Multi-goal pathfinding (MGPF) is a problem of searching for a path between a start and a destination allowing a set of goals to be satisfied. We address MGPF in ubiquitous environments that accommodate cyber, physical and social (CPS) entities from smart objects to sensors and to humans. Given a MGPF problem in a pervasive environment, our approach aims at exploiting data from various resources including CPS entities located in the environment and external resources such as the Web to solve the problem. In this paper, we present a knowledge model for describing a ubiquitous environment integrating its spatial dimension, CPS entities it contains and its relevant resources. A global view of the approach is provided. We address particularly one of the challenges in MGPF, namely goal satisfaction problem, which consists of identifying through which entities a goal can be satisfied. Towards this aim, we design an ontology to formally model CPS entities, goals and their relations. We describe a method to exploit modeled knowledge in order to solve the goal satisfaction problem.
{"title":"Multi-goal pathfinding in ubiquitous environments: modeling and exploiting knowledge to satisfy goals","authors":"O. Kem, Flavien Balbo, Antoine Zimmermann","doi":"10.1145/3106426.3109054","DOIUrl":"https://doi.org/10.1145/3106426.3109054","url":null,"abstract":"Multi-goal pathfinding (MGPF) is a problem of searching for a path between a start and a destination allowing a set of goals to be satisfied. We address MGPF in ubiquitous environments that accommodate cyber, physical and social (CPS) entities from smart objects to sensors and to humans. Given a MGPF problem in a pervasive environment, our approach aims at exploiting data from various resources including CPS entities located in the environment and external resources such as the Web to solve the problem. In this paper, we present a knowledge model for describing a ubiquitous environment integrating its spatial dimension, CPS entities it contains and its relevant resources. A global view of the approach is provided. We address particularly one of the challenges in MGPF, namely goal satisfaction problem, which consists of identifying through which entities a goal can be satisfied. Towards this aim, we design an ontology to formally model CPS entities, goals and their relations. We describe a method to exploit modeled knowledge in order to solve the goal satisfaction problem.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89049594","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}
An objective assessment of collaborative filtering techniques and recommender systems requires application of suitable predictive accuracy metrics. In real life, individuals meet their decisions with considerable uncertainty. This raises the question to what extent the comparison between observed and predicted user responses can be seen as an evident proof of systematic quality differences. In this paper, we accordingly justify underlying assumptions of quality assessment, introduce an appropriate uncertainty-aware evaluation strategy for recommender comparisons, and demonstrate its feasibility and consistency in experiments with real users.
{"title":"Comparative assessment of rating prediction techniques under response uncertainty","authors":"Sergej Sizov","doi":"10.1145/3106426.3106506","DOIUrl":"https://doi.org/10.1145/3106426.3106506","url":null,"abstract":"An objective assessment of collaborative filtering techniques and recommender systems requires application of suitable predictive accuracy metrics. In real life, individuals meet their decisions with considerable uncertainty. This raises the question to what extent the comparison between observed and predicted user responses can be seen as an evident proof of systematic quality differences. In this paper, we accordingly justify underlying assumptions of quality assessment, introduce an appropriate uncertainty-aware evaluation strategy for recommender comparisons, and demonstrate its feasibility and consistency in experiments with real users.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83727890","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}
Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto, Heiko Paulheim
Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.
{"title":"Large-scale taxonomy induction using entity and word embeddings","authors":"Petar Ristoski, Stefano Faralli, Simone Paolo Ponzetto, Heiko Paulheim","doi":"10.1145/3106426.3106465","DOIUrl":"https://doi.org/10.1145/3106426.3106465","url":null,"abstract":"Taxonomies are an important ingredient of knowledge organization, and serve as a backbone for more sophisticated knowledge representations in intelligent systems, such as formal ontologies. However, building taxonomies manually is a costly endeavor, and hence, automatic methods for taxonomy induction are a good alternative to build large-scale taxonomies. In this paper, we propose TIEmb, an approach for automatic unsupervised class subsumption axiom extraction from knowledge bases using entity and text embeddings. We apply the approach on the WebIsA database, a database of subsumption relations extracted from the large portion of the World Wide Web, to extract class hierarchies in the Person and Place domain.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"95 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88200365","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}
Ontology, the backbone of Semantic Web, is defined as the formal specification of conceptual hierarchy with relationships between concepts. Ontology Learning (OL) is a process to create an ontology from text automatically or semi-automatically. OL is an important topic in the Semantic Web field in the last two decades but it is still not mature in Arabic not like Latin languages. Currently, there is a limited support for using knowledge from Arabic literature automatically in semantically-enabled systems. Deep Learning (DL), an artificial neural networks learning based application, has proved a good improvement in multiple areas including text mining. By using DL, it is possible to have word embedding as distributed word representations from textual data. The application of DL to aid Arabic ontology development remains largely unexplored. This paper investigates the performance of implementing DL with Arabic ontology learning tasks using major models such as Continuous Bag of Words (CBOW) and Skip-gram. Initial performance results are promising as an effective application of Arabic ontology learning.
{"title":"Arabic ontology learning using deep learning","authors":"Saeed Al-Bukhitan, T. Helmy, A. Al-Nazer","doi":"10.1145/3106426.3109052","DOIUrl":"https://doi.org/10.1145/3106426.3109052","url":null,"abstract":"Ontology, the backbone of Semantic Web, is defined as the formal specification of conceptual hierarchy with relationships between concepts. Ontology Learning (OL) is a process to create an ontology from text automatically or semi-automatically. OL is an important topic in the Semantic Web field in the last two decades but it is still not mature in Arabic not like Latin languages. Currently, there is a limited support for using knowledge from Arabic literature automatically in semantically-enabled systems. Deep Learning (DL), an artificial neural networks learning based application, has proved a good improvement in multiple areas including text mining. By using DL, it is possible to have word embedding as distributed word representations from textual data. The application of DL to aid Arabic ontology development remains largely unexplored. This paper investigates the performance of implementing DL with Arabic ontology learning tasks using major models such as Continuous Bag of Words (CBOW) and Skip-gram. Initial performance results are promising as an effective application of Arabic ontology learning.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90649040","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}
Human activity recognition aims to recognize human activities from sensor readings. Most of existing methods in this area can only recognize activities contained in training dataset. However, in practical applications, previously unseen activities are often encountered. In this paper, we propose a new zero-shot learning method to solve the problem of recognizing previously unseen activities. The proposed method learns a nonlinear compatibility function between feature space instances and semantic space prototypes. With this function, testing instances are classified to unseen activities with highest compatibility scores. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on three public datasets. Experimental results show that our proposed method consistently outperforms state-of-the-art methods in human activity recognition problems.
{"title":"Zero-shot human activity recognition via nonlinear compatibility based method","authors":"Wei Wang, C. Miao, Shuji Hao","doi":"10.1145/3106426.3106526","DOIUrl":"https://doi.org/10.1145/3106426.3106526","url":null,"abstract":"Human activity recognition aims to recognize human activities from sensor readings. Most of existing methods in this area can only recognize activities contained in training dataset. However, in practical applications, previously unseen activities are often encountered. In this paper, we propose a new zero-shot learning method to solve the problem of recognizing previously unseen activities. The proposed method learns a nonlinear compatibility function between feature space instances and semantic space prototypes. With this function, testing instances are classified to unseen activities with highest compatibility scores. To evaluate the effectiveness of the proposed method, we conduct extensive experiments on three public datasets. Experimental results show that our proposed method consistently outperforms state-of-the-art methods in human activity recognition problems.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78756399","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}
Recommender systems have been successfully applied to alleviate the information overload and assist user's decision makings. Emotional states have been demonstrated as effective factors in recommender systems. However, how to collect or predict a user's emotional state becomes one of the challenges to build affective recommender systems. In this paper, we explore and compare different solutions to predict emotions to be applied in the recommendation process. More specifically, we propose an approach named as collaborative chains. It predicts emotional states in a collaborative way and additionally takes correlations among emotions into consideration. Our experimental results based on a movie rating data demonstrate the effectiveness of affective prediction by collaborative chains in movie recommendations.
{"title":"Affective prediction by collaborative chains in movie recommendation","authors":"Yong Zheng","doi":"10.1145/3106426.3106535","DOIUrl":"https://doi.org/10.1145/3106426.3106535","url":null,"abstract":"Recommender systems have been successfully applied to alleviate the information overload and assist user's decision makings. Emotional states have been demonstrated as effective factors in recommender systems. However, how to collect or predict a user's emotional state becomes one of the challenges to build affective recommender systems. In this paper, we explore and compare different solutions to predict emotions to be applied in the recommendation process. More specifically, we propose an approach named as collaborative chains. It predicts emotional states in a collaborative way and additionally takes correlations among emotions into consideration. Our experimental results based on a movie rating data demonstrate the effectiveness of affective prediction by collaborative chains in movie recommendations.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85703981","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}
In this paper, to resolve some issues on personal assistance that are working on some social network services, we present a platform that allows agents to analyze some associated informations to make effective protraction and prevention of cyber-bullying. We also present a prototype implementation of our platform that allows agents handle and analyze contexts on the Mastodon-based social networks. On the current implementation, a personal assistant agent can run on a same browser that opened web site of the social networking service.
{"title":"MstdnDeck: an agent-based protection of cyber-bullying on distributedly managed linked microbloggings","authors":"Sho Oishi, Naoki Fukuta","doi":"10.1145/3106426.3109415","DOIUrl":"https://doi.org/10.1145/3106426.3109415","url":null,"abstract":"In this paper, to resolve some issues on personal assistance that are working on some social network services, we present a platform that allows agents to analyze some associated informations to make effective protraction and prevention of cyber-bullying. We also present a prototype implementation of our platform that allows agents handle and analyze contexts on the Mastodon-based social networks. On the current implementation, a personal assistant agent can run on a same browser that opened web site of the social networking service.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"54 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83275752","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}
Crowdsourcing platforms provide an attractive solution for processing numerous tasks at low cost. However, insufficient quality control remains a major concern. In the present study, we propose a grade-based training method for workers. Our training method utilizes probabilistic networks to estimate correlations between tasks based on workers' records for 18.5 million tasks and then allocates pre-learning tasks to the workers to raise the accuracy of target tasks according to the task correlations. In an experiment, the method automatically allocated 31 pre-learning task categories for 9 target task categories, and after the training of the pre-learning tasks, we confirmed that the accuracy of the target tasks was raised by 7.8 points on average. We thus confirmed that the task correlations can be estimated using a large amount of worker records, and that these are useful for the grade-based training of low-quality workers.
{"title":"Crowdsourcing worker development based on probabilistic task network","authors":"Masayuki Ashikawa, Takahiro Kawamura, Akihiko Ohsuga","doi":"10.1145/3106426.3106501","DOIUrl":"https://doi.org/10.1145/3106426.3106501","url":null,"abstract":"Crowdsourcing platforms provide an attractive solution for processing numerous tasks at low cost. However, insufficient quality control remains a major concern. In the present study, we propose a grade-based training method for workers. Our training method utilizes probabilistic networks to estimate correlations between tasks based on workers' records for 18.5 million tasks and then allocates pre-learning tasks to the workers to raise the accuracy of target tasks according to the task correlations. In an experiment, the method automatically allocated 31 pre-learning task categories for 9 target task categories, and after the training of the pre-learning tasks, we confirmed that the accuracy of the target tasks was raised by 7.8 points on average. We thus confirmed that the task correlations can be estimated using a large amount of worker records, and that these are useful for the grade-based training of low-quality workers.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90861267","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}
Supporting heterogeneous ontologies is an important issue on retrieving from and linking to linked data stored in various SPARQL endpoints via SPARQL queries. There are several approaches to support the coding process of a SPARQL query for users who are unfamiliar to code it. On the use of some ontology mapping-based support approaches on SPARQL-based query systems, we often assume that the users already have appropriate weighted ontology mappings for the ontologies used in the query. In this paper, we present ontology mapping inspection mechanisms for mapping-enhanced SPARQL queries to widely retrieve various data from Linked Open Data (LOD). Our dynamic ontology mapping adaptation technique complements the used incomplete ontology mappings by dynamically detecting and adding missing mappings to include the correspondences between entities of terms in heterogeneous ontologies.
{"title":"A mapping-enhanced linked data inspection and querying support system using dynamic ontology matching","authors":"Takuya Adachi, Naoki Fukuta","doi":"10.1145/3106426.3109414","DOIUrl":"https://doi.org/10.1145/3106426.3109414","url":null,"abstract":"Supporting heterogeneous ontologies is an important issue on retrieving from and linking to linked data stored in various SPARQL endpoints via SPARQL queries. There are several approaches to support the coding process of a SPARQL query for users who are unfamiliar to code it. On the use of some ontology mapping-based support approaches on SPARQL-based query systems, we often assume that the users already have appropriate weighted ontology mappings for the ontologies used in the query. In this paper, we present ontology mapping inspection mechanisms for mapping-enhanced SPARQL queries to widely retrieve various data from Linked Open Data (LOD). Our dynamic ontology mapping adaptation technique complements the used incomplete ontology mappings by dynamically detecting and adding missing mappings to include the correspondences between entities of terms in heterogeneous ontologies.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90755755","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}
Alex Hernández-García, F. Martínez, F. Díaz-de-María
This paper contributes to the field of affective video content analysis through the novel employment of electrodermal activity (EDA) measurements as ground truth for machine learning algorithms. The variation of the electrical properties of the skin, known as EDA, is a psychophysiological indicator widely used in medicine, psychology and neuroscience which can be considered a somatic marker of the emotional and attentional reaction of subjects towards stimuli. One of its main advantages is that the recorded information is not biased by the cognitive process of giving an opinion or a score to characterize the subjective perception. In this work, we predict the levels of emotion and attention, derived from EDA records, by means of a small set of low-level visual descriptors computed from the video stimuli. Linear regression experiments show that our descriptors predict significantly well the sum of emotion and attention levels, reaching a coefficient of determination R2 = 0.25. This result sets a promising path for further research on the prediction of emotion and attention from videos using EDA.
{"title":"Emotion and attention: predicting electrodermal activity through video visual descriptors","authors":"Alex Hernández-García, F. Martínez, F. Díaz-de-María","doi":"10.1145/3106426.3109418","DOIUrl":"https://doi.org/10.1145/3106426.3109418","url":null,"abstract":"This paper contributes to the field of affective video content analysis through the novel employment of electrodermal activity (EDA) measurements as ground truth for machine learning algorithms. The variation of the electrical properties of the skin, known as EDA, is a psychophysiological indicator widely used in medicine, psychology and neuroscience which can be considered a somatic marker of the emotional and attentional reaction of subjects towards stimuli. One of its main advantages is that the recorded information is not biased by the cognitive process of giving an opinion or a score to characterize the subjective perception. In this work, we predict the levels of emotion and attention, derived from EDA records, by means of a small set of low-level visual descriptors computed from the video stimuli. Linear regression experiments show that our descriptors predict significantly well the sum of emotion and attention levels, reaching a coefficient of determination R2 = 0.25. This result sets a promising path for further research on the prediction of emotion and attention from videos using EDA.","PeriodicalId":20685,"journal":{"name":"Proceedings of the 7th International Conference on Web Intelligence, Mining and Semantics","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78417325","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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