Ontology-based data access (OBDA), also known as virtual knowledge graphs (VKG), is a well-established approach to information management that facilitates the access to a (single) relational data source through the mediation of a high-level ontology, and the use of a declarative mapping linking the data layer to the ontology. In order to integrate multiple, possibly distributed and heterogeneous, data sources, in this work we formally introduce an extension of OBDA, called ontology-based data federation (OBDF), by combining OBDA with a data federation layer, which can expose multiple data sources as a single relational database. We discuss opportunities and challenges of OBDF, and provide techniques to deliver efficient query answering in OBDF by exploiting inter-source relations (called data hints) in the federated sources. Such techniques are validated through an extensive experimental evaluation based on the Berlin SPARQL Benchmark.

Linked Open Data (LOD) and social media often contain the representations of the same real-world entities, such as persons and organizations. These representations are increasingly interlinked, making it possible to combine and leverage both LOD and social media data in prediction problems, complementing their relative strengths: while LOD knowledge is highly structured but also scarce and obsolete for some entities, social media data provide real-time updates and increased coverage, albeit being mostly unstructured. In this paper, we investigate the feasibility of using social media data to perform type prediction for entities in a LOD knowledge graph. We discuss how to gather training data for such a task, and how to build an efficient domain-independent vector representation of entities based on social media data. Our experiments on several type prediction tasks using DBpedia and Twitter data show the effectiveness of this representation, both alone and combined with knowledge graph-based features, suggesting its potential for ontology population.

Different well-established NLP tasks contribute to elicit the semantics of entities mentioned in natural language text, such as Named Entity Recognition and Classification (NERC) and Entity Linking (EL). However, combining the outcomes of these tasks may result in NLP annotations --- such as a NERC organization linked by EL to a person --- that are unlikely or contradictory when interpreted in the light of common world knowledge about the entities these annotations refer to. We thus propose a general probabilistic model that explicitly captures the relations between multiple NLP annotations for an entity mention, the ontological entity classes implied by those annotations, and the background ontological knowledge those classes may be consistent with. We use the model to estimate the posterior probability of NLP annotations given their confidences (prior probabilities) and the ontological knowledge, and consequently revise the best annotation choice performed by the NLP tools. In a concrete scenario with two state-of-the-art tools for NERC and EL, we experimentally show on three reference datasets that for these tasks, the joint annotation revision performed by the model consistently improves on the original results of the tools.

Typically, the task of managing the social media presence of a company or a public person is the job of a dedicated social media account manager. While many attempts have been made in recent years to provide more automation to account managers, complete workflow automation has still to be achieved. Pokedem is a social media management application that aims at filling this gap, recommending actions that could be performed by the account manager to increase the popularity of a Twitter account and the engagement of its audience. By casting the problem in the setting of recommendation systems, Pokedem is able to provide account managers with a complete tool for automating their daily activities.

We present SocialLink , a publicly available Linked Open Data dataset that matches social media accounts on Twitter to the corresponding entities in multiple language chapters of DBpedia. By effectively bridging the Twitter social media world and the Linked Open Data cloud, SocialLink enables knowledge transfer between the two: on the one hand, it supports Semantic Web practitioners in better harvesting the vast amounts of valuable, up-to-date information available in Twitter; on the other hand, it permits Social Media researchers to leverage DBpedia data when processing the noisy, semi-structured data of Twitter. SocialLink is automatically updated with periodic releases and the code along with the gold standard dataset used for its training are made available as an open source project.

We present an approach for ontology population from natural language English texts that extracts RDF triples according to FrameBase, a Semantic Web ontology derived from FrameNet. Processing is decoupled in two independently-tunable phases. First, text is processed by several NLP tasks, including Semantic Role Labeling (SRL), whose results are integrated in an RDF graph of mentions, i.e., snippets of text denoting some entity/fact. Then, the mention graph is processed with SPARQL-like rules using a specifically created mapping resource from NomBank/PropBank/FrameNet annotations to FrameBase concepts, producing a knowledge graph whose content is linked to DBpedia and organized around semantic frames, i.e., prototypical descriptions of events and situations. A single RDF/OWL representation is used where each triple is related to the mentions/tools it comes from. We implemented the approach in PIKES, an open source tool that combines two complementary SRL systems and provides a working online demo. We evaluated PIKES on a manually annotated gold standard, assessing precision/recall in (i) populating FrameBase ontology, and (ii) extracting semantic frames modeled after standard predicate models, for comparison with state-of-the-art tools for the Semantic Web. We also evaluated (iii) sampled precision and execution times on a large corpus of 110 K Wikipedia-like pages.

Although the quantity of structured information on the Web and within organizations is increasing, the majority of information remains available only in unstructured form. While different in form, both unstructured and structured information sources provide information about entities in the world and their properties and relations; still, frameworks for their seamless integration have not been deeply investigated. In this paper the authors describe the KnowledgeStore, a scalable, fault-tolerant, and Semantic Web grounded open-source storage system for interlinking structured and unstructured data. They present the concept, design, function and implementation of the system, and report on its concrete usage in three application scenarios within the NewsReader EU project, where it stores and supports the querying of millions of news articles interlinked with millions of RDF triples extracted from text and imported from Linked Open Data sources. The authors report on data population and data retrieval performances of the system measured through a number of experiments, and they also discuss the practical issues and lessons learned from these experiences.

We demonstrate the Search Computing framework for multi-domain queries upon ranked data collected from Web sources. Search Computing answers to queries like "Find a good Jazz concert close to a specified location, a good restaurant and a hotel at walking distance" and fills the gap between generic and domain-specific search engines, by proposing new methods, techniques, interfaces, and tools for building search-based applications spanning multiple data services. The main enabling technology is an execution engine supporting methods for rank-join execution upon ranked data sources, abstracted and wrapped by means of a unifying service model. The demo walks through the interface for formulating multi-domain queries and follows the steps of the query engine that builds the result, with the help of run-time monitors that clearly explain the system's behavior. Once results are extracted, the demonstration shows several approaches for visualizing results and exploring the information space.