We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL2QL and RDFS ontologies), and its support for all major relational databases.

/pdf/ontop-answering-sparql-queries-over-relational-databases-3r4toru69s.pdf

Ontop: Answering SPARQL queries over relational databases

Transportation Research Part F: Traffic Psychology and Behaviour

The Semantic Web contains an enormous amount of information in the form of knowledge bases (KB). To make this information available, many question answering (QA) systems over KBs were created in the last years. Building a QA system over KBs is difficult because there are many different challenges to be solved. In order to address these challenges, QA systems generally combine techniques from natural language processing, information retrieval, machine learning and Semantic Web. The aim of this survey is to give an overview of the techniques used in current QA systems over KBs. We present the techniques used by the QA systems which were evaluated on a popular series of benchmarks: Question Answering over Linked Data. Techniques that solve the same task are first grouped together and then described. The advantages and disadvantages are discussed for each technique. This allows a direct comparison of similar techniques. Additionally, we point to techniques that are used over WebQuestions and SimpleQuestions, which are two other popular benchmarks for QA systems.

/pdf/core-techniques-of-question-answering-systems-over-knowledge-ztjne1c44r.pdf

Core techniques of question answering systems over knowledge bases: a survey

We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.

/pdf/ontology-based-data-access-a-survey-46e31grqvo.pdf

Ontology-based data access: a survey

Templates are an important asset for question answering over knowledge graphs, simplifying the semantic parsing of input utterances and generating structured queries for interpretable answers. State-of-the-art methods rely on hand-crafted templates with limited coverage. This paper presents QUINT, a system that automatically learns utterance-query templates solely from user questions paired with their answers. Additionally, QUINT is able to harness language compositionality for answering complex questions without having any templates for the entire question. Experiments with different benchmarks demonstrate the high quality of QUINT.

Automated Template Generation for Question Answering over Knowledge Graphs

Smart traffic analytics in the semantic web with STAR-CITY

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.

/pdf/predicting-severity-of-road-traffic-congestion-using-1j6152h9xf.pdf

Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

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.

STAR-CITY: semantic traffic analytics and reasoning for CITY

With the increasing fuel prices and the pressure towards greener modes of transportation, ridesharing has emerged as an alternative to private car ownership and public transportation. In this paper, we focus on a common destination ridesharing system which is of interest in large organizations such as companies and government offices. Particularly, such organizations are looking at using company owned vehicles to offer a ridesharing service by which employees carpool to work thus leading to several benefits that include decreasing pressure on on-campus parking spaces, lowering localized on-campus congestion, in addition to offering a greener transportation mode while lowering transportation costs for employees. Based on discussions with our industry partners, optimizing the distribution of limited number of company vehicles while insuring robustness against unlikely vehicle unavailability is of critical importance. Thus in this paper, we present a stochastic mixed integer programming model to optimize the allocation of shared vehicles to employees while taking into account the unforeseen event of vehicle unavailability which would require some participants to take own vehicles or rerouting of existing vehicles. Since solving the proposed model to optimality is computationally challenging for problems of large sizes, we also propose a heuristic that is capable of finding good quality solutions in limited computational time. The proposed model and heuristic are tested on several instances of varying sizes showing the computational performance. Finally, a test case based on the city of Rome, Italy is presented and insights related to vehicle distribution and travel time savings are discussed.

Stochastic optimization approach for the car placement problem in ridesharing systems

We present a domain-agnostic system for Question Answering over multiple semi-structured and possibly linked datasets without the need of a training corpus. The system is motivated by an industry use-case where Enterprise Data needs to be combined with a large body of Open Data to fulfill information needs not satisfied by prescribed application data models. Our proposed Question Answering pipeline combines existing components with novel methods to perform, in turn, linguistic analysis of a query, named entity extraction, entity/graph search, fusion and ranking of possible answers. We evaluate QuerioDALI with two open-domain benchmarks and a biomedical one over Linked Open Data sources, and show that our system produces comparable results to systems that require training data and are domain-dependent. In addition, we analyze the current challenges and shortcomings.

Pierpaolo Tommasi

Papers

Smart traffic analytics in the semantic web with STAR-CITY

Predicting Severity of Road Traffic Congestion Using Semantic Web Technologies

STAR-CITY: semantic traffic analytics and reasoning for CITY

Stochastic optimization approach for the car placement problem in ridesharing systems

QuerioDALI: Question Answering Over Dynamic and Linked Knowledge Graphs