A Basis for Interactive Schema Merging

We present a technique for merging the schemas of heterogeneous databases that generalizes to several different data models, and show how it can be used in an interactive program that merges Entity-Relationship diagrams. Given a collection of schemas to be merged, the user asserts the correspondence between entities and relationships in the various schemas by defining "isa" relations between them. These assertions are then considered to be elementary schemas, and are combined with the elementary schemas in the merge. Since the method defines the merge to be the join in an information ordering on schemas, it is a commutative and associative operation, which means that the merge is defined independent of the order in which schemas are presented. We briefly describe a prototype interactive schema merging tool that has been built on these principles. Keywords: schemas, merging, semantic data models, entity-relationship data models, inheritance 1 Introduction Schema merging is the proble..

Report of MIRACLE team for Geographical IR in CLEF 2006

The main objective of the designed experiments is testing the effects of geographical information retrieval from documents that contain geographical tags. In the designed experiments we try to isolate geographical retrieval from textual retrieval replacing all geo-entity textual references from topics with associated tags and splitting the retrieval process in two phases: textual retrieval from the textual part of the topic without geo-entity references and geographical retrieval from the tagged text generated by the topic tagger. Textual and geographical results are combined applying different techniques: union, intersection, difference, and external join based. Our geographic information retrieval system consists of a set of basics components organized in two categories: (i) linguistic tools oriented to textual analysis and retrieval and (ii) resources and tools oriented to geographical analysis. These tools are combined to carry out the different phases of the system: (i) documents and topics analysis, (ii) relevant documents retrieval and (iii) result combination. If we compare the results achieved to the last campaign’s results, we can assert that mean average precision gets worse when the textual geo-entity references are replaced with geographical tags. Part of this worsening is due to our experiments return cero pertinent documents if no documents satisfy de geographical sub-query. But if we only analyze the results of queries that satisfied both textual and geographical terms, we observe that the designed experiments recover pertinent documents quickly, improving R-Precision values. We conclude that the developed geographical information retrieval system is very sensible to textual georeference and therefore it is necessary to improve the name entity recognition module

Disparate Tax Treatment of Different Types of Business Organizations: Where Should We Go from Here?

If several persons wish to join together in a common enterprise in order to pool their capital or labor or some of each, they may choose among a variety of available organizational structures that will serve that purpose. The most common entity forms are partnerships (including joint ventures), corporations, and trusts. While, in its typical structure, each of those entity forms has its own distinct characteristics, the structure of such organizations often is modified by agreement so as to adopt attributes of another type of entity. Because of this, the substantive distinction between entity types is blurred

Subgraph Pattern Matching over Uncertain Graphs with Identity Linkage Uncertainty

There is a growing need for methods which can capture uncertainties and answer queries over graph-structured data. Two common types of uncertainty are uncertainty over the attribute values of nodes and uncertainty over the existence of edges. In this paper, we combine those with identity uncertainty. Identity uncertainty represents uncertainty over the mapping from objects mentioned in the data, or references, to the underlying real-world entities. We propose the notion of a probabilistic entity graph (PEG), a probabilistic graph model that defines a distribution over possible graphs at the entity level. The model takes into account node attribute uncertainty, edge existence uncertainty, and identity uncertainty, and thus enables us to systematically reason about all three types of uncertainties in a uniform manner. We introduce a general framework for constructing a PEG given uncertain data at the reference level and develop highly efficient algorithms to answer subgraph pattern matching queries in this setting. Our algorithms are based on two novel ideas: context-aware path indexing and reduction by join-candidates, which drastically reduce the query search space. A comprehensive experimental evaluation shows that our approach outperforms baseline implementations by orders of magnitude

Query-Driven Sampling for Collective Entity Resolution

Probabilistic databases play a preeminent role in the processing and management of uncertain data. Recently, many database research efforts have integrated probabilistic models into databases to support tasks such as information extraction and labeling. Many of these efforts are based on batch oriented inference which inhibits a realtime workflow. One important task is entity resolution (ER). ER is the process of determining records (mentions) in a database that correspond to the same real-world entity. Traditional pairwise ER methods can lead to inconsistencies and low accuracy due to localized decisions. Leading ER systems solve this problem by collectively resolving all records using a probabilistic graphical model and Markov chain Monte Carlo (MCMC) inference. However, for large datasets this is an extremely expensive process. One key observation is that, such exhaustive ER process incurs a huge up-front cost, which is wasteful in practice because most users are interested in only a small subset of entities. In this paper, we advocate pay-as-you-go entity resolution by developing a number of query-driven collective ER techniques. We introduce two classes of SQL queries that involve ER operators --- selection-driven ER and join-driven ER. We implement novel variations of the MCMC Metropolis Hastings algorithm to generate biased samples and selectivity-based scheduling algorithms to support the two classes of ER queries. Finally, we show that query-driven ER algorithms can converge and return results within minutes over a database populated with the extraction from a newswire dataset containing 71 million mentions

Nonprofit Mergers: An Assessment of Nonprofits' Experiences with the Merger Process

An increasing number of nonprofit organizations are exploring mergers -- the process by which at least two nonprofit corporations join to form one legal entity. Yet, little is known about nonprofits' experiences with the merger process. What leads nonprofits to explore a merger and what outcomes do they expect to achieve as a result? Who within the organization is typically involved in facilitating the merger? How long do mergers take to complete, what do they cost and, above all, what are the results? Drawing on the experiences of 22 nonprofit organizations in Allegheny County that explored, attempted or completed a merger, combined with a comprehensive literature review, this report seeks to answer those questions and provide recommendations that nonprofits and funders can use to inform their conversations about the merger process

Off-the-Shelf, into the Community: Academic/Community Outreach

Three members of the University of Northern Colorado\u27s Libraries faculty share their experiences implementing programming and engaged outreach to the campus and surrounding community. Leveraging opportunities on - and off-campus revealed unanticipated challenges and rewards for the presenters; join this lively conversation about our journey as a nontraditional engaged campus entity