Maya Ramanath

NAGA: Searching and Ranking Knowledge

The Web has the potential to become the worlds largest knowledge base. In order to unleash this potential, the wealth of information available on the Web needs to be extracted and organized. There is a need for new querying techniques that are simple and yet more expressive than those provided by standard keyword-based search engines. Searching for knowledge rather than Web pages needs to consider inherent semantic structures like entities (person, organization, etc.) and relationships (isA, located In, etc.). In this paper, we propose NAGA, a new semantic search engine. NAGA builds on a knowledge base, which is organized as a graph with typed edges, and consists of millions of entities and relationships extracted from Web-based corpora. A graph-based query language enables the formulation of queries with additional semantic information. We introduce a novel scoring model, based on the principles of generative language models, which formalizes several notions such as confidence, informativeness and compactness and uses them to rank query results. We demonstrate NAGAs superior result quality over state-of-the-art search engines and question answering systems.

StatiX: making XML count

The availability of summary data for XML documents has many applications, from providing users with quick feedback about their queries, to cost-based storage design and query optimization. StatiX is a novel XML Schema-aware statistics framework that exploits the structure derived by regular expressions (which define elements in an XML Schema) to pinpoint places in the schema that are likely sources of structural skew. As we discuss below, this information can be used to build concise, yet accurate, statistical summaries for XML data. StatiX leverages standard XML technology for gathering statistics, notably XML Schema validators, and it uses histograms to summarize both the structure and values in an XML document. In this paper we describe the StatiX system. We develop algorithms that decompose schemas to obtain statistics at different granularities and discuss how statistics can be gathered as documents are validated. We also present an experimental evaluation which demonstrates the accuracy and scalability of our approach and show an application of these statistics to cost-based XML storage design.

STAR: Steiner-Tree Approximation in Relationship Graphs

Large graphs and networks are abundant in modern information systems: entity-relationship graphs over relational data or Web-extracted entities, biological networks, social online communities, knowledge bases, and many more. Often such data comes with expressive node and edge labels that allow an interpretation as a semantic graph, and edge weights that reflect the strengths of semantic relations between entities. Finding close relationships between a given set of two, three, or more entities is an important building block for many search, ranking, and analysis tasks. From an algorithmic point of view, this translates into computing the best Steiner trees between the given nodes, a classical NP-hard problem. In this paper, we present a new approximation algorithm, coined STAR, for relationship queries over large relationship graphs. We prove that for n query entities, STAR yields an O(log(n))-approximation of the optimal Steiner tree in pseudopolynomial run-time, and show that in practical cases the results returned by STAR are qualitatively comparable to or even better than the results returned by a classical 2-approximation algorithm. We then describe an extension to our algorithm to return the top-k Steiner trees. Finally, we evaluate our algorithm over both main-memory as well as completely diskresident graphs containing millions of nodes. Our experiments show that in terms of efficiency STAR outperforms the best state-of-the-art database methods by a large margin, and also returns qualitatively better results.

The YAGO-NAGA approach to knowledge discovery

This paper gives an overview on the YAGO-NAGA approach to information extraction for building a conveniently searchable, large-scale, highly accurate knowledge base of common facts. YAGO harvests infoboxes and category names of Wikipedia for facts about individual entities, and it reconciles these with the taxonomic backbone of WordNet in order to ensure that all entities have proper classes and the class system is consistent. Currently, the YAGO knowledge base contains about 19 million instances of binary relations for about 1.95 million entities. Based on intensive sampling, its accuracy is estimated to be above 95 percent. The paper presents the architecture of the YAGO extractor toolkit, its distinctive approach to consistency checking, its provisions for maintenance and further growth, and the query engine for YAGO, coined NAGA. It also discusses ongoing work on extensions towards integrating fact candidates extracted from natural-language text sources.

Database and information-retrieval methods for knowledge discovery

Comprehensive knowledge bases would tap the Webs deepest information sources and relationships to address questions beyond todays keyword-based search engines.

NAGA: harvesting, searching and ranking knowledge

The presence of encyclopedic Web sources, such as Wikipedia, the Internet Movie Database (IMDB), World Factbook, etc. calls for new querying techniques that are simple and yet more expressive than those provided by standard keyword-based search engines. Searching for explicit knowledge needs to consider inherent semantic structures involving entities and relationships. In this demonstration proposal, we describe a semantic search system named NAGA. NAGA operates on a knowledge graph, which contains millions of entities and relationships derived from various encyclopedic Web sources, such as the ones above. NAGAs graph-based query language is geared towards expressing queries with additional semantic information. Its scoring model is based on the principles of generative language models, and formalizes several desiderata such as confidence, informativeness and compactness of answers. We propose a demonstration of NAGA which will allow users to browse the knowledge base through a user interface, enter queries in NAGAs query language and tune the ranking parameters to test various ranking aspects.

Query relaxation for entity-relationship search

Entity-relationship-structured data is becoming more important on the Web. For example, large knowledge bases have been automatically constructed by information extraction from Wikipedia and other Web sources. Entities and relationships can be represented by subject-property-object triples in the RDF model, and can then be precisely searched by structured query languages like SPARQL. Because of their Boolean-match semantics, such queries often return too few or even no results. To improve recall, it is thus desirable to support users by automatically relaxing or reformulating queries in such a way that the intention of the original user query is preserved while returning a sufficient number of ranked results. In this paper we describe comprehensive methods to relax SPARQL-like triplepattern queries in a fully automated manner. Our framework produces a set of relaxations by means of statistical language models for structured RDF data and queries. The query processing algorithms merge the results of different relaxations into a unified result list, with ranking based on any ranking function for structured queries over RDF-data. Our experimental evaluation, with two different datasets about movies and books, shows the effectiveness of the automatically generated relaxations and the improved quality of query results based on assessments collected on the Amazon Mechanical Turk platform.

Searching for efficient XML-to-relational mappings

We consider the problem of cost-based strategies to derive efficient relational configurations for XML applications that subscribe to an XML Schema. In particular, we propose a flexible framework for XML schema transformations and show how it can be used to design algorithms to search the space of equivalent relational configurations. We study the impact of the schema transformations and query workload on the search strategies for finding efficient XML-to-relational mappings. In addition, we propose several optimizations to speed up the search process. Our experiments indicate that a judicious choice of transformations and search strategies can lead to relational configurations of substantially higher quality than those recommended by previous approaches.

Deep answers for naturally asked questions on the web of data

We present DEANNA, a framework for natural language question answering over structured knowledge bases. Given a natural language question, DEANNA translates questions into a structured SPARQL query that can be evaluated over knowledge bases such as Yago, Dbpedia, Freebase, or other Linked Data sources. DEANNA analyzes questions and maps verbal phrases to relations and noun phrases to either individual entities or semantic classes. Importantly, it judiciously generates variables for target entities or classes to express joins between multiple triple patterns. We leverage the semantic type system for entities and use constraints in jointly mapping the constituents of the question to relations, classes, and entities. We demonstrate the capabilities and interface of DEANNA, which allows advanced users to influence the translation process and to see how the different components interact to produce the final result.

Harmony and dissonance: organizing the people's voices on political controversies

The wikileaks documents about the death of Osama Bin Laden and the debates about the economic crisis in Greece and other European countries are some of the controversial topics being played on the news everyday. Each of these topics has many different aspects, and there is no absolute, simple truth in answering questions such as: should the EU guarantee the financial stability of each member country, or should the countries themselves be solely responsible? To understand the landscape of opinions, it would be helpful to know which politician or other stakeholder takes which position - support or opposition - on these aspects of controversial topics.