Scalable Ontology Systems

Since the adoption of the Resource Description Framework (RDF) by the World Wide Web Consortium (W3C), ontologies have become commonplace as a way to represent both knowledge and data. RDF databases have flexible schemas, are easy to integrate and allow a semantically rich query language. Unfortunately, these advantages come at the expense of increased query and application complexity. Existing RDF systems have attempted to address this problem by representing RDF data in relational format and translating queries and answers to and from SQL. As we will show, typical access patterns in RDF are substantially different than those in relational databases, to the extent that the performance of relational-backed systems degrades significantly for large datasets or complex queries. In this dissertation, we propose two solutions to the scalability issue in RDF databases. First, we introduce Annotated RDF, a representation language that extends the semantics of RDF by allowing triples to be annotated with partially ordered information such as temporal validity intervals, probabilities, provenance and many others. In standard RDF, using such information creates a blowup in the size of the database and therefore greatly increases the data complexity of queries. We define a query language for Annotated RDF that extends the RDF query language SPARQL and provides query processing and view maintenance algorithms. Our experimental evaluation shows Annotated RDF can answer queries 1.5 to 3.5 times faster than widely used systems such as Jena2, Sesame2 or Oracle 11g. Second, we introduce GRIN, to our knowledge the first index structure designed specifically for SPARQL queries. We describe query and update processing algorithms and a theoretical analysis of index optimization. GRIN is extended to Annotated RDF and evaluated thoroughly on real-world datasets of up to 26 million triples and benchmark synthetic datasets of up to 1 billion triples. Our results show that for SPARQL queries, GRIN outperforms all relational index structures at comparable resource expenditure. Moreover, we show GRIN can be integrated with Annotated RDF, but also with existing systems such as Jena2 or LucidDB

A Distributed Graph Approach for Pre-processing Linked RDF Data Using Supercomputers

Efficient RDF, graph based queries are becoming more pertinent based on the increased interest in data analytics and its intersection with large, unstructured but connected data. Many commercial systems have adopted distributed RDF graph systems in order to handle increasing dataset sizes and complex queries. This paper introduces a distribute graph approach to pre-processing linked data. Instead of traversing the memory graph, our system indexes pre-processed join elements that are organized in a graph structure. We analyze the Dbpedia data-set (derived from the Wikipedia corpus) and compare our access method to the graph traversal access approach which we also devise. Results show from our experiments that the distributed, pre-processed graph approach to accessing linked data is faster than the traversal approach over a specific range of linked queries

Bounding Quality in Diverse Planning

Diverse planning is an important problem in automated planning with many real world applications. Recently, diverse planning has seen renewed interest, with work that defines a taxonomy of computational problems with respect to both plan quality and solution diversity. However, despite the recent advances in diverse planning, the variety of approaches and the number of available planners are still quite limited, even nonexistent for several computational problems. In this work, we aim to extend the portfolio of planners for various computational problems in diverse planning. To that end, we introduce a novel approach to finding solutions for three computational problems within diverse planning and present planners for these three problems. For one of these problems, our approach is the first one that is able to provide solutions to the problem. For another, we show that top-k and top quality planners can provide, albeit naive, solutions to the problem and we extend these planners to improve the diversity of the solution. Finally, for the third problem, we show that some existing diverse planners already provide solutions to that problem. We suggest another approach and empirically show it to compare favorably with these existing planners

State Projection via AI Planning

Imagining the future helps anticipate and prepare for what is coming. This has great importance to many, if not all, human endeavors. In this paper, we develop the Planning Projector system prototype, which applies plan-recognition-as-planning technique to both explain the observations derived from analyzing relevant news and social media, and project a range of possible future state trajectories for human review. Unlike the plan recognition problem, where a set of goals, and often a plan library must be given as part of the input, the Planning Projector system takes as input the domain knowledge, a sequence of observations derived from the news, a time horizon, and the number of trajectories to produce. It then computes the set of trajectories by applying a planner capable of finding a set of high-quality plans on a transformed planning problem. The Planning Projector prototype integrates several components including: (1) knowledge engineering: the process of encoding the domain knowledge from domain experts; (2) data transformation: the problem of analyzing and transforming the raw data into a sequence of observations; (3) trajectory computation: characterizing the future state projection problem and computing a set of trajectories; (4) user interface: clustering and visualizing the trajectories. We evaluate our approach qualitatively and conclude that the Planning Projector helps users understand future possibilities so that they can make more informed decisions

Top-Quality Planning: Finding Practically Useful Sets of Best Plans

The need for finding a set of plans rather than one has been motivated by a variety of planning applications. The problem is studied in the context of both diverse and top-k planning: while diverse planning focuses on the difference between pairs of plans, the focus of top-k planning is on the quality of each individual plan. Recent work in diverse planning introduced additionally restrictions on solution quality. Naturally, there are application domains where diversity plays the major role and domains where quality is the predominant feature. In both cases, however, the amount of produced plans is often an artificial constraint, and therefore the actual number has little meaning. Inspired by the recent work in diverse planning, we propose a new family of computational problems called top-quality planning, where solution validity is defined through plan quality bound rather than an arbitrary number of plans. Switching to bounding plan quality allows us to implicitly represent sets of plans. In particular, it makes it possible to represent sets of plans that correspond to valid plan reorderings with a single plan. We formally define the unordered top-quality planning computational problem and present the first planner for that problem. We empirically demonstrate the superior performance of our approach compared to a top-k planner-based baseline, ranging from 41% increase in coverage for finding all optimal plans to 69% increase in coverage for finding all plans of quality up to 120% of optimal plan cost. Finally, complementing the new approach by a complete procedure for generating all valid reorderings of a given plan, we derive a top-quality planner. We show the planner to be competitive with a top-k planner based baseline