Prithviraj Sen, Marina Danilevsky, Yunyao Li, Siddhartha Brahma, Matthias Boehm, Laura Chiticariu, Rajasekar Krishnamurthy. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.

DSMM 2019 will explore the challenges of macro-modeling with financial and economic datasets. The workshop will also showcase the 2019 Financial Entity Identification and Information Integration (FEIII) Challenge which involves two challenge tasks, one over small business data and the other over customs data for shipping manifests.

The ability to create and interact with large-scale domain-specific knowledge bases from unstructured/semi-structured data is the foundation for many industry-focused cognitive systems. We will demonstrate the Content Services system that provides cloud services for creating and querying high-quality domain-specific knowledge bases by analyzing and integrating multiple (un/semi)structured content sources. We will showcase an instantiation of the system for a financial domain. We will also demonstrate both cross-lingual natural language queries and programmatic API calls for interacting with this knowledge base.

Social media is playing a growing role in providing consumer feedback to companies about their products and services. To maximize the benefit of this feedback, companies want to know how different consumer-segments they are interested in, such as parents, frequent travelers, and comic book fans react to their products and campaigns. In this paper, we describe how constructing consumer profiles is valuable to obtain such insights. We present the challenges in analyzing noisy social media data and the techniques we employ for building the profiles. We also present detailed experimental results from the analysis of over seven billion messages to construct profiles of over 100 million consumers. We demonstrate how consumer profiles can help in understanding consumer feedback by different key segments using a TV show analysis scenario.

We introduce HIL, a high-level scripting language for entity resolution and integration. HIL aims at providing the core logic for complex data processing flows that aggregate facts from large collections of structured or unstructured data into clean, unified entities. Such flows typically include many stages of processing that start from the outcome of information extraction and continue with entity resolution, mapping and fusion. A HIL program captures the overall integration flow through a combination of SQL-like rules that link, map, fuse and aggregate entities. A salient feature of HIL is the use of logical indexes in its data model to facilitate the modular construction and aggregation of complex entities. Another feature is the presence of a flexible, open type system that allows HIL to handle input data that is irregular, sparse or partially known.

No abstract available.

We propose to demonstrate an end-to-end framework for leveraging time-sensitive and critical social media information for businesses. More specifically, we focus on identifying, structuring, integrating, and exposing timely insights that are essential to marketing services and monitoring reputation over social media. Our system includes components for information extraction from text, entity resolution and integration, analytics, and a user interface.

MapReduce is emerging as a generic parallel programming paradigm for large clusters of machines. This trend combined with the growing need to run machine learning (ML) algorithms on massive datasets has led to an increased interest in implementing ML algorithms on MapReduce. However, the cost of implementing a large class of ML algorithms as low-level MapReduce jobs on varying data and machine cluster sizes can be prohibitive. In this paper, we propose SystemML in which ML algorithms are expressed in a higher-level language and are compiled and executed in a MapReduce environment. This higher-level language exposes several constructs including linear algebra primitives that constitute key building blocks for a broad class of supervised and unsupervised ML algorithms. The algorithms expressed in SystemML are compiled and optimized into a set of MapReduce jobs that can run on a cluster of machines. We describe and empirically evaluate a number of optimization strategies for efficiently executing these algorithms on Hadoop, an open-source MapReduce implementation. We report an extensive performance evaluation on three ML algorithms on varying data and cluster sizes.

Information Extraction (IE)-the problem of extracting structured information from unstructured text - has become the key enabler for many enterprise applications such as semantic search, business analytics and regulatory compliance. While rule-based IE systems are widely used in practice due to their well-known "explainability," developing high-quality information extraction rules is known to be a labor-intensive and time-consuming iterative process.

The primary goal of the Midas project is to build a system that enables easy and scalable integration of unstructured and semi-structured information present across multiple data sources. As a first step in this direction, we have built a system that extracts and integrates information from regulatory filings submitted to the U.S. Securities and Exchange Commission (SEC) and the Federal Deposit Insurance Corporation (FDIC). Midas creates a repository of entities, events, and relationships by extracting, conceptualizing, integrating, and aggregating data from unstructured and semi-structured documents. This repository enables applications to use the extracted and integrated data in a variety of ways including mashups with other public data and complex risk analysis.

Rule-based information extraction is a process by which structured objects are extracted from text based on user-defined rules. The compositional nature of rule-based information extraction also allows rules to be expressed over previously extracted objects. Such extraction is inherently uncertain, due to the varying precision associated with the rules used in a specific extraction task. Quantifying this uncertainty is crucial for querying the extracted objects in probabilistic databases, and for improving the recall of extraction tasks that use compositional rules. In this paper, we provide a probabilistic framework for handling the uncertainty in rule-based information extraction. Specifically, for each extraction task, we build a parametric exponential model of uncertainty that captures the interaction between the different rules, as well as the compositional nature of the rules; the exponential form of our model follows from maximum-entropy considerations. We also give model-decomposition techniques that make the learning algorithms scalable to large numbers of rules and constraints. Experiments over multiple real-world extraction tasks confirm that our approach yields accurate probability estimates with only a small performance overhead. Moreover, our framework supports incremental pay-as-you-go improvements in the accuracy of probability estimates as new rules, data, or constraints are added.

Traditional approaches to rule-based information extraction (IE) have primarily been based on regular expression grammars. However, these grammar-based systems have difficulty scaling to large data sets and large numbers of rules. Inspired by traditional database research, we propose an algebraic approach to rule-based IE that addresses these scalability issues through query optimization. The operators of our algebra are motivated by our experience in building several rule-based extraction programs over diverse data sets. We present the operators of our algebra and propose several optimization strategies motivated by the text-specific characteristics of our operators. Finally we validate the potential benefits of our approach by extensive experiments over real-world blog data.

"Sparse" data, in which relations have many attributes that are null for most tuples, presents a challenge for relational database management systems. If one uses the normal "horizontal" schema to store such data sets in any of the three leading commercial RDBMS, the result is tables that occupy vast amounts of storage, most of which is devoted to nulls. If one attempts to avoid this storage blowup by using a "vertical" schema, the storage utilization is indeed better, but query performance is orders of magnitude slower for certain classes of queries. In this paper, we argue that the proper way to handle sparse data is not to use a vertical schema, but rather to extend the RDBMS tuple storage format to allow the representation of sparse attributes as interpreted fields. The addition of interpreted storage allows for efficient and transparent querying of sparse data, uniform access to all attributes, and schema scalability. We show, through an implementation in PostgreSQL, that the interpreted storage approach dominates in query efficiency and ease-of-use over the current horizontal storage and vertical schema approaches over a wide range of queries and sparse data sets.

Many searches on the web have a transactional intent. We argue that pages satisfying transactional needs can be distinguished from the more common pages that have some information and links, but cannot be used to execute a transaction. Based on this hypothesis, we provide a recipe for constructing a transaction annotator. By constructing an annotator with one corpus and then demonstrating its classification performance on another,we establish its robustness. Finally, we show experimentally that a search procedure that exploits such pre-annotation greatly outperforms traditional search for retrieving transactional pages.

We present Avatar Semantic Search, a prototype search engine that exploits annotations in the context of classical keyword search. The process of annotations is accomplished offline by using high-precision information extraction techniques to extract facts, con-cepts, and relationships from text. These facts and concepts are represented and indexed in a structured data store. At runtime, keyword queries are interpreted in the context of these extracted facts and converted into one or more precise queries over the structured store. In this demonstration we describe the overall architecture of the Avatar Semantic Search engine. We also demonstrate the superiority of the AVATAR approach over traditional keyword search engines using Enron email data set and a blog corpus.

The SQL queries produced in XML-to-SQL query translation are often unnecessarily complex, even for simple input XML queries. In this paper we argue that relational systems can do a better job of XML-to-SQL query translation with the addition of a simple new constraint, which we term the "lossless from XML" constraint. Intuitively, this constraint states that a given relational data set resulted from the shredding of an XML document that conformed to a given schema. We illustrate the power of this approach by giving an algorithm that exploits the "lossless from XML" constraint to translate path expression queries into efficient SQL, even in the presence of recursive XML schemas. We argue that this approach is likely to be simpler and more effective than the current state of the art in optimizing XML-to-SQL query translation, which involves identifying and declaring multiple complex relational constraints and then reasoning about relational query containment in the presence of these constraints.

Recently, there has been a great deal of interest in the development of techniques to evaluate path expressions over collections of XML documents. In general, these path expressions contain both structural and keyword components. Several methods have been proposed for processing path expressions over graph/tree-structured XML data. These methods can be classified into two broad classes. The first involves graph traversal where the input query is evaluated by traversing the data graph or some compressed representation. The other class involves information-retrieval style processing using inverted lists. In this framework, structure indexes have been proposed to be used as a substitute for graph traversal. Here, we focus on a subclass of CAS queries consisting of simple path expressions. We study algorithmic issues in integrating structure indexes with inverted lists for the evaluation of these queries, where we rank all documents that match the query and return the top k documents in order of relevance.

We consider the problem of translating XML queries into SQL when XML documents have been stored in an RDBMS using a schema-based relational decomposition. Surprisingly, there is no published XML-to-SQL query translation algorithm for this scenario that handles recursive XML schemas. We present a generic algorithm to translate path expression queries into SQL in the presence of recursion in the schema and queries. This algorithm handles a general class of XML-to-relational mappings, which includes all techniques proposed in literature. Some of the salient features of this algorithm are: (i) It translates a path expression query into a single SQL query, irrespective of how complex the XML schema is, (ii) It uses the "with" clause in SQL99 to handle recursive queries even over nonrecursive schemas, (iii) It reconstructs recursive XML subtrees with a single SQL query and (iv) It shows that the support for linear recursion in SQL99 is sufficient for handling path expression queries over arbitrarily complex recursive XML schema.

Several methods have been proposed to evaluate queries over a native XML DBMS, where the queries specify both path and keyword constraints. These broadly consist of graph traversal approaches, optimized with auxiliary structures known as structure indexes; and approaches based on information-retrieval style inverted lists. We propose a strategy that combines the two forms of auxiliary indexes, and a query evaluation algorithm for branching path expressions based on this strategy. Our technique is general and applicable for a wide range of choices of structure indexes and inverted list join algorithms. Our experiments over the Niagara XML DBMS show the benefit of integrating the two forms of indexes. We also consider algorithmic issues in evaluating path expression queries when the notion of relevance ranking is incorporated. By integrating the above techniques with the Threshold Algorithm proposed by Fagin et al., we obtain instance optimal algorithms to push down top k computation.

We consider the scenario where existing relational data is exported as XML. In this context, we look at the problem of translating XML queries into SQL. XML query languages have two different notions of duplicates: node-identity based and value-based. Path expression queries have an implicit node-identity based duplicate elimination built into them. On the other hand, SQL only supports value-based duplicate elimination. In this paper, using a simple path expression query we illustrate the problems that arise when we attempt to simulate the node-identity based duplicate elimination using value-based duplicate elimination in the SQL queries. We show how a general solution for this problem covering the class of views considered in published literature requires a fairly complex mechanism.