Today, the main applications of OBDA SQL) defines a broad collection of languages. Keyvalue can be found in data integration as well as in querying the stores are NOSQL systems adopting the data model of Semantic Web. The interest of OBDA is to allow the users to key-value records (also called JSON records). These records ask queries on high-level ontology vocabularies and to delegate are processed on distributed systems, but also increasingly to algorithms (1) the reformulation of these high-level exchanged on the Web thereby replacing semistructured queries into a set of low-level databases queries, (2) the efficient XML data and many RDF formats (see JSON-LD (Sporny computation of their answers by native data management et al. 2004)). Key-value records are non-first normal forms systems in which data is stored and indexed, and (3) where values are not only atomic (in contrast with relational the combination of these answers in order to obtain the final databases) and nesting is possible (Abiteboul, Hull, answers to the users' query. The advantage of OBDA is and Vianu 1995).

We investigate the problem of learning description logic (DL) ontologies in Angluin et al.’s framework of exact learning via queries posed to an oracle. We consider membership queries of the form “is a tuple a of individuals a certain answer to a data retrieval query q in a given ABox and the unknown target ontology?” and completeness queries of the form “does a hypothesis ontology entail the unknown target ontology?” Given a DL L and a data retrieval query language Q, we study polynomial learnability of ontologies in L using data retrieval queries in Q and provide an almost complete classification for DLs that are fragments of EL with role inclusions and of DL-Lite and for data retrieval queries that range from atomic queries and EL/ELI-instance queries to conjunctive queries. Some results are proved by non-trivial reductions to learning from subsumption examples.

Most description logics (DL) query languages allow instance retrieval from an ABox. However, SPARQL is a schema query language allowing access to the TBox (in addition to the ABox). Moreover, its entailment regimes enable to take into account knowledge inferred from knowledge bases in the query answering process. This provides a new perspective for the containment problem. In this paper, we study the containment of SPARQL queries over OWL EL axioms under entailment. OWL EL is the language used by many large scale ontologies and is based on EL ++ . The main contribution is a novel approach to rewriting queries using SPARQL property paths and the μ-calculus in order to reduce containment test under entailment into validity check in the μ-calculus.

Ontology-based data access (OBDA) is a novel paradigm facilitating access to relational data, realized by linking data sources to an ontology by means of declarative mappings. DL-Lite_R, which is the logic underpinning the W3C ontology language OWL 2 QL and the current language of choice for OBDA, has been designed with the goal of delegating query answering to the underlying database engine, and thus is restricted in expressive power. E.g., it does not allow one to express disjunctive information, and any form of recursion on the data. The aim of this paper is to overcome these limitations of DL-Lite_R, and extend OBDA to more expressive ontology languages, while still leveraging the underlying relational technology for query answering. We achieve this by relying on two well-known mechanisms, namely conservative rewriting and approximation, but significantly extend their practical impact by bringing into the picture the mapping, an essential component of OBDA. Specifically, we develop techniques to rewrite OBDA specifications with an expressive ontology to "equivalent" ones with a DL-Lite_R ontology, if possible, and to approximate them otherwise. We do so by exploiting the high expressive power of the mapping layer to capture part of the domain semantics of rich ontology languages. We have implemented our techniques in the prototype system OntoProx, making use of the state-of-the-art OBDA system Ontop and the query answering system Clipper, and we have shown their feasibility and effectiveness with experiments on synthetic and real-world data.

Commonsense knowledge about part-whole relations (e.g., screen partOf notebook) is important for interpreting user input in web search and question answering, or for object detection in images. Prior work on knowledge base construction has compiled part-whole assertions, but with substantial limitations: i) semantically different kinds of part-whole relations are conflated into a single generic relation, ii) the arguments of a part-whole assertion are merely words with ambiguous meaning, iii) the assertions lack additional attributes like visibility (e.g., a nose is visible but a kidney is not) and cardinality information (e.g., a bird has two legs while a spider eight), iv) limited coverage of only tens of thousands of assertions. This paper presents a new method for automatically acquiring part-whole commonsense from Web contents and image tags at an unprecedented scale, yielding many millions of assertions, while specifically addressing the four shortcomings of prior work. Our method combines pattern-based information extraction methods with logical reasoning. We carefully distinguish different relations: physicalPartOf, memberOf, substanceOf. We consistently map the arguments of all assertions onto WordNet senses, eliminating the ambiguity of word-level assertions. We identify whether the parts can be visually perceived, and infer cardinalities for the assertions. The resulting commonsense knowledge base has very high quality and high coverage, with an accuracy of 89% determined by extensive sampling, and is publicly available.

In this paper we describe the Unified Cybersecurity Ontology (UCO) that is intended to support information integration and cyber situational awareness in cybersecurity systems. The ontology incorporates and integratesheterogeneous data and knowledge schemas from different cybersecurity systems and most commonly usedcybersecurity standards for information sharing and exchange. The UCO ontology has also been mapped to anumber of existing cybersecurity ontologies as well asconcepts in the Linked Open Data cloud (Berners-Lee,Bizer, and Heath 2009). Similar to DBpedia (Auer etal. 2007) which serves as the core for general knowledge in Linked Open Data cloud, we envision UCO toserve as the core for cybersecurity domain, which wouldevolve and grow with the passage of time with additional cybersecurity data sets as they become available.We also present a prototype system and concrete usecases supported by the UCO ontology. To the best of ourknowledge, this is the first cybersecurity ontology thathas been mapped to general world ontologies to support broader and diverse security use cases. We comparethe resulting ontology with previous efforts, discuss itsstrengths and limitations, and describe potential futurework directions.

It is well known that the title of an article impacts how well it is discovered by potential readers and read. With both people and search engines, acting on behalf of people, accessing papers from digital libraries, it is important that the paper titles should promote discovery. In this paper, we investigate the characteristics of titles of AI papers and then propose au- tomatic ways to augment them so that they can be better in- dexed and discovered by users. A user study with researchers shows that they overwhelmingly prefer the augmented titles over the originals for being more helpful.

In this paper, we propose an intelligent dialogue agent for the IoT home. The goal of the proposed system is to efficiently control IoT devices with natural spoken dialogue. This system is made up of the following components: Spoken Language Understanding for analyzing textual input and understanding user intention, Dialogue Management with a State Manager that consists of dialogue policies, Context Manager for understanding the environment, Action Planner responsible for generating a sequence of actions to achieve user intention, Things Manager for observing and controlling IoT devices, and Natural Language Generation that generates natural language from computer-based representation. This system is fully implemented in software and is evaluated in a real IoT home environment.

This paper tackles the problem of endogenous link prediction for knowledge base completion. Knowledge bases can be represented as directed graphs whose nodes correspond to entities and edges to relationships. Previous attempts either consist of powerful systems with high capacity to model complex connectivity patterns, which unfortunately usually end up overfitting on rare relationships, or in approaches that trade capacity for simplicity in order to fairly model all relationships, frequent or not. In this paper, we propose Tatec, a happy medium obtained by complementing a high-capacity model with a simpler one, both pre-trained separately and then combined. We present several variants of this model with different kinds of regularization and combination strategies and show that this approach outperforms existing methods on different types of relationships by achieving state-of-the-art results on four benchmarks of the literature.

Having spent the past 31 years memorizing an astonishing collection of general knowledge, the artificial-intelligence engine created by Doug Lenat is finally ready to go to work. Lenat's creation is Cyc, a knowledge base of semantic information designed to give computers some understanding of how things work in the real world. Cyc has been given many thousands of facts, including lots of information that you wouldn't find in an encyclopedia because it seems self-evident. It knows, for example, that that Sir Isaac Newton is a famous historical figure who is no longer alive. But more important, Cyc also understands that if you let go of an apple it will fall to the ground; that an apple is not bigger than a person; and that a person cannot throw an apple into space.