Search

Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database and World-Wide-Web to reason on semantics-augmented data streams, thus a way to answering questions on real time events. However existing approaches do not consider stream change diagnosis i.e., identification of the nature and cause of changes, where explaining the logical connection of knowledge and inferring insight on time changing events are the main challenges. We exploit the Description Logics (DL)-based semantics of streams to tackle these challenges. Based on an analysis of stream behavior through change and inconsistency over DL axioms, we tackled change diagnosis by determining and constructing a comprehensive view on potential causes of inconsistencies. We report a large-scale evaluation of our approach in the context of live stream data from Dublin City Council.

International audience; Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. We propose and evaluate a method, including a dataset, to benchmark explanation methods on the task of explainable link prediction using RGCNs.

International audience; Prototype networks (Li et al. 2018) provide explanations to users using a prototype vector; that is, a vector learned by the network representing a "typical" observation. In this work, we propose an approach that identifies relevant features in the input space used by the Prototype network. We find however that empirical evaluation of explanation quality is difficult without ground truth explanations. We include a discussion about developing methods for generating explanations, identifying when one explanation method is preferable to another, and the complications that arise when measuring explanation quality.

International audience; Relational Graph Convolutional Networks (RGCNs) identify relationships within a Knowledge Graph to learn real-valued embeddings for each node and edge. Recently, researchers have proposed explanation methods to interpret the predictions of these blackbox models. However, comparisons across explanation methods for link prediction remains difficult, as there is neither a method nor dataset to compare explanations against. Furthermore, there exists no standard evaluation metric to identify when one explanation method is preferable to the other. In this paper, we leverage linked data to propose a method, including two datasets (Royalty-20k, and Royalty-30k), to benchmark explanation methods on the task of explainable link prediction using Graph Neural Networks. In particular, we rely on the Semantic Web to construct explanations, ensuring that each predictable triple has an associated set of triples providing a ground truth explanation. Additionally, we propose the use of a scoring metric for empirically evaluating explanation methods, allowing for a quantitative comparison. We benchmark these datasets on state-of-the-art link prediction explanation methods using the defined scoring metric, and quantify the different types of errors made with respect to both data and semantics. CCS CONCEPTS • Mathematics of computing → Computing most probable explanation; • Computing methodologies → Knowledge representation and reasoning; Neural networks.

International audience; Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms, or explanation methods, have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. In this paper, we introduce a method, including a dataset (FrenchRoyalty-200k), to benchmark explanation methods on the task of link prediction on KGs, when there are multiple explanations to consider. We conduct a user experiment, where users score each possible ground truth explanation based on their understanding of the explanation. We propose the use of several scoring metrics, using relevance weights derived from user scores for each predicted explanation. Lastly, we benchmark this dataset on state-of-the-art explanation methods for link prediction using the proposed scoring metrics.

Relational Graph Convolutional Networks (RGCNs) are commonly used on Knowledge Graphs (KGs) to perform black box link prediction. Several algorithms have been proposed to explain their predictions. Evaluating performance of explanation methods for link prediction is difficult without ground truth explanations. Furthermore, there can be multiple explanations for a given prediction in a KG. No dataset exists where observations have multiple ground truth explanations to compare against. Additionally, no standard scoring metrics exist to compare predicted explanations against multiple ground truth explanations. We propose and evaluate a method, including a dataset, to benchmark explanation methods on the task of explainable link prediction using RGCNs.

Flight delays impact airlines, airports and passengers. Delay prediction is crucial during the decision-making process for all players in commercial aviation, and in particular for airlines to meet their on-time performance objectives. Although many machine learning approaches have been experimented with, they fail in (i) predicting delays in minutes with low errors (less than 15 minutes), (ii) being applied to small carriers i.e., low cost companies characterized by a small amount of data. This work presents a Long Short-Term Memory (LSTM) approach to predicting flight delay, modeled as a sequence of flights across multiple airports for a particular aircraft throughout the day. We then suggest a transfer learning approach between heterogeneous feature spaces to train a prediction model for a given smaller airline using the data from another larger airline. Our approach is demonstrated to be robust and accurate for low cost airlines in Europe.

This paper proposes a congestion balancing parking guidance system that suggests to a driver a sequence of streets to follow around the desired destination with the aim to reduce the total distance that is travelled while searching for a free parking spot. The system requires only limited infrastructure information, and neither requires parking spaces to be instrumented, nor vehicles to communicate with each other. Specifically, the system utilizes parking vacancy information on each street. The system also accounts for the added cost of not finding a free space, which is typically expressed as the additional distance that needs to be travelled to find an available parking spot. To avoid local congestion, different drivers respond to different suggestions based on a probability distribution that considers the total distance that needs to be travelled. A mobility simulator is used to model the searching behaviors of vehicles for parking spaces with and without the smart parking algorithm and experimental results are provided using the road network of the city of Dublin, Ireland.

Stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. However, efforts to understand whether knowledge representation and reasoning are useful for addressing concept drift 1 , one of the core challenges from the stream learning community, particularly those due to dramatic changes in knowledge, have been limited and scattered. In this work, we propose to study the problem in the context of the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with an ontological schema. A fundamental challenge is to understand what knowledge should be encoded and how it can be integrated with stream learning methods. To address this, we show that at least three levels of knowledge encoded in ontology streams are needed to deal with concept drifts: (i) existence of novel knowledge gained from stream dynamics, (ii) significance of knowledge change and evolution, and (iii) (in)consistency of knowledge evolution. We propose an approach to encoding such knowledge via schema-enabled knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on supervised classification tasks. Our main findings are that: (i) It is possible to develop a general purpose framework to address concept drifts in ontology streams by coupling any machine learning classification algorithms with our proposed schema-enabled knowledge graph embeddings method; (ii) Our proposed method is robust to significant concept drift (up to 51% of stream update ratio) and out-performs state of the art methods with 12% to 35% improvement on the Macro-F1 score in the tested scenarios; (iii) Only a small part of the ontological entailment (less than 20%) play an important role in determining the consistency between two snapshots; (iv) Predictions with consistent models outperform those with inconsistent models by over 300% in the two use cases. Our findings could help future work on applications of stream learning, such as autonomous driving, which demand high accuracy of stream learning in the presence of sudden and disruptive changes.

International audience; The development of theory, frameworks and tools for Explainable AI (XAI) is a very active area of research these days, and articulating any kind of coherence on a vision and challenges is itself a challenge. At least two sometimes complementary and colliding threads have emerged. The first focuses on the development of pragmatic tools for increasing the transparency of automatically learned prediction models, as for instance by deep or reinforcement learning. The second is aimed at anticipating the negative impact of opaque models with the desire to regulate or control impactful consequences of incorrect predictions, especially in sensitive areas like medicine and law. The formulation of methods to augment the construction of predictive models with domain knowledge can provide support for producing human understandable explanations for predictions. This runs in parallel with AI regulatory concerns, like the European Union General Data Protection Regulation, which sets standards for the production of explanations from automated or semi-automated decision making. Despite the fact that all this research activity is the growing acknowledgement that the topic of explainability is essential, it is important to recall that it is also among the oldest fields of computer science. In fact, early AI was re-traceable, interpretable, thus understandable by and explainable to humans. The goal of this research is to articulate the big picture ideas and their role in advancing the development of XAI systems, to acknowledge their historical roots, and to emphasise the biggest challenges to moving forward.

Zero-shot learning (ZSL) is a popular research problem that aims at predicting for those classes that have never appeared in the training stage by utilizing the inter-class relationship with some side information. In this study, we propose to model the compositional and expressive semantics of class labels by an OWL (Web Ontology Language) ontology, and further develop a new ZSL framework with ontology embedding. The effectiveness has been verified by some primary experiments on animal image classification and visual question answering., Accepted by KR 2020 - 17th International Conference on Principles of Knowledge Representation and Reasoning

International audience; Global business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach $1.6 Trillion by 2020 according to the Global Business Travel Association, the world's premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimising spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organisations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191, 346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses.

This paper presents a reasoning system deployed for supporting the maintenance of IT devices in use by a leading broadcasting and cable television company in North America. We describe a reasoning engine pipeline relying on semantic data representation and some machine learning approaches such as clustering. The engine derives problems on a telecommunication network from a textual description and uses structured historical data of problems, error codes and proposed solutions to prescribe potential solutions. The engine is capable of proposing solutions to unseen problems by using analogical reasoning on structured representations. When a problem happens on the network or more precisely on one of the devices, these devices generate error codes. We addressed two scenarios; (i) we assumed that the list of error codes that we captured is complete, (ii) we assumed, more realistically, that this list is incomplete. In the first case, we suggested solutions for seen and new problems and reported results on real data. In the second case, we proposed a method to infer the complete list of errors, tested that method on synthetic data and showed results with high accuracy. Although both scenarios are in-use, the first scenario is more usual than the second one, but both need to be considered.

Visual Question Answering (VQA) is a challenging task that has received increasing attention from both the computer vision and the natural language processing communities. A VQA model combines visual and textual features in order to answer questions grounded in an image. Current works in VQA focus on questions which are answerable by direct analysis of the question and image alone. We present a concept-aware algorithm, ConceptBert, for questions which require common sense, or basic factual knowledge from external structured content. Given an image and a question in natural language, ConceptBert requires visual elements of the image and a Knowledge Graph (KG) to infer the correct answer. We introduce a multi-modal representation which learns a joint Concept-Vision-Language embedding inspired by the popular BERT architecture. We exploit ConceptNet KG for encoding the common sense knowledge and evaluate our methodology on the Outside Knowledge-VQA (OK-VQA) and VQA datasets.

Visual Question Answering (VQA) remains algorithmically challenging while it is effortless for humans. Humans combine visual observations with general and commonsense knowledge to answer questions about a given image. In this paper, we address the problem of incorporating general knowledge into VQA models while leveraging the visual information. We propose a model that captures the interactions between objects in a visual scene and entities in an external knowledge source. Our model is a graph-based approach that combines scene graphs with concept graphs, which learns a question-adaptive graph representation of related knowledge instances. We use Graph Attention Networks to set higher importance to key knowledge instances that are mostly relevant to each question. We exploit ConceptNet as the source of general knowledge and evaluate the performance of our model on the challenging OK-VQA dataset.

Transfer learning aims at building robust prediction models by transferring knowledge gained from one problem to another. In the semantic Web, learning tasks are enhanced with semantic representations. We exploit their semantics to augment transfer learning by dealing with when to transfer with semantic measurements and what to transfer with semantic embeddings. We further present a general framework that integrates the above measurements and embeddings with existing transfer learning algorithms for higher performance. It has demonstrated to be robust in two real-world applications: bus delay forecasting and air quality forecasting.

Feature selection is a crucial step in the conception of Machine Learning models, which is often performed via datadriven approaches that overlook the possibility of tapping into the human decision-making of the model’s designers and users. We present a human-in-the-loop framework that interacts with domain experts by collecting their feedback regarding the variables (of few samples) they evaluate as the most relevant for the task at hand. Such information can be modeled via Reinforcement Learning to derive a per-example feature selection method that tries to minimize the model’s loss function by focusing on the most pertinent variables from a human perspective. We report results on a proof-of-concept image classification dataset and on a real-world risk classification task in which the model successfully incorporated feedback from experts to improve its accuracy.

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. As data is evolving on a temporal basis, its underlying knowledge is subject to many challenges. Concept drift, 1 as one of core challenge from the stream learning community, is described as changes of statistical properties of the data over time, causing most of machine learning models to be less accurate as changes over time are in unforeseen ways. This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with ontological vocabulary. In particular we exploit three levels of knowledge encoded in ontology streams to deal with concept drifts: i) existence of novel knowledge gained from stream dynamics, ii) significance of knowledge change and evolution, and iii) (in)consistency of knowledge evolution. Such knowledge is encoded as knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on classification tasks of supervised learning. Key contributions of the study include: (i) an effective knowledge graph embedding approach for stream ontologies, and (ii) a generic consistent prediction framework with integrated knowledge graph embeddings for dealing with concept drifts. The experiments have shown that our approach provides accurate predictions towards air quality in Beijing and bus delay in Dublin with real world ontology streams.

Most service composition approaches rely on top-down decomposition of a problem and AI-style planning to assemble service components into a meaningful whole, impeding reuse and flexibility. In this article, we propose an approach that starts from declarative knowledge about the semantics of individual service components and algorithmically constructs a full-blown service orchestration process that supports sequence, choice, and parallelism. The output of our algorithm can be mapped directly into a number of service orchestration languages such as OWL-S and BPEL. The approach consists of two steps. First, semantic links specifying data dependencies among the services are derived and organized in a flexible network. Second, based on a user request indicating the desired outcomes from the composition, an executable composition is constructed from the network that satisfies the dependencies. The approach is unique in producing complex compositions out of semantic links between services in a flexible way. It also allows reusing knowledge about semantic dependencies in the network to generate new compositions through new requests and modification of services at runtime. The approach has been implemented in a prototype that outperforms related composition prototypes in experiments.

The current development towards the Internet of Things introduces the need for more flexibility in stream processing. To counter these challenges, the authors propose elastic stream processing for distributed environments, building on top of cloud computing and allowing a scalable and more flexible solution compared to traditional approaches.

The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities — Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.

Part 5: MAKE Explainable AI; International audience; Explainable AI is not a new field. Since at least the early exploitation of C.S. Pierce's abductive reasoning in expert systems of the 1980s, there were reasoning architectures to support an explanation function for complex AI systems, including applications in medical diagnosis , complex multi-component design, and reasoning about the real world. So explainability is at least as old as early AI, and a natural consequence of the design of AI systems. While early expert systems consisted of handcrafted knowledge bases that enabled reasoning over narrowly well-defined domains (e.g., INTERNIST, MYCIN), such systems had no learning capabilities and had only primitive uncertainty handling. But the evolution of formal reasoning architectures to incorporate principled probabilistic reasoning helped address the capture and use of uncertain knowledge. There has been recent and relatively rapid success of AI/machine learning solutions arises from neural network architectures. A new generation of neural methods now scale to exploit the practical applicability of statistical and algebraic learning approaches in arbitrarily high dimensional spaces. But despite their huge successes, largely in problems which can be cast as classification problems, their effectiveness is still limited by their un-debuggability, and their inability to "explain" their decisions in a human understandable and reconstructable way. So while AlphaGo or DeepStack can crush the best humans at Go or Poker, neither program has any internal model of its task; its representations defy interpretation by humans, there is no mechanism to explain their actions and behaviour, and furthermore, there is no obvious instructional value... the high performance systems can not help humans improve. Even when we understand the underlying mathematical scaffolding of current machine learning architectures, it is often impossible to get insight into the internal working of the models; we need explicit modeling and reasoning tools to explain how and why a result was achieved. We also know that a significant challenge for future AI is contextual adaptation, i.e., systems that incrementally help to construct explanatory models for solving real-world problems. Here it would be beneficial not to exclude human expertise, but to augment human intelligence with artificial intelligence.

International audience; The process of managing risks of client contracts is manual and resource-consuming, particularly so for Fortune 500 companies. As an example, Accenture assesses the risk of eighty thousand contracts every year. For each contract, different types of data will be consolidated from many sources and used to compute its risk tier. For high-risk tier contracts, a Quality Assurance Director (QAD) is assigned to mitigate or even prevent the risk. The QAD gathers and selects the recommended actions during regular portfolio review meetings to enable leadership to take the appropriate actions. In this paper, we propose to automatically personalize and contextualize actions to improve the efficacy. Our approach integrates enterprise and external data into a knowledge graph and interprets actions based on QADs' profiles through semantic reasoning over this knowledge graph. User studies showed that QADs could efficiently select actions that better mitigate the risk than the existing approach.

International audience; Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic em-beddings. The experiments show accurate prediction with data from Dublin and Beijing.

We present SPUD , a semantic environment for cataloging, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for the incremental and continuous integration of static and streaming data, based on Semantic Web technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogeneous datasets coming from distributed data sources and spanning multiple domains.

Knowledge discovery, as an area focusing upon methodologies for extracting knowledge through deduction (a priori) or from data (a posteriori), has been largely studied in Database and Artificial Intelligence. Deductive reasoning such as logic reasoning gains logically knowledge from pre-established (certain) knowledge statements, while inductive inference such as data mining or learning discovers knowledge by generalising from initial information. While deductive reasoning and inductive learning are conceptually addressing knowledge discovery problems from different perspectives, they are inference techniques that nicely complement each other in real-world applications. In this chapter we will present how techniques from machine learning and reasoning can be reconciled and integrated to address large scale problems in the context of (i) transportation in cities of Bologna, Dublin, Miami, Rio and (ii) spend optimisation in finance.

Nowadays web users have clearly expressed their wishes to receive and interact with personalized services directly. However, existing approaches, largely syntactic content-based, fail to provide robust, accurate and useful personalized services to its users. Towards such an issue, the semantic web provides technologies to annotate and match services’ descriptions with users’ features, interests and preferences, thus allowing for more efficient access to services and more generally information. The aim of our work, part of service personalization, is on automated instantiation of services which is crucial for advanced usability i.e., how to prepare and present services ready to be executed while limiting useless interactions with users? We introduce the constructive Description Logics reasoning join and couple it with concept abduction to i) identify useful parts of users profiles that satisfy services requirements and ii) compute the description required by a service to be executed but not provided by users profiles.

Ranking and optimization of web service compositions represent challenging areas of research with significant implications for the realization of the “Web of Services” vision. “Semantic web services” use formal semantic descriptions of web service functionality and interface to enable automated reasoning over web service compositions. To judge the quality of the overall composition, for example, we can start by calculating the semantic similarities between outputs and inputs of connected constituent services, and aggregate these values into a measure of semantic quality for the composition. This paper takes a specific interest in combining semantic and nonfunctional criteria such as quality of service (QoS) to evaluate quality in web services composition. It proposes a novel and extensible model balancing the new dimension of semantic quality (as a functional quality metric) with a QoS metric, and using them together as ranking and optimization criteria. It also demonstrates the utility of Genetic Algorithms to allow optimization within the context of a large number of services foreseen by the “Web of Services” vision. We test the performance of the overall approach using a set of simulation experiments, and discuss its advantages and weaknesses.

Web service composition enhanced by semantic technologies is currently one of the most hyped and addressed issues in Service Oriented Computing. This work focuses on both i) conditional composition, i.e., how to automate the composition of services that produces non-deterministic outcomes, and ii) consistent connections between web services (also known as data flows) in the composition. To this end, we suggest the joint use of semantic links and causal laws, respectively, in the area of Description Logics and AI planning. In more detail, an augmented and adapted version of the logic programming language Golog, i.e., sslGolog, is presented as a natural formalism not only for reasoning about the latter semantic links and causal laws, but also for automatically composing non-deterministic services. sslGolog operates as an offline interpreter that supports not only binary but also n-ary sensing services (i.e., services conditioned on their possible output parameters) to achieve conditional compositions of services. Lastly, sslGolog has been implemented and tested in the context of Telecommunication scenarios.

Most of the work on automated web service composition has focused so far on composition of stateful web services. This level of composition so-called "Process Level" considers web services with their internal and complex behaviors. At process level formal models such as State Transition Systems (STS from now) or Interface Automata are the most appropriate models to represent the internal behavior of stateful web services. However such models focus only on semantics of their behaviors and unfortunately not on semantics of actions and their parameters. In this paper, we suggest to extend the STS model, by following the WSMO based annotation for Abstract State Machine. This semantic enhancement of STS so called S2 TS will enable to model semantics of internal behaviors and semantics of their actions together with their input and output parameters. Secondly, we will focus on automated generation of data flow (or the process to perform automated assignments between parameter of services involved in a composition). Thus we do not restrict to assignments of exact parameters (which is practically never used in industrial scenario) but extend assignments to semantically close parameters (e.g., through a subsumption matching) in the same ontology. Our system is implemented and interacting with web services dedicated to Telecommunication scenarios. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach.

International audience; Automated composition of Web services or the process of forming new value-added Web services is one of the most promising challenges facing the Semantic Web today. Semantics enables Web service to describe capabilities together with their processes, hence one of the key elements for the automated composition of Web services. In this paper, we focus on the functional level of Web services i.e. services are described according to some input, output parameters semantically enhanced by concepts in a domain ontology. Web service composition is then viewed as a composition of semantic links wherein the latter links refer to semantic matchmaking between Web service parameters (i.e. outputs and inputs) in order to model their connection and interaction. The key idea is that the matchmaking enables, at run time, finding semantic compatibilities among independently defined Web service descriptions. By considering such a level of composition, a formal model to perform the automated composition of Web services i.e. Semantic Link Matrix, is introduced. The latter model is required as a starting point to apply problem-solving techniques such as regression (or progression)-based search for Web service composition. The model supports a semantic context in order to find correct, complete, consistent and robust plans as solutions. In this paper, an innovative and formal model for an AI (Artificial Intelligence) planning-oriented composition is presented. Our system is implemented and interacting with Web services which are dedicated to Telecom scenarios. The preliminary evaluation results showed high efficiency and effectiveness of the proposed approach. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0218843009001975

Les avancees recentes des technologies « NTIC » (Nouvelles Technologies de l’Information et des Telecommunications) ont (re)-ouvert la voie a de nouvelles solutions pour le developpement d’architectures de traitement distribue dont l’impact industriel et commercial semble cette fois tres prometteur. En effet, la conjonction de la maturite des architectures logicielles a composants distribues, la tres forte penetration des technologies du Web et enfin la forte pression de l’economie mondiale ont conduit a une irresistible proliferation de composants physiques ou logiciels autonomes disponibles et distribues sur le web. Le W3C les definit comme des « services web » : « le service web est un systeme logiciel concu pour rendre interoperable la communication de Machine a Machine connectees en reseaux. Il est decrit par une interface (WSDL) et peut interagir avec d’autres systemes par l’envoi de messages (SOAP) typiquement vehicules par un protocole HTTP et une representation syntaxique XML conjointement a d’autres standards du Web ». Une tres importante fonctionnalite de ces systemes a base de services web est leur capacite a offrir des services composites construits a partir de ces composants physiques ou logiciels autonomes et reutilisables, formant une nouvelle infrastructure de traitement informatique distribue sur le Web. Profitant de l’infrastructure du web semantique, les services web dits « semantiques » ouvrent des perspectives d’avancees cles dans le traitement d’information couvrant un large spectre d’applications comme les services commerciaux (e-Enterprise, e-Business), la recherche scientifique (ex. grille de calcul « e-Science »), l’education (e-Learning) et enfin les services aux citoyens (e-Government, e-Democracy). Nous presentons dans ce tutoriel (en deux parties) un etat de l’art synthetique des langages et technologies associes aux services web semantiques, un panorama detaille des raisonnements de decouverte existants et des solutions majeures pour la composition de services web, pour terminer sur des exemples d’applications et une feuille de route.

Recent advances in networks, information and computation grids, and WWW have resulted in the proliferation of a multitude of physically distributed and autonomously developed Web services. This second part presents two key scenarios in B2B and B2C, the central function of services composition and orchestration, the languages and prominent technologies and lastly an industry and research roadmap.

Scalable means for the search of relevant web services are essential for the development of intelligent service-based applications in the future Internet. Key idea of semantic web services is to enable such applications to perform a high-precision search and automated composition of services based on formal ontology-based representations of service semantics. In this paper, we briefly survey the state of the art of semantic web service search.

Many various types of sensors coming from different complex devices collect data from a city. Their underlying data representation follows specific manufacturer specifications that have possibly incomplete descriptions (in ontology) alignments. This paper addresses the problem of determining accurate and complete matching of ontologies given some common descriptions and their pre-determined high level alignments. In this context the problem of ontology matching consists of automatically determining all matching given the latter alignments, and manually verifying the matching results. Especially for applications where it is crucial that ontologies are matched correctly the latter can turn into a very time-consuming task for the user. This paper tackles this challenge and addresses the problem of computing the minimum number of user inputs needed to verify all matchings. We show how to represent this problem as a reasoning problem over a bipartite graph and how to encode it over pseudo Boolean constraints. Experiments show that our approach can be successfully applied to real-world data sets.

Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.

OWL 2 EL is one of the tractable profiles of the Web Ontology Language OWL which is a W3C-recommended standard. OWL 2 EL provides sufficient expressivity to model large biomedical ontologies as well as streaming data such as traffic, while at the same time allows for efficient reasoning services. Existing reasoners for OWL 2 EL, however, use only a single machine and are thus constrained by memory and computational power. At the same time, the automated generation of ontological information from streaming data and text can lead to very large ontologies which can exceed the capacities of these reasoners. We thus describe a distributed reasoning system that scales well using a cluster of commodity machines. We also apply our system to a use case on city traffic data and show that it can handle volumes which cannot be handled by current single machine reasoners.

Data captured in OWL ontologies is generally considered to be more prone to changes than the schema in many situations. Such changes often necessitate consistency checking over the resulting ontologies in order to maintain coherent knowledge, specifically in dynamic settings. In this paper, we present an approach to check the consistency over an evolving ontology resulting from data insertions and deletions, given by some expressive underlying Description Logic dialect. The approach, assuming an initially consistent ontology, works by syntactically identifying "relevant" and representative parts of the data for the given updates, i.e., the part that may contribute to subsequent consistency checking. Our approach has demonstrated its efficacy in checking consistency over large and real-world ontologies and outperforms existing approaches in several circumstances.

Diagnosis, or the process of identifying the nature and cause of an anomaly in an ontology, has been largely studied by the Semantic Web community. In the context of ontology stream, diagnosis results are not captured by a unique fixed ontology but numerous time-evolving ontologies. Thus any anomaly can be diagnosed by a large number of different explana- tions depending on the version and evolution of the ontology. We address the problems of identifying, representing, exploiting and exploring the evolution of diagnoses representations. Our approach consists in a graph-based representation, which aims at (i) efficiently organizing and linking time-evolving di- agnoses and (ii) being used for scalable exploration. The ex- periments have shown scalable diagnoses exploration in the context of real and live data from Dublin City.

This paper presents STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. We present how semantic diagnosis and predictive reasoning, both using and interpreting semantics of data to deliver useful, accurate and consistent inferences, have been exploited and adapted systematized in an intelligent user interface. Our prototype of semantics-aware traffic analytics and reasoning, experimented in Dublin City Ireland, works and scales efficiently with historical together with real live and heterogeneous stream data.

The Internet of Things is one of the next big changes in which devices, objects, and sensors are getting linked to the semantic web. However, the increasing availability of generated data leads to new integration problems. In this paper we present an architecture and approach that illustrates how semantic sensor networks, semantic web technologies, and reasoning can help in real-world applications to automatically derive complex models for analytics tasks such as prediction and diagnostics. We demonstrate our approach for buildings and their numerous connected sensors and show how our semantic framework allows us to detect and diagnose abnormal building behavior. This can lead to not only an increase of occupant well-being but also to a reduction of energy use. Given that buildings consume 40% of the world's energy use we therefore also make a contribution towards global sustainability. The experimental evaluation shows the benefits of our approach for buildings at IBM's Technology Campus in Dublin.

We present SPUD, a semantic environment for cataloguing, exploring, integrating, understanding, processing and transforming urban information. A series of challenges are identified: namely, the heterogeneity of the domain and the impracticality of a common model, the volume of information and the number of data sets, the requirement for a low entry threshold to the system, the diversity of the input data, in terms of format, syntax and update frequency (streams vs static data), the complex data dependencies and the sensitivity of the information. We propose an approach for incremental and continuous integration of static and streaming data, based on Semantic Web technologies and apply our technology to a traffic diagnosis scenario. We demonstrate our approach through a system operating on real data in Dublin and we show that semantic technologies can be used to obtain business results in an environment with hundreds of heterogenous datasets coming from distributed data sources and spanning multiple domains.

This paper gives a high-level presentation of STAR-CITY, a system supporting semantic traffic analytics and reasoning for city. STAR-CITY, which integrates (human and machine-based) sensor data using variety of formats, velocities and volumes, has been designed to provide insight on historical and real-time traffic conditions, all supporting efficient urban planning. Our system demonstrates how the severity of road traffic congestion can be smoothly analyzed, diagnosed, explored and predicted using semantic web technologies. Our prototype of semantics-aware traffic analytics and reasoning, illustrated and experimented in Dublin Ireland, but also tested in Bologna Italy, Miami USA and Rio Brazil works and scales efficiently with real, historical together with live and heterogeneous stream data. This paper highlights the lessons learned from deploying and using a system in Dublin City based on Semantic Web technologies.

Predictive reasoning, or the problem of estimating future observations given some historical information, is an important inference task for obtaining insight on cities and supporting efficient urban planning. This paper, focusing on transportation, presents how severity of road traffic congestion can be predicted using semantic Web technologies. In particular we present a system which integrates numerous sensors (exposing heterogenous, exogenous and raw data streams such as weather information, road works, city events or incidents) to improve accuracy and consistency of traffic congestion prediction. Our prototype of semantics-aware prediction, being used and experimented currently by traffic controllers in Dublin City Ireland, works efficiently with real, live and heterogeneous stream data. The experiments have shown accurate and consistent prediction of road traffic conditions, main benefits of the semantic encoding.