Catherine Faron Zucker

The KGRAM Abstract Machine for Knowledge Graph Querying

In this paper we present the KGRAM abstract machine dedicated to querying knowledge graphs. It is the result of an abstraction process we performed to reach a generic solution to the problem of querying graphs in various models. We identified high level abstract primitives that constitute the expressions of the query language and the interfaces of KGRAM for both its data structures and its operations.

KGRAM Versatile Inference and Query Engine for the Web of Linked Data

Querying and linking distributed and heterogeneous databases is increasingly needed, as plentiful data resources are published over the Web. This work describes the design of a versatile query system named KGRAM that supports (i) multiple query languages among which the SPARQL 1.1 standard, (ii) federation of multiple heterogeneous and distributed data sources, and (iii) adaptability to various data manipulation use cases. KGRAM provides abstractions for both the query language and the data model, thus delivering unifying reasoning mechanisms. It is implemented as a modular software suite to ease architecting and deploying dedicated data manipulation platforms. Its design integrates optimization concerns to deliver high query performance. Both KGRAMs software versatility and performance are evaluated.

Empirical study on overlapping community detection in question and answer sites

In many social networks, people interact based on their interests. Community detection algorithms are then useful to reveal the sub-structures of a network and help us find interest groups. Identifying these social communities can bring benefit to understanding and predicting users behaviors. However, for some kind of online community sites such as question-and-answer (Q&A) sites or forums, there is no friendship based social network structure, which means people are not aware who they are in contact with. Therefore, many traditional community detection techniques do not apply directly. In this paper, we propose an empirical approach for extracting data from Q&A sites suitable to apply community detection methods. Then we compare three kinds of community detection methods we applied on a dataset extracted from the popular Q&A site StackOverflow. We analyze and comment the results of each method.

Dynamically Time-Capped Possibilistic Testing of SubClassOf Axioms Against RDF Data to Enrich Schemas

Axiom scoring is a critical task both for the automatic enrichment/learning and for the automatic validation of knowledge bases and ontologies. We designed and developed an axiom scoring heuristic based on possibility theory, which aims at overcoming some limitations of scoring heuristics based on statistical inference and taking into account the open-world assumption of the linked data on the Web. Since computing the possibilistic score can be computationally quite heavy for some candidate axioms, we propose a method based on time capping to alleviate the computation of the heuristic without giving up the precision of the scores. We evaluate our proposal by applying it to the problem of testing SubClassOf axioms against the DBpedia RDF dataset.

Studying the History of Pre-modern Zoology by Extracting Linked Zoological Data from Mediaeval Texts and Reasoning on It

In this paper we first present the international multidisciplinary research network Zoomathia, which aims at studying the transmission of zoological knowledge from Antiquity to Middle Ages through varied resources, and considers especially textual information, including compilation literature such as encyclopaedias. We then present a preliminary work in the context of Zoomathia consisting in i extracting pertinent knowledge from mediaeval texts using Natural Language Processing NLP methods, ii semantically enriching semi-structured zoological data and publishing it as an RDF dataset and its vocabulary, linked to other relevant Linked Data sources, and iii reasoning on this linked RDF data to help epistemologists, historians and philologists in their analysis of these ancient texts. This paper is an extended and updated version ofi¾ź[13].

Answering N-Relation Natural Language Questions in the Commercial Domain

This paper presents SynchroBot, a Natural Language Question Answering system in the Commercial Domain. It relies on an RDF dataset and an RDFS ontology that we have developed for the commercial domain of the mobile phone industry. We propose an approach to understand and interpret natural language questions, based on the use of regular expressions to identify both the properties connecting entities, and their values. These regex are automatically learned from a subset of our dataset with a genetic algorithm.