جستجوی مقالات مرتبط با کلیدواژه « ontology » در نشریات گروه « برق »

Recommender systems are systems that, over time, learn what product(s) or item(s) each person or customer is (are) likely to like and recommend it (them) to him/her. These systems often operate based on similar behaviors from other (possibly similar) people. Finding similar people is generally a highly time-consuming process due to the large number of users and inaccurate due to the lack of information. For this reason, some methods have resorted to increasing speed. On the other hand, some other methods have added additional information so that they can increase the accuracy of finding similar or neighboring users. Some others have resorted to hybrid methods. Recently, by the use of basic clustering methods, which is based on finding the most similar neighbors with the help of users’ clustering, as well as by using basic content analysis methods and sometimes adding ontology to these methods, researchers have been able to take the advantage of these methods in order to solve some of the above challenges acceptably. In the proposed hybrid recommender system, we have used a two-stage system in which, in the first stage, two models of predictions are made, then in the second stage, by a combining component, the results of the first two parts are combined and the obtained results are given to us as the final results of the system. In the first part, a system based on imputation of missing values fills in the blanks in the scoring matrix. For this end, among the methods of the missing data imputation, we designed a method that was compatible with filling the data set in very sparse conditions, and then generalized it to our own method. In this regard, we have proposed a method based on the grey distance clustering. In the second part, which itself is a hybrid ontology-based recommender system, we first extract the information of each item with the help of a web crawler, then based on a basic article, we produce our own limited ontology, and after that we apply our proposed method. Then, with the help of a proposed method, we improve the ontology structure, thus increasing the accuracy of measuring semantic similarity between the items and users in later stages, and significantly improving the effectiveness of the created recommendations. It should be noted that this ontology is not comprehensive. Finally, we measure the similarity of item-items, user-users, and user-items using an innovative basic ontology similarity measurement method. By the use of this similarity matrix, we cluster users and items, and then store similar users and items as a new feature in the user/item profile for each user/item. This will help us speed up the process of looking for similar users and similar items in the future. In fact, based on this feature, we have increased the speed of the whole work. Since we have set our goal to build a system that makes a balance between the two criteria of accuracy and speed, we use these two criteria to evaluate the proposed system using a real data set. The results of comparing our proposed method with some up-to-date similar methods presented in this field (using the same data set) implies that our method is slower than fast methods, although it is more accurate than them. These results also suggest that the proposed method is faster than accurate methods and its quality is more competitive or even better than them.

In recent years, ontologies, as one of the most important components of the semantic web, have expanded in various fields. The problem of ontology matching has been raised with the aim of creating a set of mappings between entities of ontologies. This problem is classified as an NP-hard problem. Therefore, greedy methods have been proposed to solve it in different ways. Selecting the appropriate lexical, structural and semantic similarity criteria and using an effective combination method to obtain the final mapping is one of the most important challenges of these methods. In this paper, an automatic method of matching ontologies is proposed to provide a one-to-one mapping set. This method detects primary mappings based on a new lexical similarity criterion, which is accordance with the descriptive essence of entities and combining this similarity with semantic similarity obtained from external semantic sources. By locally propagating the score of initial mappings in the class hierarchy graph, structurally matching entities are identified. In this method, property matching is examined in a separate step. In the final step, the mapping filter is applied in order to maintain the consistency of the final mapping set. In the evaluation section, comparing the performance of the lexical similarity measure compared to other proposed textual similarity measures, indicates the efficiency of this measure in the problem of ontology matching. In addition, the results of the proposed matching system compared to the results of the set of participating systems in the OAEI competitions shows this system in the second place and higher than many complex matching systems.

Extracting structured information about entities from web texts is an important task in web mining, natural language processing, and information extraction. Information extraction is useful in many applications including search engines, question-answering systems, recommender systems, machine translation, and etc. An information extraction system aims to identify the entities from the text and extract their related information to form a profile of the target entity. In recent years, several methods have been proposed for extracting structured information from web text. The majority of existing methods for extracting entity-centric information require a predefined ontology. The ontology includes the complete knowledge of the entities and their attributes. The main challenge of these methods is their inability to extract information about entities that are not already defined in the ontology. Besides, the existing methods have ignored semantic information extraction and have not linked the extracted information to the general ontology entries. This highlights that introducing new methods for semantic information extraction is an open problem and there is room for more efforts in this field. As an element of research, we proposed a new method for the automatic extraction of semantically structured information from Farsi web text. The proposed method does not require background knowledge about the entities and their properties. The proposed method consists of three main phases including pre-processing, semantic analysis and frame extraction. To fulfill these phases, we use a combination of language resources, text processing tools, and distant ontologies. The main focuses of the proposed method are to enrich the predicate-argument frames with the semantic information extracted from distant ontologies, extract the entity-related information from predicate-argument frames, and link the extracted information with their corresponding sense in DBPedia ontology. The issue facilitates the processing of Farsi texts by computers. To evaluate the proposed method, we created a small Farsi dataset containing 100 complete sentences. Then, the proposed method is compared with three information extraction methods on this dataset. The results of experiments show the superiority of the proposed method compared to counterpart methods in terms of precision and F1 measures.

Recommender systems that predict user ratings for a set of items are known as subset of information filtration systems. They help users find their favorite items from thousands of available items. One of the most important and challenging problems that recommendation systems suffer from is the problem of dispersion. This means that due to the scatter of data in the system, they are not able to find popular items with the desired reliability and accuracy. This is especially true when there are a large number of items and users in the system and the filled ratings are low. Another challenging problem that these systems suffer from is their scalability. One of the major problems with these systems is the cold start. This problem occurs due to the small number of items rated by the user, i.e. the scatter of users. This problem is divided into two categories: new user and new item. The main focus of this article is on the problem of the new user type. This problem occurs when a new user has just logged in and has not rated any item yet, or when the user has already logged in but has been less active in rating. The goal is to address these three challenges. In this study, an ontology-based hybrid recommender system is introduced in which ontology is used in the content-based filtering section, while the ontology structure is improved by the collaborative filtering section. In this paper, a new hybrid approach based on combining demographic similarity and cosine similarity between users is presented in order to solve the cold start problem of the new user type. Also, a new approach based on combining ontological similarity and cosine similarity between items is proposed to solve the cold start problem of the new item type. The main idea of the proposed method is to extend users’/items’ profiles based on different mechanisms to create higher-performance profiles for users/items. The proposed method is evaluated in a real data set, and experiments show that the proposed method performs better than the advanced recommender system methods, especially in the case of cold start.

The recommender systems are models that are to predict the potential interests of users among a number of items. These systems are widespread and they have many applications in real-world. These systems are generally based on one of two structural types: collaborative filtering and content filtering. There are some systems which are based on both of them. These systems are named hybrid recommender systems. Recently, many researchers have proved that using content models along with these systems can improve the efficacy of hybrid recommender systems. In this paper, we propose to use a new hybrid recommender system where we use a WordNet to improve its performance. This WordNet is also automatically generated and improved during its generation. Our ontology creates a knowledge base of concepts and their relations. This WordNet is used in the content collaborator section in our hybrid recommender system. We improve our ontological structure via a content filtering technique. Our method also benefits from a clustering task in its collaborative section. Indeed, we use a passive clustering task to improve the time complexity of our hybrid recommender system. Although this is a hybrid method, it consists of two separate sections. These two sections work together during learning. Our hybrid recommender system incorporates a basic memory-based approach and a basic model-based approach in such a way that it is as accurate as a memory-based approach and as scalable as a model-based approach. Our hybrid recommender system is assessed by a well-known data set. The empirical results indicate that our hybrid recommender system is superior to the state of the art methods. Also, our hybrid recommender system is more accurate and scalable compared to the recommender systems, which are simply memory-based (KNN) or basic model-based. The empirical results also confirm that our hybrid recommender system is superior to the state of the art methods in terms of the consumed time. While this method is more accurate than model-based methods, it is also faster than memory-based methods. However, this method is not much weaker in terms of accuracy than memory-based methods, and not much weaker in terms of speed than model-based methods.

The diverse applications and widespread effects of cyberspace in most military and civilian applications have led to the rapid growth of data, information, knowledge, technology, methods, tools, and cyber systems. One of the important and strategic requirements in the cyber command and control cycle is the capability to extract, process, fusion and analyze data and information from various sources to achieve the desired situational awareness of the cyberspace and cyber operations environment. Considering the various aspects of this issue, the use of soft data, ie data and information from human resources, along with hard data, ie data and information of machine resources, can help in achieving more accurate and reliable recognition and decision. One of the main research topics in this area is designing a conceptual model and appropriate processing framework for analysis and inference based on data and information with the ability of linking various information from different sources with each other and modeling different types of uncertainty in hard and soft data and fusing them.This paper presents an ontology-based approach to the processing and fusion of hard and soft data in cyber command and control, in which various aspects of the issue has been addressed including ontology-based architecture and processing framework of information inference and fusion, the method of representation of uncertainty and reliability in hard and soft data and fusing these data, conversion of beliefs into probabilities to be able to make decisions on the hypotheses under consideration, designing an appropriate ontology model for cyber command and control purposes, and design and implementation of ontology-based logic of inference and fusion. The results of applying the proposed model in a typical scenario of cyber command and control show that it is operational in fusion of hard and soft cyber data. In addition to the ability of inference and fusion, one of the notable features of the proposed approach is its scalability and adaptability to new extensions in the tools and requirements of cyber command and control.

In Natural Language Processing field, lexical resources are the mandatory tools which their representation format and accessibility has essential importance. In this paper, we focus on representation format of WordNet as an important lexical resource; aiming to benefit from the semantic web and Linked Data advantages. The Linked Data format provides knowledge representation and integration with other knowledge resources. Providing WordNet in linked data format and ontology representation, enable us to use semantic web technologies, such as inference. Development and usage of WordNet in the linked data format are more effective. The paper introduces an ontology schema for Farsi WordNet. The schema is based and Linked Data and is evaluable by Lemon model.

Data fusion techniques combine raw data of multiple sources and gather related information to achieve more specific inferences than could be achieved by using a single source. In the fusion research community, JDL is a common model, but it suffers from semantics issues. Using Semantic Web (SW) technologies for the extraction of implicit knowledge is a new approach to overcome this problem. SW technologies purpose to describe and represent the semantics related with data or information in order to facilitate automatic processing, integration, sharing and reuse of the data. So in this paper, a semantic fusion model is proposed based on JDL, which can overcome the semantic problems in heterogeneous systems by using SW technologies including ontology and semantic reasoning. Finally, military scenario is designed and implemented to display the functionality of the proposed model practically. In implementation, Jena framework was utilized for semantic programming to describe ontology and have a semantic reasoner.

Data warehouse does not provide external data that are required to dynamically build after design and create the data warehouse. Therefore, analysts conduct effective analysis to find a correlation between external data and data warehouse data, and in other cases requires a comparison both external data and data warehouse data together. The analyst forced to repeat some past repetitive situations. This includes creating terminology, measures and comparison. In this paper, for graduates of this problem, a real-time data warehouse architecture based on ontology is proposed. Furthermore, an algorithm to reduce the response time to users’ queries using materialized views and parallel processing is proposed. A case study to demonstrate how to create correlation between external data and data warehouse data is done and the results show the correlation between external data and data warehouse data is discovered. In experiments, using both direct and parent materialized views approaches in existing data warehouse architecture, reduce response time to users’ sequential, comparative and combination queries.

In recent years, many efforts have been done to design ontology learning methods and automate ontology construction process. The ontology construction process is a time-consuming and costly procedure for almost all domains/applications, so automating this process is a solution to overcome the knowledge acquisition bottleneck in information systems and reduce the construction cost. In this article a novel intelligent ontology learning method is proposed which can be used in many domains and applications. The proposed learning system has no need for initial common or specialized input ontologies or predefined semantic terms; indeed, the initial database anonly consists of input texts sets. The proposed learning system could extract associated ontologies of various domains using combined methods. To do this, a combination of linguistic, statisticaland machine learning methods based on the C-value method, the TF-IDF one, the neural network, and co occurance analysis are applied. So, first domain-related documents were collected. Then natural language processing methods such as C-value method were implemented for extracting meaningful terms from documents. Next, ART (Adaptive Resonance Theory) neural network was used to cluster documents and associated weight of terms was calculated by TF–IDF method in order to find candidate keyword for each cluster. Finally, co-occurrence analysis was used to construct concept hierarchy and complete the ontology. Results show that the proposed ontology learning method has a high precision comparing to similar studies.