Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics最新文献_第9页

Concept-aware geographic information retrieval 概念感知地理信息检索

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106498

Noemi Mauro, L. Ardissono, Adriano Savoca

{"title":"Concept-aware geographic information retrieval","authors":"Noemi Mauro, L. Ardissono, Adriano Savoca","doi":"10.1145/3106426.3106498","DOIUrl":"https://doi.org/10.1145/3106426.3106498","url":null,"abstract":"Textual queries are largely employed in information retrieval to let users specify search goals in a natural way. However, differences in user and system terminologies can challenge the identification of the user's information needs, and thus the generation of relevant results. We argue that the explicit management of ontological knowledge, and of the meaning of concepts (by integrating linguistic and encyclopaedic knowledge in the system ontology), can improve the analysis of search queries, because it enables a flexible identification of the topics the user is searching for, regardless of the adopted vocabulary. This paper proposes an information retrieval support model based on semantic concept identification. Starting from the recognition of the ontology concepts that the search query refers to, this model exploits the qualifiers specified in the query to select information items on the basis of possibly fine-grained features. Moreover, it supports query expansion and reformulation by suggesting the exploration of semantically similar concepts, as well as of concepts related to those referred in the query through thematic relations. A test on a data-set collected using the OnToMap Participatory GIS has shown that this approach provides accurate results.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78438492","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Brand key asset discovery via cluster-wise biased discriminant projection 品牌关键资产发现通过集群明智的偏见判别投影

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106516

Yang Liu, Zhonglei Gu, Tobey H. Ko, Jiming Liu

{"title":"Brand key asset discovery via cluster-wise biased discriminant projection","authors":"Yang Liu, Zhonglei Gu, Tobey H. Ko, Jiming Liu","doi":"10.1145/3106426.3106516","DOIUrl":"https://doi.org/10.1145/3106426.3106516","url":null,"abstract":"Accurate and effective discovery of a brand's key assets, namely, Key Opinion Leaders (KOLs) and potential customers, plays an essential role in marketing campaigns. In a massive online social network, brands are challenged with identifying a small portion of key assets over an enormous volume of irrelevant users, making the problem a highly imbalanced one. Moreover, having to deal with social media data that are usually high-dimensional, the task of brand key asset discovery can be immensely expensive yet inaccurate if the information are not processed efficiently to extract representative features from the original space prior to the learning process. To address the above issues, we propose a novel method dubbed Cluster-wise Biased Discriminant Projection (CBDP) to uncover the compact and informative features from users' data for brand key asset discovery. CBDP conducts a two-layer learning procedure. In the first layer, a Discriminant Clustering (DC) scheme is developed to partition the original dataset into clusters with maximum discriminant capacity. In the second layer, a Biased Discriminant Projection (BDP) algorithm is proposed and performed on each cluster to map the high-dimensional data to the low-dimensional subspace, where the discriminant information of classes with high importance/preference is preserved. A unified mapping function of CBDP is finally established by integrating these two layers. Experiments on both synthetic examples and a real-world brand key asset dataset validate the effectiveness of the proposed method.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83391067","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Improving the classification of events in tweets using semantic enrichment 使用语义充实改进tweets中的事件分类

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106435

Simone Aparecida Pinto Romero, Karin Becker

{"title":"Improving the classification of events in tweets using semantic enrichment","authors":"Simone Aparecida Pinto Romero, Karin Becker","doi":"10.1145/3106426.3106435","DOIUrl":"https://doi.org/10.1145/3106426.3106435","url":null,"abstract":"Contextual enrichment using external sources has been proposed as a means to deal with the poor textual contents of tweets for event classification. Related work performs contextual enrichment according to specific assumptions about the events. Furthermore, enrichment adds a significant amount of extra features, most of them with no discriminative contribution to the event classification task. In this paper, we propose an enrichment framework targeted at the classification of events in general, of which the key elements are: a) external enrichment using related web pages for extending the conceptual features contained within the tweets; b) semantic enrichment using the DBpedia to add related semantic features, and c) a pruning technique that selects the semantic features with discriminative potential. We compared the proposed approach against two distinct baselines based on textual features only and word embeddings, using seven different event datasets. Our experiments reveal that the proposed framework supports the classification of distinct event types, outperforming the textual baseline in 63.5% of the cases, and the word embeddings baseline in 96.5% of the cases.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81887214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 10

An IoT approach for context-aware smart traffic management using ontology 使用本体实现上下文感知智能交通管理的物联网方法

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106499

Deepti Goel, S. Chaudhury, Hiranmay Ghosh

{"title":"An IoT approach for context-aware smart traffic management using ontology","authors":"Deepti Goel, S. Chaudhury, Hiranmay Ghosh","doi":"10.1145/3106426.3106499","DOIUrl":"https://doi.org/10.1145/3106426.3106499","url":null,"abstract":"This paper exhibits a novel context-aware service framework for IoT based Smart Traffic Management using ontology to regulate smooth traffic flow in smart cities by analyzing real-time traffic environment. The proposed approach makes smarter use of transport networks to achieve objectives related to performance of transport system. This requires efficient traffic planning measures which relate to the actions designed to adjust the demand and capacity of the network in time and space by use of IoT technologies. The adoption of sensors and IoT devices in Smart Traffic System helps to capture the user's preferences and context information which can be in the form of travel time, weather conditions or real-life driving patterns. We have employed multimedia ontology to derive higher level descriptions of traffic conditions and vehicles from perceptual observation of traffic information which provides important grounds for our proposed IoT framework. The multimedia ontology encoded in Multimedia Web Ontology Language(MOWL) helps to define classes, properties, and structure of a possible traffic environment to provide insights across the transportation network. MOWL supports Dynamic Bayesian networks (DBN) to deal with time-series data and uncertainties linked with context observations which fits the definition of an intelligent IoT system. Thus, our proposed smart traffic framework aggregates information corresponding to traffic domain such as traffic videos captured using CCTV cameras and allows automatic prediction of dynamically changing situations which helps to make traffic authorities more responsive. We have illustrated use of our approach by utilizing contextual information, to assess real-time congestion situation on roads thus allowing to visualize planning services. Once the congestion situation is predicted, alternate congestion free routes which are in accordance with the coveted criteria are suggested that can be propagated through text-messages or e-mails to the users.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"173 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79565614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17

Mining ordinal data under human response uncertainty 人类响应不确定性下的有序数据挖掘

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106448

Sergej Sizov

引用次数: 2

Adaptive training instance selection for cross-domain emotion identification 跨领域情感识别的自适应训练实例选择

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106457

Wenbo Wang, Lu Chen, Keke Chen, K. Thirunarayan, A. Sheth

{"title":"Adaptive training instance selection for cross-domain emotion identification","authors":"Wenbo Wang, Lu Chen, Keke Chen, K. Thirunarayan, A. Sheth","doi":"10.1145/3106426.3106457","DOIUrl":"https://doi.org/10.1145/3106426.3106457","url":null,"abstract":"This paper exploits a large number of self-labeled emotion tweets as the training data from the source domain to improve emotion identification in target domains (i.e., blogs and fairy tales), where there is a short supply of labeled data. Due to the noisy and ambiguous nature of self-labeled emotion training data, the existing domain adaptation methods that typically depend on high-quality labeled source-domain data do not work satisfactorily. This paper describes an adaptive source-domain training instance selection method to address the problem of noisy source-domain training data. The proposed approach can effectively identify the most informative training examples based on three carefully designed measures: consistency, diversity, and similarity. It uses an iterative method that consists of the following steps in each iteration: selecting informative samples from the source domain with the informativeness measures, merging with the target-domain training data, evaluating the performance of learned classifier for the target domain, and updating the informativeness measures for the next iteration. It stops until no new training instance is selected or in a designated number of iterations. Experiments show that our approach performs effectively for cross-domain emotion identification and consistently outperforms baseline approaches across four domains.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"294 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77050350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

A proactive event-driven approach for dynamic QoS compliance in cloud of things 在物联网中实现动态QoS遵从的主动事件驱动方法

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3109431

Falak Nawaz, O. Hussain, N. Janjua, Elizabeth Chang

引用次数: 7

Consensus-based ranking of wikipedia topics 基于共识的维基百科主题排名

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106529

Waleed Nema, Yinshan Tang

{"title":"Consensus-based ranking of wikipedia topics","authors":"Waleed Nema, Yinshan Tang","doi":"10.1145/3106426.3106529","DOIUrl":"https://doi.org/10.1145/3106426.3106529","url":null,"abstract":"To improve the effectiveness of users' information seeking experience in interactive web search we hypothesize how people might be influenced when making relevance judgment decisions by introducing the Consensus Theory & Relevance Judgment Model (CT&M). This is combined with a practical path to assess the extent of difference between suggestions of current search engines versus user expectations. A user-centered, evidence-based, phenomenology approach is used to improve on Google PageRank (GPR) in two ways. The first by biasing GPR's equal navigation probability assumption using (f)actual usage stats as implicit user consensus which leads to the StatsRank (SR) algorithm. Secondly, we aggregate users' explicit ranking to derive Consensus Rank (CR) which is shown to predict individual user ranking significantly better than GPR and meta-search of modern search engines Google and Yahoo/Bing real-time. CT&M contextualizes CR, SR, and a live open online web experiment, called The Ranking Game, which is based on the August-2016 English Wikipedia corpus (12.7 million pages) and Page View Statistics for May to July 2016. Limiting this work to Wikipedia makes GPR topic-based since any Wikipedia page is focused on one topic. TREC's pooling is used to merge top 20 results from major search engines and present an alphabetized list for users' explicit ranking via drag and drop. The same platform captures implicit data for future research and can be used for controlled experiments. Our contributions are: CT&M, SR, CR, and the open online user feedback web experiment research platform.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75260427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains 供应链规划交错审议中结构化与非结构化信息的联合利用

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106545

N. Janjua, O. Hussain, Elizabeth Chang, S. M. Islam

{"title":"Conjoint utilization of structured and unstructured information for planning interleaving deliberation in supply chains","authors":"N. Janjua, O. Hussain, Elizabeth Chang, S. M. Islam","doi":"10.1145/3106426.3106545","DOIUrl":"https://doi.org/10.1145/3106426.3106545","url":null,"abstract":"Effective business planning requires seamless access and intelligent analysis of information in its totality to allow the business planner to gain enhanced critical business insights for decision support. Current business planning tools provide insights from structured business data (i.e. sales forecasts, customers and products data, inventory details) only and fail to take into account unstructured complementary information residing in contracts, reports, user's comments, emails etc. In this article, a planning support system is designed and developed that empower business planners to develop and revise business plans utilizing both structured data and unstructured information conjointly. This planning system activity model comprises of two steps. Firstly, a business planner develops a candidate plan using planning template. Secondly, the candidate plan is put forward to collaborating partners for its revision interleaving deliberation. Planning interleaving deliberation activity in the proposed framework enables collaborating planners to challenge both a decision and the thinking that underpins the decision in the candidate plan. The planning system is modeled using situation calculus and is validated through a prototype development.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75556976","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fusing domain-specific data with general data for in-domain applications 将特定于领域的数据与域内应用程序的通用数据融合

Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics Pub Date : 2017-08-23 DOI: 10.1145/3106426.3106473

An-Zi Yen, Hen-Hsen Huang, Hsin-Hsi Chen

引用次数: 1