Integrating heterogeneous pieces of content has traditionally been a tedious process, involving domain experts who manually and carefully repurpose the different types of contents using rigid formats in order to meet the needs of a specific application. The rapid increase in the volume and variety of information being made available today–both by human and machines–makes this fully manual process impractical in many settings. Instead, large organizations are today exploring automated, data-driven approaches to integrate heterogeneous pieces of information.
However, classical information integration techniques are ill-suited to meaningfully integrate dynamic and heterogeneous data originating from many different sources. On one hand, Information Retrieval techniques based on synonym sets, query expansion and pseudo-relevance feedback, though highly scalable and efficient, offer limited abstractions beyond Boolean keyword search; typically, they do not support higher-level processing for example based on declarative queries. Database techniques, on the other hand, offer powerful abstractions to syntactically reformulate complex queries posed against one database into equivalent (or subsumed) queries in a different database. However, they require the a priori definition of a global federated schema and complex mappings (views) between the global schema and each source, thus drastically limiting their dynamicity and scalability in practice.
The goal of this project is to propose an ambitious overhaul of data integration techniques to better match today’s world of information profusion. The main idea behind those techniques revolves around a new integration abstraction flexibly connecting all heterogeneous pieces of information through extremely large, data-driven, probabilistic and heterogeneous graphs. The scientific contribution of this project is divided into three distinct though highly interweaved endeavors: i) the creation of new information extraction and semantic lifting approaches to interconnect unstructured and structured content through novel abstraction layers called Heterogeneous Information Graphs (HIGs), ii) the development of new physical structures aiming at efficiently storing, managing, and retrieving the wealth of information considered by a HIG on sets of commodity machines, and iii) the design of new logical abstractions responsible for exposing and serving the richness of HIGs data to external entities through high-level declarative interfaces and abstractions.
