In the last decade, we have witnessed the birth and spread of the so-called Semantic Web. From it... more In the last decade, we have witnessed the birth and spread of the so-called Semantic Web. From its initial proposal by Tim Berners-Lee , to the latest trends and initiatives, such as Linked Data and DBpedia, the Semantic Web is progressively changing the landscape of the World Wide Web (WWW) through the use and adoption of the different semantic technologies that have come along with it. We can see how, although some of the goals of the Semantic Web have not been reached yet, several well-known and successful applications are already using semantic technologies, such as Google’s Knowledge Graph, Microsoft’s Satori, or Facebook’s Graph Search. In this successful scenario, ontologies have played a crucial role. Defined by Tom Gruber as “an explicit specification of a conceptualization” , ontologies allow to model and capture the semantics of different knowledge domains, providing a means to share definitions, and reach an implicit agreement on the meaning of the published information. Ontologies represent the vocabulary of some domain from a common perspective using a formal language, such as the current standard Web Ontology Language. Thus, with their advance and the development of their associated technologies, we can now explore deeper in the quest for smarter information Systems which exploit the semantics of data.
Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces... more Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM~2013~and SemEval-2014~tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of text. Additional wrappers and resources were used to handle task specific challenges that include processing Spanish text, comparing text sequences of different lengths, handling informal words and phrases, and matching words with sense definitions. In the *SEM~2013~task on Semantic Textual Similarity, our best performing system ranked first among the~89~submitted runs. In the SemEval-2014~task on Multilingual Semantic Textual Similarity, we ranked a close second in both the English and Spanish subtasks. In the SemEval-2014~task on Cross--Level Semantic Similarity, we ranked first in Sentence--Phrase, Phrase--Word, and Word--Sense subtasks and second in the Paragraph--Sentence subtask.
The advantage of using semantic reasoners based on Description Logics (DL) for the development of... more The advantage of using semantic reasoners based on Description Logics (DL) for the development of intelligent systems is doubtless: They make easy the optimal management of knowledge (expressed as ontologies). Reasoning is a complex and computationally expensive task traditionally performed on powerful server and desktop computers. However, we should not discard reasoners from being used on mobile devices such as smartphones or tablets, as the increasing number of applications run on wireless environments demands that many intelligent tasks should be performed on mobile devices rather than on desktop or server computers. Although less powerful that their fixed counterparts, mobile devices are becoming more and more capable of running complex tasks, such as DL reasoning. In this paper we introduce the framework of a competition for reasoners on mobile devices, based on the OWL Reasoner Evaluation, with the main goal of promoting the development of reasoners adapted to mobile environm...
The massive spread of mobile computing in our daily lives has attracted a huge community of mobil... more The massive spread of mobile computing in our daily lives has attracted a huge community of mobile application (apps) developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, and knowledge decoupling) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of discovering new knowledge, inferred from the available information.
However, using semantic APIs and reasoners on current mobile devices is not a trivial task. In this paper, we show that the most popular current available Description Logics (DL) reasoners can be used on Android-based devices, and detail the efforts needed to port them to the Android platform. We also analyze the performance of these reasoners on current smartphones/tablets against more than 300 ontologies from the ORE 2013 ontology set, showing that, despite a notable difference with respect to desktop computers, their use is feasible.
3rd International Workshop on OWL Reasoner Evaluation (ORE 2014), 2014
Applications for mobile devices could often show a more in-
telligent behavior by using a semant... more Applications for mobile devices could often show a more in-
telligent behavior by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL 2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL 2 DL and OWL 2 EL reasoners on Android smartphones and tablets.
Location-Based Services (LBSs) are attracting nowadays a great interest, mainly due to the econom... more Location-Based Services (LBSs) are attracting nowadays a great interest, mainly due to the economic value they can provide. So, different applications are being developed for tracking, navigation, advertising, etc., but most of those applications are designed for specific scenarios and goals with implicit knowledge about the application context. However, currently it is a challenge to provide a common framework that allows to manage knowledge obtained from data sent by heterogeneous moving objects (textual data, multimedia data, sensor data, etc.). Moreover, the challenge is even greater considering situations where the system must adapt itself to contexts where the knowledge changes dynamically and in which moving objects can use different underlying wireless technologies and positioning systems.
In this paper we present the system SHERLOCK, that offers a common framework with new functionalities for LBSs. Our system processes user requests continuously to provide up-to-date answers in heterogeneous and dynamic contexts. Ontologies and semantic techniques are used to share knowledge among devices, which enables the system to guide the user selecting the service that best fits his/her needs in the given context. Moreover, the system uses mobile agent technology to carry the processing tasks wherever necessary in the dynamic underlying networks at any time.
2nd International Workshop on OWL Reasoner Evaluation (ORE 2013), 2013
The massive spread of mobile computing in our daily lives
has attracted a huge community of mobi... more The massive spread of mobile computing in our daily lives
has attracted a huge community of mobile apps developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, knowledge decoupling, etc.) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of inferring logical consequences from the knowledge considered. However, using semantic APIs and reasoners on current Android-based devices is not problem-free and, currently, there are no remarkable efforts to enable mobile devices with semantic reasoning capabilities.
In this paper, we analyze whether the most popular current available DL reasoners can be used on Android-based devices. We evaluate the efforts needed to port them to the Android platform, taking into account its limitations, and present some tests to show the performance of these reasoners on current smartphones/tablets.
12th annual international conference on Mobile systems, applications, and services (MobiSys '2014), 2014
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts t... more FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts them into Privacy-Aware Pictures. These pictures are generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). FaceBlock would automatically generate a mathematical representation of face identifier for this picture. Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by. FaceBlock is a proof of concept implementation of a system that can create Privacy-Aware Pictures using smart devices. The pervasiveness of Privacy-Aware Pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable technology without being oblivious about the privacy of those around you.
13th International Semantic Web Conference (ISWC 2014)
Infoboxer uses statistical and semantic knowledge from linked data sources to ease the process of... more Infoboxer uses statistical and semantic knowledge from linked data sources to ease the process of creating Wikipedia infoboxes. It creates dynamic and semantic templates by suggesting attributes common for similar articles and controlling the expected values semantically.
For a Technical Director (TD) in charge of a live broadcasting, selecting the best camera shots a... more For a Technical Director (TD) in charge of a live broadcasting, selecting the best camera shots among the available video sources is a challenging task, even more now that the number of cameras (some of them mobile, or attached to moving objects) in the broadcasting of sport events is increasing. So, the TD needs to manage a great amount of continuously changing information to quickly select the camera whose view should be broadcasted. Besides, the better the decisions made by the TD, the more interesting the content for the audience. Therefore, the development of systems that could help the TD with the selection of camera views is demanded by broadcasting organizations.
In this paper, we present the system MultiCAMBA that helps TDs in the live broadcasting task by allowing them to indicate in run-time their interest in certain kind of shots, and the system will show the cameras that are able to provide them. To achieve this task, the system manages location-dependent queries generated according to the interests of the TD. Moreover, to avoid the use of costly on line real-image processing techniques over the camera views, such real camera views are recreated in a 3D engine by using the information contained in a 3D model of the scenario. This model is updated continuously with real-time data retrieved from the real objects and cameras in the scenario. In this way, the system extracts high-level semantic features of 2D projections of the 3D reconstruction of the camera views. We present a prototype of the system and experimental results that show the feasibility of our proposal.
8th International Workshop on Semantic Evaluation (SemEval 2014)
ABSTRACT We describe UMBC's systems developed for the SemEval 2014 tasks on Multi-lingual... more ABSTRACT We describe UMBC's systems developed for the SemEval 2014 tasks on Multi-lingual Semantic Textual Similarity (Task 10) and Cross-Level Semantic Similarity (Task 3). Our best submission in the Multilingual task ranked second in both English and Spanish subtasks using an unsupervised approach. Our best sys-tems for Cross-Level task ranked second in Paragraph-Sentence and first in both Sentence-Phrase and Word-Sense subtask. The system ranked first for the Phrase-Word subtask but was not included in the official results due to a late submission.
2nd International Workshop on Society, Privacy and the Semantic Web – Policy and Technology (PrivOn 2014)
Wearable computing devices like Google Glass are at the forefront of technological evolution in s... more Wearable computing devices like Google Glass are at the forefront of technological evolution in smart devices. The ubiquitous and oblivious nature of photography using these devices has made people concerned about their privacy in private and public settings. The FaceBlock project protects the privacy of people around Glass users by making pictures taken by the latter, Privacy-Aware. Through sharing of privacy policies, users can choose whether or not to be included in pictures. However, the current privacy model of FaceBlock only permits simple constraints such as allow versus disallow pictures. In this paper, we present an extended context-aware privacy model represented using OWL ontologies and SWRL rules. We also describe use cases of how this model can help FaceBlock to generate Privacy-Aware Pictures depending on context and privacy needs of the user.
3rd International Workshop on OWL Reasoner Evaluation (ORE 2014)
Applications for mobile devices could often show a more intelligent behavior by using a semantic ... more Applications for mobile devices could often show a more intelligent behavior by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL 2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL 2 DL and OWL 2 EL reasoners on Android smartphones and tablets.
22nd International World Wide Web Conference (WWW 2013)
Nowadays people are exposed to huge amounts of information that are generated continuously. Howev... more Nowadays people are exposed to huge amounts of information that are generated continuously. However, current mobile applications, Web pages, and Location-Based Services (LBSs) are designed for specific scenarios and goals. In this demo we show the system SHERLOCK, which searches and shares up-to-date knowledge from nearby devices to relieve the user from knowing and managing such knowledge directly. Besides, the system guides the user in the process of selecting the service that best fits his/her needs in the given context.
8th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2011), 2011
In this demo paper we present MultiCAMBA (Multi-CAMera Broadcasting Assistant), a context– and lo... more In this demo paper we present MultiCAMBA (Multi-CAMera Broadcasting Assistant), a context– and location–aware system that, using a 3D model updated continuously with real-time data retrieved from the scenario, helps technical directors (TDs) in the live broadcasting task. They can indicate in run-time their interest in certain moving objects or geographic areas, and the system is in charge of selecting the cameras that can provide the kind of view required. To achieve this task, the system continuously recreates the views of the cameras in a 3D scenario, considering possible occlusions among the objects.
The production costs of broadcasting sport events that require tracking moving objects are contin... more The production costs of broadcasting sport events that require tracking moving objects are continuously increasing. Although those events are very demanded by the audience, broadcasting organizations have economical difficulties to afford them. For that reason, they are demanding the development of new professional (software and hardware) equipments that lead to a considerable reduction of the production costs.
In this paper, we present a software system that takes into account these needs. This system allows a technical director to indicate his/her interest about certain moving objects or geographic areas in run-time. The system is in charge of selecting the cameras that can provide the types of views requested on those interesting objects and areas. So, it decreases the human effort needed to produce (create, edit and distribute) audiovisual contents, giving at the same time the opportunity to increase their quality. For this, the system provides a friendly interface to specify requirements and obtain which monitoring video cameras attached to moving or static objects fulfill them, along with a query processor to handle those requests in a continuous and efficient way. We illustrate the feasibility of our system in a specific scenario using real data of a traditional rowing race in the Basque Country.
In the last decade, we have witnessed the birth and spread of the so-called Semantic Web. From it... more In the last decade, we have witnessed the birth and spread of the so-called Semantic Web. From its initial proposal by Tim Berners-Lee , to the latest trends and initiatives, such as Linked Data and DBpedia, the Semantic Web is progressively changing the landscape of the World Wide Web (WWW) through the use and adoption of the different semantic technologies that have come along with it. We can see how, although some of the goals of the Semantic Web have not been reached yet, several well-known and successful applications are already using semantic technologies, such as Google’s Knowledge Graph, Microsoft’s Satori, or Facebook’s Graph Search. In this successful scenario, ontologies have played a crucial role. Defined by Tom Gruber as “an explicit specification of a conceptualization” , ontologies allow to model and capture the semantics of different knowledge domains, providing a means to share definitions, and reach an implicit agreement on the meaning of the published information. Ontologies represent the vocabulary of some domain from a common perspective using a formal language, such as the current standard Web Ontology Language. Thus, with their advance and the development of their associated technologies, we can now explore deeper in the quest for smarter information Systems which exploit the semantics of data.
Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces... more Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM~2013~and SemEval-2014~tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines Latent Semantic Analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of text. Additional wrappers and resources were used to handle task specific challenges that include processing Spanish text, comparing text sequences of different lengths, handling informal words and phrases, and matching words with sense definitions. In the *SEM~2013~task on Semantic Textual Similarity, our best performing system ranked first among the~89~submitted runs. In the SemEval-2014~task on Multilingual Semantic Textual Similarity, we ranked a close second in both the English and Spanish subtasks. In the SemEval-2014~task on Cross--Level Semantic Similarity, we ranked first in Sentence--Phrase, Phrase--Word, and Word--Sense subtasks and second in the Paragraph--Sentence subtask.
The advantage of using semantic reasoners based on Description Logics (DL) for the development of... more The advantage of using semantic reasoners based on Description Logics (DL) for the development of intelligent systems is doubtless: They make easy the optimal management of knowledge (expressed as ontologies). Reasoning is a complex and computationally expensive task traditionally performed on powerful server and desktop computers. However, we should not discard reasoners from being used on mobile devices such as smartphones or tablets, as the increasing number of applications run on wireless environments demands that many intelligent tasks should be performed on mobile devices rather than on desktop or server computers. Although less powerful that their fixed counterparts, mobile devices are becoming more and more capable of running complex tasks, such as DL reasoning. In this paper we introduce the framework of a competition for reasoners on mobile devices, based on the OWL Reasoner Evaluation, with the main goal of promoting the development of reasoners adapted to mobile environm...
The massive spread of mobile computing in our daily lives has attracted a huge community of mobil... more The massive spread of mobile computing in our daily lives has attracted a huge community of mobile application (apps) developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, and knowledge decoupling) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of discovering new knowledge, inferred from the available information.
However, using semantic APIs and reasoners on current mobile devices is not a trivial task. In this paper, we show that the most popular current available Description Logics (DL) reasoners can be used on Android-based devices, and detail the efforts needed to port them to the Android platform. We also analyze the performance of these reasoners on current smartphones/tablets against more than 300 ontologies from the ORE 2013 ontology set, showing that, despite a notable difference with respect to desktop computers, their use is feasible.
3rd International Workshop on OWL Reasoner Evaluation (ORE 2014), 2014
Applications for mobile devices could often show a more in-
telligent behavior by using a semant... more Applications for mobile devices could often show a more in-
telligent behavior by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL 2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL 2 DL and OWL 2 EL reasoners on Android smartphones and tablets.
Location-Based Services (LBSs) are attracting nowadays a great interest, mainly due to the econom... more Location-Based Services (LBSs) are attracting nowadays a great interest, mainly due to the economic value they can provide. So, different applications are being developed for tracking, navigation, advertising, etc., but most of those applications are designed for specific scenarios and goals with implicit knowledge about the application context. However, currently it is a challenge to provide a common framework that allows to manage knowledge obtained from data sent by heterogeneous moving objects (textual data, multimedia data, sensor data, etc.). Moreover, the challenge is even greater considering situations where the system must adapt itself to contexts where the knowledge changes dynamically and in which moving objects can use different underlying wireless technologies and positioning systems.
In this paper we present the system SHERLOCK, that offers a common framework with new functionalities for LBSs. Our system processes user requests continuously to provide up-to-date answers in heterogeneous and dynamic contexts. Ontologies and semantic techniques are used to share knowledge among devices, which enables the system to guide the user selecting the service that best fits his/her needs in the given context. Moreover, the system uses mobile agent technology to carry the processing tasks wherever necessary in the dynamic underlying networks at any time.
2nd International Workshop on OWL Reasoner Evaluation (ORE 2013), 2013
The massive spread of mobile computing in our daily lives
has attracted a huge community of mobi... more The massive spread of mobile computing in our daily lives
has attracted a huge community of mobile apps developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, knowledge decoupling, etc.) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of inferring logical consequences from the knowledge considered. However, using semantic APIs and reasoners on current Android-based devices is not problem-free and, currently, there are no remarkable efforts to enable mobile devices with semantic reasoning capabilities.
In this paper, we analyze whether the most popular current available DL reasoners can be used on Android-based devices. We evaluate the efforts needed to port them to the Android platform, taking into account its limitations, and present some tests to show the performance of these reasoners on current smartphones/tablets.
12th annual international conference on Mobile systems, applications, and services (MobiSys '2014), 2014
FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts t... more FaceBlock takes regular pictures taken by your smartphone or Google Glass as input and converts them into Privacy-Aware Pictures. These pictures are generated by using a combination of Face Detection and Face Recognition algorithms. By using FaceBlock, a user can take a picture of herself and specify her policy/rule regarding pictures taken by others (in this case ‘obscure my face in pictures from strangers’). FaceBlock would automatically generate a mathematical representation of face identifier for this picture. Using Bluetooth, FaceBlock can automatically detect and share this policy with Glass users near by. FaceBlock is a proof of concept implementation of a system that can create Privacy-Aware Pictures using smart devices. The pervasiveness of Privacy-Aware Pictures could be a right step towards balancing privacy needs and comfort afforded by technology. Thus, we can get the best out of Wearable technology without being oblivious about the privacy of those around you.
13th International Semantic Web Conference (ISWC 2014)
Infoboxer uses statistical and semantic knowledge from linked data sources to ease the process of... more Infoboxer uses statistical and semantic knowledge from linked data sources to ease the process of creating Wikipedia infoboxes. It creates dynamic and semantic templates by suggesting attributes common for similar articles and controlling the expected values semantically.
For a Technical Director (TD) in charge of a live broadcasting, selecting the best camera shots a... more For a Technical Director (TD) in charge of a live broadcasting, selecting the best camera shots among the available video sources is a challenging task, even more now that the number of cameras (some of them mobile, or attached to moving objects) in the broadcasting of sport events is increasing. So, the TD needs to manage a great amount of continuously changing information to quickly select the camera whose view should be broadcasted. Besides, the better the decisions made by the TD, the more interesting the content for the audience. Therefore, the development of systems that could help the TD with the selection of camera views is demanded by broadcasting organizations.
In this paper, we present the system MultiCAMBA that helps TDs in the live broadcasting task by allowing them to indicate in run-time their interest in certain kind of shots, and the system will show the cameras that are able to provide them. To achieve this task, the system manages location-dependent queries generated according to the interests of the TD. Moreover, to avoid the use of costly on line real-image processing techniques over the camera views, such real camera views are recreated in a 3D engine by using the information contained in a 3D model of the scenario. This model is updated continuously with real-time data retrieved from the real objects and cameras in the scenario. In this way, the system extracts high-level semantic features of 2D projections of the 3D reconstruction of the camera views. We present a prototype of the system and experimental results that show the feasibility of our proposal.
8th International Workshop on Semantic Evaluation (SemEval 2014)
ABSTRACT We describe UMBC's systems developed for the SemEval 2014 tasks on Multi-lingual... more ABSTRACT We describe UMBC's systems developed for the SemEval 2014 tasks on Multi-lingual Semantic Textual Similarity (Task 10) and Cross-Level Semantic Similarity (Task 3). Our best submission in the Multilingual task ranked second in both English and Spanish subtasks using an unsupervised approach. Our best sys-tems for Cross-Level task ranked second in Paragraph-Sentence and first in both Sentence-Phrase and Word-Sense subtask. The system ranked first for the Phrase-Word subtask but was not included in the official results due to a late submission.
2nd International Workshop on Society, Privacy and the Semantic Web – Policy and Technology (PrivOn 2014)
Wearable computing devices like Google Glass are at the forefront of technological evolution in s... more Wearable computing devices like Google Glass are at the forefront of technological evolution in smart devices. The ubiquitous and oblivious nature of photography using these devices has made people concerned about their privacy in private and public settings. The FaceBlock project protects the privacy of people around Glass users by making pictures taken by the latter, Privacy-Aware. Through sharing of privacy policies, users can choose whether or not to be included in pictures. However, the current privacy model of FaceBlock only permits simple constraints such as allow versus disallow pictures. In this paper, we present an extended context-aware privacy model represented using OWL ontologies and SWRL rules. We also describe use cases of how this model can help FaceBlock to generate Privacy-Aware Pictures depending on context and privacy needs of the user.
3rd International Workshop on OWL Reasoner Evaluation (ORE 2014)
Applications for mobile devices could often show a more intelligent behavior by using a semantic ... more Applications for mobile devices could often show a more intelligent behavior by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL 2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL 2 DL and OWL 2 EL reasoners on Android smartphones and tablets.
22nd International World Wide Web Conference (WWW 2013)
Nowadays people are exposed to huge amounts of information that are generated continuously. Howev... more Nowadays people are exposed to huge amounts of information that are generated continuously. However, current mobile applications, Web pages, and Location-Based Services (LBSs) are designed for specific scenarios and goals. In this demo we show the system SHERLOCK, which searches and shares up-to-date knowledge from nearby devices to relieve the user from knowing and managing such knowledge directly. Besides, the system guides the user in the process of selecting the service that best fits his/her needs in the given context.
8th International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous 2011), 2011
In this demo paper we present MultiCAMBA (Multi-CAMera Broadcasting Assistant), a context– and lo... more In this demo paper we present MultiCAMBA (Multi-CAMera Broadcasting Assistant), a context– and location–aware system that, using a 3D model updated continuously with real-time data retrieved from the scenario, helps technical directors (TDs) in the live broadcasting task. They can indicate in run-time their interest in certain moving objects or geographic areas, and the system is in charge of selecting the cameras that can provide the kind of view required. To achieve this task, the system continuously recreates the views of the cameras in a 3D scenario, considering possible occlusions among the objects.
The production costs of broadcasting sport events that require tracking moving objects are contin... more The production costs of broadcasting sport events that require tracking moving objects are continuously increasing. Although those events are very demanded by the audience, broadcasting organizations have economical difficulties to afford them. For that reason, they are demanding the development of new professional (software and hardware) equipments that lead to a considerable reduction of the production costs.
In this paper, we present a software system that takes into account these needs. This system allows a technical director to indicate his/her interest about certain moving objects or geographic areas in run-time. The system is in charge of selecting the cameras that can provide the types of views requested on those interesting objects and areas. So, it decreases the human effort needed to produce (create, edit and distribute) audiovisual contents, giving at the same time the opportunity to increase their quality. For this, the system provides a friendly interface to specify requirements and obtain which monitoring video cameras attached to moving or static objects fulfill them, along with a query processor to handle those requests in a continuous and efficient way. We illustrate the feasibility of our system in a specific scenario using real data of a traditional rowing race in the Basque Country.
Uploads
Papers by Roberto Yus
through the use and adoption of the different semantic technologies that have come along with it. We can see how, although some of the goals of the Semantic Web have
not been reached yet, several well-known and successful applications are already using semantic technologies, such as Google’s Knowledge Graph, Microsoft’s Satori, or Facebook’s Graph Search. In this successful scenario, ontologies have played a crucial role. Defined by
Tom Gruber as “an explicit specification of a conceptualization” , ontologies allow to model and capture the semantics of different knowledge domains, providing a means to share definitions, and reach an implicit agreement on the meaning of the published information. Ontologies represent the vocabulary of some domain from a common perspective using a formal language, such as the current standard Web Ontology Language. Thus, with their advance and the development of their associated technologies, we can
now explore deeper in the quest for smarter information Systems which exploit the semantics of data.
However, using semantic APIs and reasoners on current mobile devices is not a trivial task. In this paper, we show that the most popular current available Description Logics (DL) reasoners can be used on Android-based devices, and detail the efforts needed to port them to the Android platform. We also analyze the performance of these reasoners on current smartphones/tablets against more than 300 ontologies from the ORE 2013 ontology set, showing that, despite a notable difference with respect to desktop computers, their use is feasible.
telligent behavior by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL 2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL 2 DL and OWL 2 EL reasoners on Android smartphones and tablets.
In this paper we present the system SHERLOCK, that offers a common framework with new functionalities for LBSs. Our system processes user requests continuously to provide up-to-date answers in heterogeneous and dynamic contexts. Ontologies and semantic techniques are used to share knowledge among devices, which enables the system to guide the user selecting the service that best fits his/her needs in the given context. Moreover, the system uses mobile agent technology to carry the processing tasks wherever necessary in the dynamic underlying networks at any time.
has attracted a huge community of mobile apps developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, knowledge decoupling, etc.) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of inferring logical consequences from the knowledge considered. However, using semantic APIs and reasoners on current Android-based devices is not problem-free and, currently, there are no remarkable efforts to enable mobile devices with semantic reasoning capabilities.
In this paper, we analyze whether the most popular current available DL reasoners can be used on Android-based devices. We evaluate the efforts needed to port them to the Android platform, taking into account its limitations, and present some tests to show the performance of these reasoners on current smartphones/tablets.
In this paper, we present the system MultiCAMBA that helps TDs in the live broadcasting task by allowing them to indicate in run-time their interest in certain kind of shots, and the system will show the cameras that are able to provide them. To achieve this task, the system manages location-dependent queries generated according to the interests of the TD. Moreover, to avoid the use of costly on line real-image processing techniques over the camera views, such real camera views are recreated in a 3D engine by using the information contained in a 3D model of the scenario. This model is updated continuously with real-time data retrieved from the real objects and cameras in the scenario. In this way, the system extracts high-level semantic features of 2D projections of the 3D reconstruction of the camera views. We present a prototype of the system and experimental results that show the feasibility of our proposal.
In this paper, we present a software system that takes into account these needs. This system allows a technical director to indicate his/her interest about certain moving objects or geographic areas in run-time. The system is in charge of selecting the cameras that can provide the types of views requested on those interesting objects and areas. So, it decreases the human effort needed to produce (create, edit and distribute) audiovisual contents, giving at the same time the opportunity to increase their quality. For this, the system provides a friendly interface to specify requirements and obtain which monitoring video cameras attached to moving or static objects fulfill them, along with a query processor to handle those requests in a continuous and efficient way. We illustrate the feasibility of our system in a specific scenario using real data of a traditional rowing race in the Basque Country.
through the use and adoption of the different semantic technologies that have come along with it. We can see how, although some of the goals of the Semantic Web have
not been reached yet, several well-known and successful applications are already using semantic technologies, such as Google’s Knowledge Graph, Microsoft’s Satori, or Facebook’s Graph Search. In this successful scenario, ontologies have played a crucial role. Defined by
Tom Gruber as “an explicit specification of a conceptualization” , ontologies allow to model and capture the semantics of different knowledge domains, providing a means to share definitions, and reach an implicit agreement on the meaning of the published information. Ontologies represent the vocabulary of some domain from a common perspective using a formal language, such as the current standard Web Ontology Language. Thus, with their advance and the development of their associated technologies, we can
now explore deeper in the quest for smarter information Systems which exploit the semantics of data.
However, using semantic APIs and reasoners on current mobile devices is not a trivial task. In this paper, we show that the most popular current available Description Logics (DL) reasoners can be used on Android-based devices, and detail the efforts needed to port them to the Android platform. We also analyze the performance of these reasoners on current smartphones/tablets against more than 300 ontologies from the ORE 2013 ontology set, showing that, despite a notable difference with respect to desktop computers, their use is feasible.
telligent behavior by using a semantic reasoner to discover new knowledge. Unfortunately, using Description Logic reasoners on Android devices is not trivial. In this paper we continue our previous work on investigating the use of semantic reasoners on mobile devices. In particular, we port some new OWL 2 EL reasoners to Android and analyze the results of some experiments measuring the performance of several OWL 2 DL and OWL 2 EL reasoners on Android smartphones and tablets.
In this paper we present the system SHERLOCK, that offers a common framework with new functionalities for LBSs. Our system processes user requests continuously to provide up-to-date answers in heterogeneous and dynamic contexts. Ontologies and semantic techniques are used to share knowledge among devices, which enables the system to guide the user selecting the service that best fits his/her needs in the given context. Moreover, the system uses mobile agent technology to carry the processing tasks wherever necessary in the dynamic underlying networks at any time.
has attracted a huge community of mobile apps developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, knowledge decoupling, etc.) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of inferring logical consequences from the knowledge considered. However, using semantic APIs and reasoners on current Android-based devices is not problem-free and, currently, there are no remarkable efforts to enable mobile devices with semantic reasoning capabilities.
In this paper, we analyze whether the most popular current available DL reasoners can be used on Android-based devices. We evaluate the efforts needed to port them to the Android platform, taking into account its limitations, and present some tests to show the performance of these reasoners on current smartphones/tablets.
In this paper, we present the system MultiCAMBA that helps TDs in the live broadcasting task by allowing them to indicate in run-time their interest in certain kind of shots, and the system will show the cameras that are able to provide them. To achieve this task, the system manages location-dependent queries generated according to the interests of the TD. Moreover, to avoid the use of costly on line real-image processing techniques over the camera views, such real camera views are recreated in a 3D engine by using the information contained in a 3D model of the scenario. This model is updated continuously with real-time data retrieved from the real objects and cameras in the scenario. In this way, the system extracts high-level semantic features of 2D projections of the 3D reconstruction of the camera views. We present a prototype of the system and experimental results that show the feasibility of our proposal.
In this paper, we present a software system that takes into account these needs. This system allows a technical director to indicate his/her interest about certain moving objects or geographic areas in run-time. The system is in charge of selecting the cameras that can provide the types of views requested on those interesting objects and areas. So, it decreases the human effort needed to produce (create, edit and distribute) audiovisual contents, giving at the same time the opportunity to increase their quality. For this, the system provides a friendly interface to specify requirements and obtain which monitoring video cameras attached to moving or static objects fulfill them, along with a query processor to handle those requests in a continuous and efficient way. We illustrate the feasibility of our system in a specific scenario using real data of a traditional rowing race in the Basque Country.