A Novel Approach for Domain Specific Lucky Web Search

2010 ◽  
Vol 5 (5) ◽  
pp. 72-80 ◽  
Author(s):  
Debajyoti Mukhopadhyay ◽  
Sukanta Sinha
Keyword(s):  
Web Search ◽  
Domain Specific ◽  
Novel Approach

scholarly journals A Novel Approach of Web Search Based on Community Wisdom

2008 ◽  
Author(s):  
Weiliang Zhao ◽  
Vijay Varadharajan
Keyword(s):  
Web Search ◽  
Novel Approach

scholarly journals DISSECT: DISentangle SharablE ConTent for Multimodal Integration and Crosswise-mapping

2020 ◽  
Author(s):  
Geoffrey Schau ◽  
Erik Burlingame ◽  
Young Hwan Chang
Keyword(s):  
Deep Learning ◽  
Complete Information ◽  
Specific Information ◽  
Multimodal Integration ◽  
Specific Data ◽  
Domain Specific ◽  
Cross Domain ◽  
Input Feature ◽  
Novel Approach ◽  
Latent Representations

AbstractDeep learning systems have emerged as powerful mechanisms for learning domain translation models. However, in many cases, complete information in one domain is assumed to be necessary for sufficient cross-domain prediction. In this work, we motivate a formal justification for domain-specific information separation in a simple linear case and illustrate that a self-supervised approach enables domain translation between data domains while filtering out domain-specific data features. We introduce a novel approach to identify domainspecific information from sets of unpaired measurements in complementary data domains by considering a deep learning cross-domain autoencoder architecture designed to learn shared latent representations of data while enabling domain translation. We introduce an orthogonal gate block designed to enforce orthogonality of input feature sets by explicitly removing non-sharable information specific to each domain and illustrate separability of domain-specific information on a toy dataset.

scholarly journals Application Layer Packet Processing Using PISA Switches

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8010
Author(s):  
Ismail Butun ◽  
Yusuf Tuncel ◽  
Kasim Oztoprak
Keyword(s):  
Streaming Data ◽  
The Novel ◽  
Application Layer ◽  
Domain Specific ◽  
Switch Architecture ◽  
Novel Approach ◽  
Data Processor ◽  
Application Identification ◽  
The Cost ◽  
Telecommunication Operators

This paper investigates and proposes a solution for Protocol Independent Switch Architecture (PISA) to process application layer data, enabling the inspection of application content. PISA is a novel approach in networking where the switch does not run any embedded binary code but rather an interpreted code written in a domain-specific language. The main motivation behind this approach is that telecommunication operators do not want to be locked in by a vendor for any type of networking equipment, develop their own networking code in a hardware environment that is not governed by a single equipment manufacturer. This approach also eases the modeling of equipment in a simulation environment as all of the components of a hardware switch run the same compatible code in a software modeled switch. The novel techniques in this paper exploit the main functions of a programmable switch and combine the streaming data processor to create the desired effect from a telecommunication operator perspective to lower the costs and govern the network in a comprehensive manner. The results indicate that the proposed solution using PISA switches enables application visibility in an outstanding performance. This ability helps the operators to remove a fundamental gap between flexibility and scalability by making the best use of limited compute resources in application identification and the response to them. The experimental study indicates that, without any optimization, the proposed solution increases the performance of application identification systems 5.5 to 47.0 times. This study promises that DPI, NGFW (Next-Generation Firewall), and such application layer systems which have quite high costs per unit traffic volume and could not scale to a Tbps level, can be combined with PISA to overcome the cost and scalability issues.

Mining Domain Terminologies Using Search Engine's Query Log

2021 ◽  
Vol 20 (6) ◽  
pp. 1-32
Author(s):  
Weijian Ni ◽  
Tong Liu ◽  
Qingtian Zeng ◽  
Nengfu Xie
Keyword(s):  
Language Processing ◽  
Heterogeneous Network ◽  
Transductive Learning ◽  
Query Log ◽  
Domain Specific ◽  
Novel Approach ◽  
Commercial Search Engine ◽  
Domain Expertise ◽  
Traditional Approaches ◽  
Domain Independent

Domain terminologies are a basic resource for various natural language processing tasks. To automatically discover terminologies for a domain of interest, most traditional approaches mostly rely on a domain-specific corpus given in advance; thus, the performance of traditional approaches can only be guaranteed when collecting a high-quality domain-specific corpus, which requires extensive human involvement and domain expertise. In this article, we propose a novel approach that is capable of automatically mining domain terminologies using search engine's query log—a type of domain-independent corpus of higher availability, coverage, and timeliness than a manually collected domain-specific corpus. In particular, we represent query log as a heterogeneous network and formulate the task of mining domain terminology as transductive learning on the heterogeneous network. In the proposed approach, the manifold structure of domain-specificity inherent in query log is captured by using a novel network embedding algorithm and further exploited to reduce the need for the manual annotation efforts for domain terminology classification. We select Agriculture and Healthcare as the target domains and experiment using a real query log from a commercial search engine. Experimental results show that the proposed approach outperforms several state-of-the-art approaches.

INTENT RECOGNITION IN MULTI-AGENT SYSTEMS: COW HERDING

2014 ◽  
Vol 28 (05) ◽  
pp. 1459006
Author(s):  
NAJLA AHMAD ◽  
ARVIN AGAH
Keyword(s):  
Conceptual Framework ◽  
Recognition System ◽  
Plan Recognition ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Intent Recognition ◽  
Domain Specific ◽  
Novel Approach ◽  
Multi Agent

In a multi-agent system, an agent may utilize its idle time to assist other agents in the system. Intent recognition is proposed to accomplish this with minimal communication. An agent performing recognition observes the tasks other agents are performing and, unlike the much studied field of plan recognition, the overall intent of an agent is recognized instead of a specific plan. The observing agent may use capabilities that it has not observed. A conceptual framework is proposed for intent recognition systems. An implementation of the conceptual framework is tested and evaluated. We hypothesize that using intent recognition in a multi-agent system increases utility (where utility is domain specific) and decreases the amount of communication. We test our hypotheses using the domain of Cow Herding, where agents attempt to herd cow agents into team corrals. A set of metrics, including task time and number of communications, is used to compare the performance of plan recognition and intent recognition. In our results, we find that intent recognition agents communicate fewer times than plan recognition agents. In addition, unlike plan recognition, when agents use the novel approach of intent recognition, they select unobserved actions to perform. Intent recognition agents were also able to outperform plan recognition agents by consistently scoring more points in the Cow Herding domain. This research shows that under certain conditions, an intent recognition system is more efficient than a plan recognition system. The advantage of intent recognition over plan recognition becomes more apparent in complex domains.

Research on Web Search Engine Optimization and its Application

2014 ◽  
Vol 687-691 ◽  
pp. 1908-1911
Author(s):  
Wei Zhong Huang
Keyword(s):  
Search Engine ◽  
Web Search ◽  
Internet Survey ◽  
The Internet ◽  
Network Information ◽  
Domain Specific ◽  
Wide Range ◽  
Information Retrieval Systems ◽  
Almost All ◽  
Mail Service

The universal search engine, which is widely used now, has significantly improved the efficiency of retrieving information. According to CNNIC (China Internet Network Information Center) 26th Internet survey, the search takes up 76.30% for absolute advantage as a major way for users to obtain information from the Internet. Among almost all the surveys of using on the Internet in the world, search engine is second only to e-mail service. But with the growth of a wide range of information, these universal search engines can not meet people's needs either in retrieval precision or in retrieval efficiency when retrieving information on a subject or topic. That's because as long as the user enters the same keywords, the feedbacks of universal search engine are just the same. Universal search engine does not take the differences in interests and needs between different users, which often exist, into account. For example, dentists and ceramics enthusiasts would hold different concerns about the term "ceramic". In order to be more rapid, accurate and efficient in retrieving information on particular subject or theme, it is essential to develop information retrieval systems on specific areas, that is, the domain-specific search engine.

Learning Non-Taxonomic Relations on Demand for Ontology Extension

2014 ◽  
Vol 24 (08) ◽  
pp. 1159-1175 ◽  
Author(s):  
Yan Xu ◽  
Ge Li ◽  
Lili Mou ◽  
Yangyang Lu
Keyword(s):  
Semantic Relatedness ◽  
Semantic Relations ◽  
Learning Approaches ◽  
Important Research ◽  
Domain Specific ◽  
On Demand ◽  
Novel Approach ◽  
Important Research Topic ◽  
Good Potential ◽  
Taxonomic Relations

Learning non-taxonomic relations becomes an important research topic in ontology extension. Most of the existing learning approaches are mainly based on expert crafted corpora. These approaches are normally domain-specific and the corpora acquisition is laborious and costly. On the other hand, based on the static corpora, it is not able to meet personalized needs of semantic relations discovery for various taxonomies. In this paper, we propose a novel approach for learning non-taxonomic relations on demand. For any supplied taxonomy, it can focus on the segment of the taxonomy and collect information dynamically about the taxonomic concepts by using Wikipedia as a learning source. Based on the newly generated corpus, non-taxonomic relations are acquired through three steps: a) semantic relatedness detection; b) relations extraction between concepts; and c) relations generalization within a hierarchy. The proposed approach is evaluated on three different predefined taxonomies and the experimental results show that it is effective in capturing non-taxonomic relations as needed and has good potential for the ontology extension on demand.

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