Toward Entity-Aware Search

As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability

Managing the consistency of distributed documents

Many businesses produce documents as part of their daily activities: software engineers produce requirements specifications, design models, source code, build scripts and more; business analysts produce glossaries, use cases, organisation charts, and domain ontology models; service providers and retailers produce catalogues, customer data, purchase orders, invoices and web pages. What these examples have in common is that the content of documents is often semantically related: source code should be consistent with the design model, a domain ontology may refer to employees in an organisation chart, and invoices to customers should be consistent with stored customer data and purchase orders. As businesses grow and documents are added, it becomes difficult to manually track and check the increasingly complex relationships between documents. The problem is compounded by current trends towards distributed working, either over the Internet or over a global corporate network in large organisations. This adds complexity as related information is not only scattered over a number of documents, but the documents themselves are distributed across multiple physical locations. This thesis addresses the problem of managing the consistency of distributed and possibly heterogeneous documents. “Documents” is used here as an abstract term, and does not necessarily refer to a human readable textual representation. We use the word to stand for a file or data source holding structured information, like a database table, or some source of semi-structured information, like a file of comma-separated values or a document represented in a hypertext markup language like XML [Bray et al., 2000]. Document heterogeneity comes into play when data with similar semantics is represented in different ways: for example, a design model may store a class as a rectangle in a diagram whereas a source code file will embed it as a textual string; and an invoice may contain an invoice identifier that is composed of a customer name and date, both of which may be recorded and managed separately. Consistency management in this setting encompasses a number of steps. Firstly, checks must be executed in order to determine the consistency status of documents. Documents are inconsistent if their internal elements hold values that do not meet the properties expected in the application domain or if there are conflicts between the values of elements in multiple documents. The results of a consistency check have to be accumulated and reported back to the user. And finally, the user may choose to change the documents to bring them into a consistent state. The current generation of tools and techniques is not always sufficiently equipped to deal with this problem. Consistency checking is mostly tightly integrated or hardcoded into tools, leading to problems with extensibility with respect to new types of documents. Many tools do not support checks of distributed data, insisting instead on accumulating everything in a centralized repository. This may not always be possible, due to organisational or time constraints, and can represent excessive overhead if the only purpose of integration is to improve data consistency rather than deriving any additional benefit. This thesis investigates the theoretical background and practical support necessary to support consistency management of distributed documents. It makes a number of contributions to the state of the art, and the overall approach is validated in significant case studies that provide evidence of its practicality and usefulness

Extraction and Analysis of Facebook Friendship Relations

Online Social Networks (OSNs) are a unique Web and social phenomenon, affecting tastes and behaviors of their users and helping them to maintain/create friendships. It is interesting to analyze the growth and evolution of Online Social Networks both from the point of view of marketing and other of new services and from a scientific viewpoint, since their structure and evolution may share similarities with real-life social networks. In social sciences, several techniques for analyzing (online) social networks have been developed, to evaluate quantitative properties (e.g., defining metrics and measures of structural characteristics of the networks) or qualitative aspects (e.g., studying the attachment model for the network evolution, the binary trust relationships, and the link prediction problem).\ud However, OSN analysis poses novel challenges both to Computer and Social scientists. We present our long-term research effort in analyzing Facebook, the largest and arguably most successful OSN today: it gathers more than 500 million users. Access to data about Facebook users and their friendship relations, is restricted; thus, we acquired the necessary information directly from the front-end of the Web site, in order to reconstruct a sub-graph representing anonymous interconnections among a significant subset of users. We describe our ad-hoc, privacy-compliant crawler for Facebook data extraction. To minimize bias, we adopt two different graph mining techniques: breadth-first search (BFS) and rejection sampling. To analyze the structural properties of samples consisting of millions of nodes, we developed a specific tool for analyzing quantitative and qualitative properties of social networks, adopting and improving existing Social Network Analysis (SNA) techniques and algorithms

The Parma Polyhedra Library: Toward a Complete Set of Numerical Abstractions for the Analysis and Verification of Hardware and Software Systems

Since its inception as a student project in 2001, initially just for the handling (as the name implies) of convex polyhedra, the Parma Polyhedra Library has been continuously improved and extended by joining scrupulous research on the theoretical foundations of (possibly non-convex) numerical abstractions to a total adherence to the best available practices in software development. Even though it is still not fully mature and functionally complete, the Parma Polyhedra Library already offers a combination of functionality, reliability, usability and performance that is not matched by similar, freely available libraries. In this paper, we present the main features of the current version of the library, emphasizing those that distinguish it from other similar libraries and those that are important for applications in the field of analysis and verification of hardware and software systems.Comment: 38 pages, 2 figures, 3 listings, 3 table

A Hybrid Approach to Finding Relevant Social Media Content for Complex Domain Specific Information Needs

While contemporary semantic search systems offer to improve classical keyword-based search, they are not always adequate for complex domain specific information needs. The domain of prescription drug abuse, for example, requires knowledge of both ontological concepts and 'intelligible constructs' not typically modeled in ontologies. These intelligible constructs convey essential information that include notions of intensity, frequency, interval, dosage and sentiments, which could be important to the holistic needs of the information seeker. We present a hybrid approach to domain specific information retrieval (or knowledge-aware search) that integrates ontology-driven query interpretation with synonym-based query expansion and domain specific rules, to facilitate search in social media. Our framework is based on a context-free grammar (CFG) that defines the query language of constructs interpretable by the search system. The grammar provides two levels of semantic interpretation: 1) a top-level CFG that facilitates retrieval of diverse textual patterns, which belong to broad templates and 2) a low-level CFG that enables interpretation of certain specific expressions that belong to such patterns. These low-level expressions occur as concepts from four different categories of data: 1) ontological concepts, 2) concepts in lexicons (such as emotions and sentiments), 3) concepts in lexicons with only partial ontology representation, called lexico-ontology concepts (such as side effects and routes of administration (ROA)), and 4) domain specific expressions (such as date, time, interval, frequency and dosage) derived solely through rules. Our approach is embodied in a novel Semantic Web platform called PREDOSE developed for prescription drug abuse epidemiology. Keywords: Knowledge-Aware Search, Ontology, Semantic Search, Background Knowledge, Context-Free GrammarComment: Accepted for publication: Journal of Web Semantics, Elsevie