi-product, multi-cell and multi-stage manufacturing system

Abstract This paper develops a formal mathematical approach to aggregate production planning for a multi-product, multi-cell and multi-stage manufacturing system. The model, based upon a vector space approach, includes all the important variables relating to the demand for individual items, inventory levels, the availability of machines taking into account any breakdowns, subcontracting of orders and overtime working. The computational procedure for determining the production planning strategies, in terms of overtime/undertime working and increase/decrease in the number of orders subcontracted, are presented. Three numerical examples are presented showing the use of the model developed. This approach makes it possible to develop realistic models of practical manufacturing systems. It is particularly applicable to flexible manufacturing systems.