Semantic Vector Space Model: Implementation and evaluation

An improved focused crawler based on Semantic Similarity Vector Space Model

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.

In view of the lack of semantic information description in commonly-used knowledge representation modes such as framework and object-oriented mode, the domain ontology of electronic protective equipment fault diagnosis is designed by analyzing and summarizing the characteristics of the equipment fault cases. Considering that the traditional VSM (vector space model) ignores the role of the position of document entries and structural elements in describing the semantic correlation between features, K-NN (K-nearest neighbor) similarity matching algorithm based on SVSM (semantic vector space model) is proposed by making full use of the advantages of ontology in semantic knowledge representation. For electronic protective equipment fault diagnosis algorithm based on CBR (case based reasoning), a detailed description is given and its feasibility and advantage in performance is proved through experiment.

Large-scale documents reduction plays a critical role in document management organizing and document mining, etc, and the research is concentrated on two special aspects: the construction of document representation model and index optimization of feature space for similarity search. While the semantic gap and curse of dimensionality are still two open and tough issues. Motivated by this, in the paper, we propose a novel method based on domain ontology and E2LSH (Exact Euclidean Locality-Sensitive Hashing). Firstly, we build an improved model based on domain ontology, called Semantic Vector Space Model (SVSM), to reveal the latent semantic relationships among document feature terms besides syntax information. The SVSM shortens the semantic gap of traditional VSM and reduces feature dimension. Then in view of the complexity of searching space for the similarity computation among documents pairs, we introduce E2LSH to build indexes of feature space, optimizing the searching space and overcoming the curse of dimensionality. Experimental validation has been conducted using realistic documents, and experimental results indicate the rationality and effectiveness of our method.

Fuzzy control GA with a novel hybrid semantic similarity strategy for text clustering

Vector Space Models (VSMs) of Semantics are useful tools for exploring the semantics of single words, and the composition of words to make phrasal meaning. While many methods can estimate the meaning (i.e. vector) of a phrase, few do so in an interpretable way. We introduce a new method (CNNSE) that allows word and phrase vectors to adapt to the notion of composition. Our method learns a VSM that is both tailored to support a chosen semantic composition operation, and whose resulting features have an intuitive interpretation. Interpretability allows for the exploration of phrasal semantics, which we leverage to analyze performance on a behavioral task.

With the increasing development of web service technologies, more and more applications need web services to give strong capabilities. We divide the existing information space into two levels - semantic space and information space. It will make the tranditional operations on web services been abstracted from information space level into semantic space level. Through semantic computing and semantic reasoning, it is more effective to implement the service discovering, searching , and composing etc. We give a semantic resource description framework RDF4S and its description document WSDL4S for semantic web service. This paper presents an approach to establish semantic information space model based on WSDL4S document using data mining technologies and ontology technologies. The method constructs a mapping between general information space and semantic information space. A specific case is given to prove the validity of this method at the end of the paper.

Currently, the data that dealt by traditional text clustering methods is small. Text representation model that text clustering used is traditional vector space model (VSM). The traditional text clustering has defect of low efficiency when processing big data. The quality is bad when using traditional VSM model for text representation. To solve these two problems, this paper puts forward a VSM model for text representation, which is improved by Term Frequency(TF), and using Mahout to text clustering. Experimental results show that use Mahout for text clustering has a higher efficiency than traditional text clustering; this novel VSM model can improve the text clustering quality.

The past few years have witnessed significant increase in DDoS attacks on Internet, prompting network security as a great concern. With the attacks getting more sophisticated, automatically reasoning the attack scenarios in real time and categorizing those scenarios become a critical challenge. However,the overwhelming flow of events generated by Intrusion Detection System (IDS) sensors make it hard for security administrators to uncover hidden attack plans. This paper presents a semantic vector space model to extract and categorize attack scenarios based on First-order Logics (FOL) and linguistics. The modified Case Grammar is introduced to formalize the heterogeneous IDS alerts into uniform structured alert streams. The attack resolution is then used to generate attack semantic network. Afterwards, mutual information is used to determine the alert semantic context range. Based on the attack ontology and alert contexts, attack scenarios are extracted and the alerts are represented as attack semantic space vectors. Finally text categorization technique are used to categorize the intrusion stages. The preliminary results show our model has better performance than the traditional alert correlations.

As a semantic vector space model for information retrieval (IR), Latent Semantic Indexing (LSI) employs singular value decomposition (SVD) to transform individual documents into the statistically derived semantic vectors. In this paper a new junk email (spam) filtering model, 2LSI-SF, is proposed and it is based on the augmented category LSI spaces and classifies email messages by their content. The model utilizes the valuable discriminative information in the training data and incorporates several pertinent feature selection and message classification algorithms. The experiments of 2LSI-SF on a benchmark spam testing corpus (PU1) and a newly compiled Chinese spam corpus (ZH1) have been conducted. The results from the experiments and performance comparison with the popular Support Vector Machines (SVM) and naive Bayes classifiers have shown that 2LSI-SF is capable of filtering spam effectively.

Product process design relies upon the process knowledge support acquired by process designers and enterprises. Process knowledge recommendation has been attached more importance by enterprises in that it can quickly provide process designers with accurate and appropriate process knowledge. In this paper, a “scene-label-classification” knowledge recommendation scheme for process knowledge graph is established, and a knowledge content matching degree calculation method supporting process knowledge recommendation is proposed. Design requirements are described by parameterized characteristics from various dimensions such as material, precision requirements. Based on knowledge coding, the attribute characteristics of process knowledge are uniformly identified and associated with requirement scene characteristics. A requirements-knowledge semantic vector space model is established, and a calculation method of matching degree between design requirements and knowledge content is proposed based on improved cosine distance. This scheme enables quickly positioning the corresponding process knowledge based on coding labels, and final process knowledge recommendation candidate set is obtained through matching degree calculation and filtering sort, which thus enables dynamic classification and appropriate recommendation of process knowledge.

The vector space model of information retrieval is one of the classical and widely applied retrieval models. Paradoxically, it has been characterized by a discrepancy between its formal framework and implementable form. The underlying concepts of the vector space model are mathematical terms: linear space, vector, and inner product. However, in the vector space model, the mathematical meaning of these concepts is not preserved. They are used as mere computational constructs or metaphors. Thus, the vector space model actually does not follow formally from the mathematical concepts on which it has been claimed to rest. This problem has been recognized for more than two decades, but no proper solution has emerged so far. The present article proposes a solution to this problem. First, the concept of retrieval is defined based on the mathematical measure theory. Then, retrieval is particularized using fuzzy set theory. As a result, the retrieval function is conceived as the cardinality of the intersection of two fuzzy sets. This view makes it possible to build a connection to linear spaces. It is shown that the classical and the generalized vector space models, as well as the latent semantic indexing model, gain a correct formal background with which they are consistent. At the same time it becomes clear that the inner product is not a necessary ingredient of the vector space model, and hence of Information Retrieval (IR). The Principle of Object Invariance is introduced to handle this situation. Moreover, this view makes it possible to consistently formulate new retrieval methods: in linear space with general basis, entropy‐based, and probability‐based. It is also shown that Information Retrieval may be viewed as integral calculus, and thus it gains a very compact and elegant mathematical way of writing. Also, Information Retrieval may thus be conceived as an application of mathematical measure theory.

Over the years, various document‐clustering techniques were developed to group the textual data. The performance of document clustering systems heavily relies on the optimal use of text representations. Vector space model is an extensively used technique by existing clustering algorithms to present the text in a structured form. However, such representations suffer from a lack of semantic associations, high dimensionality, and sparsity. In order to enrich the document representation by retaining the semantic and morphological associations, this paper introduced a word cluster‐based modified term frequency‐inverse document frequency (WC_MTI) model, in which semantically associated word embeddings from the word2vec are supplemented with morphological information using kernel principal component analysis. In addition, to address high dimensionality and sparsity issues and improve the clustering, we use a self‐training technique that learns discriminative features using the WC_MTI model and autoencoder (AE) and then updates the encoder network weights using assignments from a clustering algorithm as supervision. The proposed model organizes documents into topically compatible clusters by maintaining the semantic and morphological similarity between terms using the skip‐gram with negative sampling (SGNS) and low‐dimensional vector representations. We evaluate the proposed approach against the existing text representation methods. Experimental findings suggest that the proposed approach enhanced the average accuracy by 89.62%.

In knowledge management systems (KMS), managing explicit knowledge is comparatively easy using information technology such as databases. However, tacit knowledge, usually possessed by human experts in unstructured forms such as know-how and experiences, is hard to systemize. Recent research has shown that it is more effective to provide search mechanisms for experts than to directly search for specific knowledge itself in KMS to pinpoint experts with needed knowledge in the organizations so that users can acquire the knowledge from the found experts. In this article, we propose an intelligent search framework to provide search capabilities for experts who not only match search conditions exactly but also belong to the similar or related subject fields according to the user’s needs. In enabling intelligent searches for experts, the Fuzzy Abstraction Hierarchy (FAH) framework has been adopted. Based on FAH, searching for experts with similar or related expertise is facilitated using the subject field hierarchy defined in the system. While adopting FAH, a text categorization approach based on Vector Space Model is also utilized to overcome the limitation of the original FAH framework. To test applicability and practicality of the proposed framework, the prototype system, “Knowledge Portal for Researchers in Science and Technology” sponsored by the Ministry of Science and Technology (MOST) of Korea, has been developed.

This study presents an overview of the research aimed at optimizing library information retrieval through the utilization of the Vector Space Model (VSM) method in a computer science context. Libraries, as publicly financed collections, provide extensive knowledge resources, eliminating the need for individual book purchases. However, the challenge lies in efficiently navigating the expanding library collections. To tackle this issue, the study employs information retrieval techniques, particularly the VSM method, which assesses term similarity by assigning weights to terms, enabling document and query representation as vectors. The relevance between documents and queries is measured through vector similarity. This approach, integrated with indexing, streamlines collection retrieval in libraries. Employing the Waterfall model for system development, the research outlines phases like analysis, design, coding, testing, and implementation. While effective, the model's rigidity in accommodating evolving requirements poses limitations. The VSM method's numerical representation of text documents facilitates precise similarity calculations, supported by TF-IDF values indicating term importance in documents relative to the corpus. The study further extends to system design using UML diagrams and a visitor interface, integrating VSM for efficient search functionality. Black-box testing confirms the robustness of the system components and interfaces. Overall, this research presents a systematic approach to enhance information retrieval in libraries, emphasizing the VSM's pivotal role in optimizing document searches within expansive collections.