Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663801
Y. Yao, R. Wu, Hui Liu
{"title":"JTOWL: A JSON to OWL Converto","authors":"Y. Yao, R. Wu, Hui Liu","doi":"10.1145/2663792.2663801","DOIUrl":"https://doi.org/10.1145/2663792.2663801","url":null,"abstract":"JSON is a popular web data interchange format in Internet, especially for Social Network applications. To process these current JSON data sets using Semantic Web technologies, we should firstly convert them into well-defined semantic description languages, such as OWL. In this paper, we propose a method, which can automatically convert related JSON data sets into OWL ontology. This method can extend data semantics using semantic reasoning and combine multiple related resources into a unique ontology. The constructed semantic data contains concepts, properties, constrains and values which are implied in JSON object. We develop the method as a JSON to OWL convertor JTOWL that can process web data resources and create semantic data effectively.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128255616","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663799
I. Nawrot, Oskar Gross, A. Doucet, Hannu (TT) Toivonen
{"title":"Novel Query Suggestions: Initial Work Report","authors":"I. Nawrot, Oskar Gross, A. Doucet, Hannu (TT) Toivonen","doi":"10.1145/2663792.2663799","DOIUrl":"https://doi.org/10.1145/2663792.2663799","url":null,"abstract":"Query auto-completion (QAC) is one of the most recognizable and widely used services of modern search engines. Its goal is to assist a user in the process of query formulation. Current QAC systems are mainly reactive. They respond to the present request using past knowledge. Specifically, they mostly rely on query logs analysis or corpus terms co-occurrences and rank suggestions according to their similarity with the partial user query, their past popularity, or their temporal dynamics features (e.g. trends, bursts, seasonality in query popularity). Consequently, a suggestion to be recommended by the QAC system must be preceded with a substantial users' interest and ipso facto must be an old information. However, a growing amount of people turns to search engines to find novel information, that is emergent or recently created (not redundant) one. Conventional QAC systems are thus unable to fulfill the increasingly real-time needs of the users.\u0000 In this work-in-progress report, we introduce a new approach to QAC - the system filtering out potentially novel information and proactively delivering it to the users. It aims at providing the users with some novel insight. Thus, it caters for their open-ended or persistent and increasingly real-time information needs. The preliminary method proposed in this paper to evaluate this approach forms time specific suggestions based on a comparison of two corpora constantly being updated with new data from chosen sources. An unsupervised and language-independent algorithm relying on relative novelty of terms co-occurrences is used to generate suggestions. The initial experimental results demonstrate the effectiveness of the approach in recommending queries leading to novel information. Therefore, they prove that such a system can enhance the exploratory power of a search engine and support the proactive information search.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115034692","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663802
K. Stefanidis, Georgia Koloniari
{"title":"Enabling Social Search in Time through Graphs","authors":"K. Stefanidis, Georgia Koloniari","doi":"10.1145/2663792.2663802","DOIUrl":"https://doi.org/10.1145/2663792.2663802","url":null,"abstract":"Recently, social networks have attracted considerable attention. The huge volume of information contained in them, as well as their dynamic nature, make the problem of searching social data challenging. In this work, we envision the design of a complete framework for social search by exploiting both the underlying social graph and the temporal information available in social networks. To encompass the numerous search needs, our framework includes a time-aware graph and query model. To deploy the proposed query model over any social network, we define a logical algebra that provides a set of operators required for query evaluation, and for supporting a ranking functionality. We conclude by presenting SQTime, a prototype that implements our framework.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132346527","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663793
Qun Luo, Weiran Xu, Jun Guo
{"title":"A Study on the CBOW Model's Overfitting and Stability","authors":"Qun Luo, Weiran Xu, Jun Guo","doi":"10.1145/2663792.2663793","DOIUrl":"https://doi.org/10.1145/2663792.2663793","url":null,"abstract":"Word vectors are distributed representations of word features. Continuous Bag-of-Words Model(CBOW) is a state-of-the-art model for learning word vectors, yet can be ameliorated for learning better word vectors because we find that CBOW is vulnerable to be overfitted and unstable. We use two methods to solve these two problems so that CBOW can learn better word vectors. In this study, we add the regularized structure risk summation to the objective function of the CBOW model and propose inverse word frequency encoding for the CBOW model. Our proposed methods substantially improve the quality of word vectors, boosting r from 0.638 to 0.696 for word relatedness and total accuracy from 30.80% to 38.43% for word pairs relationship relatedness regarding to 52 million training words with 200 dimensionality.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124950271","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663796
Saravadee Sae Tan, Lay-Ki Soon, T. Lim, E. Tang, C. Loo
{"title":"Learning the Mapping Rules for Sentiment Analysis","authors":"Saravadee Sae Tan, Lay-Ki Soon, T. Lim, E. Tang, C. Loo","doi":"10.1145/2663792.2663796","DOIUrl":"https://doi.org/10.1145/2663792.2663796","url":null,"abstract":"There is an increasing popularity of people posting their feelings on microblogging such as Twitter. Sentiment analysis on the tweets allows organizations to monitor public' feelings towards a product or brand. In this paper, we model sentiment analysis problem as a multi-classification approach that utilizes various feature types, including predicate-argument relation, hashtag, mention and emotion in the tweets. We describe a Content-Structure Correspondence (CSC) model that is able to represent diverse feature types in a tweet. We present a conceptual hierarchy to express the characteristics of a tweet. A multi-classification approach is used to map tweet content to the conceptual hierarchy. The mapping patterns are learned to identify the sentiment of a tweet.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122269193","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663800
Snehasish Banerjee, Abhishek Mishra, R. Dasgupta
{"title":"Semantic Exploration of Sensor Data","authors":"Snehasish Banerjee, Abhishek Mishra, R. Dasgupta","doi":"10.1145/2663792.2663800","DOIUrl":"https://doi.org/10.1145/2663792.2663800","url":null,"abstract":"With governments and administrations releasing open linked data, and with the gradual rise of sensor deployments across the world, semantic queries on the combined sensor and linked data has become a need to provide several intelligent smart city services and applications. The data is represented in form of triples (RDF), concepts and relations in form of ontologies (OWL) and the corresponding query language is SPARQL as per standards of Semantic Web. In this paper, a system for sensor exploration is presented, which takes a set of keywords, context, data, learned and background knowledge as input and produces the intentioned result as output. The system tries to keep the underlying semantic web technologies transparent to the end user. The relevant challenges and the scope of future work is also discussed.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124835442","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663797
Saravadee Sae Tan, T. Lim, Lay-Ki Soon, E. Tang
{"title":"Learning to Match Heterogeneous Structures using Partially Labeled Data","authors":"Saravadee Sae Tan, T. Lim, Lay-Ki Soon, E. Tang","doi":"10.1145/2663792.2663797","DOIUrl":"https://doi.org/10.1145/2663792.2663797","url":null,"abstract":"This paper addresses the problem of matching between highly heterogeneous structures. The problem is modeled as a classification task where training examples are used to learn the matching between structures. In our approach, training is performed using partially labeled data. We propose a Greedy Mapping approach to generate training examples from partially labeled data. Different types of structures may have different types of attributes that can be exploited to enhance the matching problem. We utilize three types of attributes, namely, text content, structure name and path correspondence, in the matching problem. Experiments are performed on two types of structures: semantic domain and semantic role. We evaluate the effectiveness of the Greedy Mapping as well as the performance on different types of attributes. Finally, the results are presented and discussed.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127668788","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663803
R. Nayak, Rachel Mills, R. D. Vries, S. Geva
{"title":"Clustering and Labeling a Web Scale Document Collection using Wikipedia clusters","authors":"R. Nayak, Rachel Mills, R. D. Vries, S. Geva","doi":"10.1145/2663792.2663803","DOIUrl":"https://doi.org/10.1145/2663792.2663803","url":null,"abstract":"Clustering is an important technique in organising and categorising web scale documents. The main challenges faced in clustering the billions of documents available on the web are the processing power required and the sheer size of the datasets available. More importantly, it is nigh impossible to generate the labels for a general web document collection containing billions of documents and a vast taxonomy of topics. However, document clusters are most commonly evaluated by comparison to a ground truth set of labels for documents. This paper presents a clustering and labeling solution where the Wikipedia is clustered and hundreds of millions of web documents in ClueWeb12 are mapped on to those clusters. This solution is based on the assumption that the Wikipedia contains such a wide range of diverse topics that it represents a small scale web. We found that it was possible to perform the web scale document clustering and labeling process on one desktop computer under a couple of days for the Wikipedia clustering solution containing about 1000 clusters. It takes longer to execute a solution with finer granularity clusters such as 10,000 or 50,000. These results were evaluated using a set of external data.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122979934","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663794
Sandeep Panem, Manish Gupta, Vasudeva Varma
{"title":"Structured Information Extraction from Natural Disaster Events on Twitter","authors":"Sandeep Panem, Manish Gupta, Vasudeva Varma","doi":"10.1145/2663792.2663794","DOIUrl":"https://doi.org/10.1145/2663792.2663794","url":null,"abstract":"As soon as natural disaster events happen, users are eager to know more about them. However, search engines currently provide a ten blue links interface for queries related to such events. Relevance of results for such queries can be significantly improved if users are shown a structured summary of the fresh events related to such queries. This would not just reduce the number of user clicks to get the relevant information but would also help users get updated with more fine grained attribute-level information. Twitter is a great source that can be exploited for obtaining such fine-grained structured information for fresh natural disaster events. Such events are often reported on Twitter much earlier than on other news media. However, extracting such structured information from tweets is challenging because: 1. tweets are noisy and ambiguous; 2. there is no well defined schema for various types of natural disaster events; 3. it is not trivial to extract attribute-value pairs and facts from unstructured text; and 4. it is difficult to find good mappings between extracted attributes and attributes in the event schema.\u0000 We propose algorithms to extract attribute-value pairs, and also devise novel mechanisms to map such pairs to manually generated schemas for natural disaster events. Besides the tweet text, we also leverage text from URL links in the tweets to fill such schemas. Our schemas are temporal in nature and the values are updated whenever fresh information flows in from human sensors on Twitter. Evaluation on ~58000 tweets for 20 events shows that our system can fill such event schemas with an F1 of ~0.6.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124051188","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}
Web-KR '14Pub Date : 2014-11-03DOI: 10.1145/2663792.2663798
Chao He, Kun Yue, Hao Wu, Weiyi Liu
{"title":"Structure Learning of Bayesian Network with Latent Variables by Weight-Induced Refinement","authors":"Chao He, Kun Yue, Hao Wu, Weiyi Liu","doi":"10.1145/2663792.2663798","DOIUrl":"https://doi.org/10.1145/2663792.2663798","url":null,"abstract":"Bayesian network (BN) with latent variables (LVs) provides a concise and straightforward framework for representing and inferring uncertain knowledge with unobservable variables or with regard to missing data. To learn the BN with LVs consistently with the realistic situations, we propose the information theory based concept of existence weight and incorporate it into the clique-based learning method. In line with the challenges when learning BN with LVs, we focus on determining the number of LVs, and determining the relationships between LVs and the observed variables. First, we define the existence weight and propose the algorithms for finding the ε-cliques from the BN without LVs learned from data. Then, we introduce the LV to each ε-clique and adjust the BN structure with LVs. Further, we adjust the value of parameter ε to determine the number of LVs. Experimental results show the feasibility of our method.","PeriodicalId":289794,"journal":{"name":"Web-KR '14","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123395932","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}
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