kno.e.sis - Projects

Semantic Discovery: Discovering Complex Relationships in Semantic Web An NSF Medium ITR project No. 071441 to Wright State University

SemDis is a collaborative project between Kno.e.sis Center at Wright State University (WSU), LSDIS lab at the University of Georgia, Athens (SemDis at UGA), and eBiquity at the University of Maryland, Baltimore County ( SemDis at UMBC).

Project Summary

Research in search techniques was a critical component of the first generation of the Web and has gone from academe to mainstream. A second generation "Semantic Web"will be built by adding semantic annotations that software can understand and from which humans can benefit. Modeling, discovering, and reasoning about complex relationships on the Semantic Web will enable this vision and transform the hunt for documents into a more automated analysis enabled by semantic technology. The beginnings of this shift from search to analysis can be observed in research and industry as users look beyond finding relevant documents based on keywords to finding actionable information leading to decision making and insights. Large-scale semantic annotation of data (domain-independent and domain-specific) is now made possible by cumulative advances in entity identification, automatic classification, taxonomy and ontology development, and metadata extraction. The next frontier, which changes the way we acquire and use knowledge fundamentally, is the automatic identification of complex relationships between entities in semantically annotated data. Instead of a search engine that returns documents containing terms of interest, we envision a system that returns actionable information (with associated sources and supporting evidence) to a user or application. The user interacts with information universe through a hypothesis-driven approach that combines search and inferencing, enabling more complex analysis and deeper insight. The examples in our narrative show that such a capability also greatly enhances the capacity of intelligence analysts to obtain (in time) information leading to a more secure homeland and world.

Our research will focus on the design, prototyping and evaluation of a system, called SemDIS (Semantic Discovery), that supports indexing and querying of complex semantic relationships and is driven by notions of information trust and provenance and models of hypotheses and arguments under investigation.

From a scientific perspective, we face the challenges of formally defining and representing meaningful and interesting relationships (which we call semantic associations), and defining the notion of quality of results similar to the familiar metrics of precision, recall, and document ranking. Another challenge is the (semi) automatic construction of argument structures built on these relationships to validate or deny a given hypothesis. Additional scientific and engineering challenges include those related to the scale of storing and complex query processing of large metadata sets, with corresponding more complex data structures to represent entities and relationships; the need to utilize context to select relevant subsets of metadata to process; and new techniques that use information provenance and trust to improve ranking of relationships. These challenges call for a fresh look at indexing, query processing, and ranking as well as tractable and scalable graph algorithms that exploit heuristics. Our work proposes to address these challenges building on our preliminary results in semantic metadata extraction, practical domain-specific ontology creation, definition of semantic associations, main-memory query processing, use of distributed trust to enforce security policies, and knowledge representation and reasoning on the semantic web. Scientific results from SemDIS will involve detailed scenarios and an evaluation testbed and will be measured in terms of novel techniques as well as performance metrics and measures of quality, scalability, and performance for computing complex semantic relationships. Corresponding to the breadth and depth of the topics involved in the challenge undertaken, ours is a collaborative proposal involving researchers at Wright State, UGA, and UMBC and covering the areas of information modeling and knowledge representation, storage and database management, information retrieval, and artificial intelligence.

Our efforts will have broad effects beyond the education and training of graduate students and the publication of research findings. Results from our research will be integrated with courses we teach, both existing and new. We will use institutional mechanisms in place to seek participation of students from underrepresented groups. Datasets used for test bed evaluations, some targeted tools will be made public or open-source, and new measures for relevance and ranking of semantic associations will provide input to future work comparing various approaches and techniques. Our work will also gain from several university-industry collaborations of the investigators. We will have the opportunity to leverage commercial infrastructure and raw metadata provided by Semagix and IBM, and, when appropriate, technology licensing will be encouraged. The researchers will collaborate with industry, and the students will be encouraged to intern at collaborating industrial labs. Within a broader social context, emerging knowledge-centric technologies raise legitimate privacy and civil liberties concerns. Building on past policymaking experience, we will comment on potential implications of our scientific progress.

Specific Focus areas at Kno.e.sis

This project has resulted in several scientific and community resources including ontologies, tools, and systems that are made available under open source license at no cost to the research community. Some of these are widely used and referred to by others in the Semantic Web research community. The resources include:

This material is based on work supported by the National Science Foundation under Award No. 071441 to Wright State University and No. IIS-0325464 to University of Georgia titled “SemDis: Discovering Complex Relationships in the Semantic Web.” Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. PI: Amit Sheth, Co-PIs: I. Budak Arpinar, Krys Kochut, and John Miller.