Retrieving information from heterogeneous freight data sources to answer natural language queries

textThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to user’s query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a user’s question is proposed.Civil, Architectural, and Environmental Engineerin

A Bayesian Approach to Graphical Record Linkage and De-duplication

We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves the representation of the pattern of links between records as a bipartite graph, in which records are directly linked to latent true individuals, and only indirectly linked to other records. This flexible representation of the linkage structure naturally allows us to estimate the attributes of the unique observable people in the population, calculate transitive linkage probabilities across records (and represent this visually), and propagate the uncertainty of record linkage into later analyses. Our method makes it particularly easy to integrate record linkage with post-processing procedures such as logistic regression, capture-recapture, etc. Our linkage structure lends itself to an efficient, linear-time, hybrid Markov chain Monte Carlo algorithm, which overcomes many obstacles encountered by previously record linkage approaches, despite the high-dimensional parameter space. We illustrate our method using longitudinal data from the National Long Term Care Survey and with data from the Italian Survey on Household and Wealth, where we assess the accuracy of our method and show it to be better in terms of error rates and empirical scalability than other approaches in the literature.Comment: 39 pages, 8 figures, 8 tables. Longer version of arXiv:1403.0211, In press, Journal of the American Statistical Association: Theory and Methods (2015

Pair-Linking for Collective Entity Disambiguation: Two Could Be Better Than All

Collective entity disambiguation aims to jointly resolve multiple mentions by linking them to their associated entities in a knowledge base. Previous works are primarily based on the underlying assumption that entities within the same document are highly related. However, the extend to which these mentioned entities are actually connected in reality is rarely studied and therefore raises interesting research questions. For the first time, we show that the semantic relationships between the mentioned entities are in fact less dense than expected. This could be attributed to several reasons such as noise, data sparsity and knowledge base incompleteness. As a remedy, we introduce MINTREE, a new tree-based objective for the entity disambiguation problem. The key intuition behind MINTREE is the concept of coherence relaxation which utilizes the weight of a minimum spanning tree to measure the coherence between entities. Based on this new objective, we design a novel entity disambiguation algorithms which we call Pair-Linking. Instead of considering all the given mentions, Pair-Linking iteratively selects a pair with the highest confidence at each step for decision making. Via extensive experiments, we show that our approach is not only more accurate but also surprisingly faster than many state-of-the-art collective linking algorithms

NLSC: Unrestricted Natural Language-based Service Composition through Sentence Embeddings

Current approaches for service composition (assemblies of atomic services) require developers to use: (a) domain-specific semantics to formalize services that restrict the vocabulary for their descriptions, and (b) translation mechanisms for service retrieval to convert unstructured user requests to strongly-typed semantic representations. In our work, we argue that effort to developing service descriptions, request translations, and matching mechanisms could be reduced using unrestricted natural language; allowing both: (1) end-users to intuitively express their needs using natural language, and (2) service developers to develop services without relying on syntactic/semantic description languages. Although there are some natural language-based service composition approaches, they restrict service retrieval to syntactic/semantic matching. With recent developments in Machine learning and Natural Language Processing, we motivate the use of Sentence Embeddings by leveraging richer semantic representations of sentences for service description, matching and retrieval. Experimental results show that service composition development effort may be reduced by more than 44\% while keeping a high precision/recall when matching high-level user requests with low-level service method invocations.Comment: This paper will appear on SCC'19 (IEEE International Conference on Services Computing) on July 1