Lushan Han

RDF123: From Spreadsheets to RDF

We describe RDF123, a highly flexible open-source tool for translating spreadsheet data to RDF. Existing spreadsheet-to-rdf tools typically map only to star-shaped RDF graphs, i.e. each spreadsheet row is an instance, with each column representing a property. RDF123, on the other hand, allows users to define mappings to arbitrary graphs, thus allowing much richer spreadsheet semantics to be expressed. Further, each row in the spreadsheet can be mapped with a fairly different RDF scheme. Two interfaces are available. The first is a graphical application that allows users to create their mapping in an intuitive manner. The second is a Web service that takes as input a URL to a Google spreadsheet or CSV file and an RDF123 map, and provides RDF as output.

Improving Word Similarity by Augmenting PMI with Estimates of Word Polysemy

Pointwise mutual information (PMI) is a widely used word similarity measure, but it lacks a clear explanation of how it works. We explore how PMI differs from distributional similarity, and we introduce a novel metric, PMImax, that augments PMI with information about a words number of senses. The coefficients of PMImax are determined empirically by maximizing a utility function based on the performance of automatic thesaurus generation. We show that it outperforms traditional PMI in the application of automatic thesaurus generation and in two word similarity benchmark tasks: human similarity ratings and TOEFL synonym questions. PMImax achieves a correlation coefficient comparable to the best knowledge-based approaches on the Miller-Charles similarity rating data set.

Meerkat Mafia: Multilingual and Cross-Level Semantic Textual Similarity Systems

We describe UMBC’s systems developed for the SemEval 2014 tasks on Multilingual Semantic Textual Similarity (Task 10) and Cross-Level Semantic Similarity (Task 3). Our best submission in the Multilingual task ranked second in both English and Spanish subtasks using an unsupervised approach. Our best systems for Cross-Level task ranked second in Paragraph-Sentence and first in both Sentence-Phrase and Word-Sense subtask. The system ranked first for the PhraseWord subtask but was not included in the official results due to a late submission.

GoRelations: an intuitive query system for DBpedia

Although a formal query language, SPARQL, is available for accessing DBpedia, it remains challenging for users to query the knowledge unless they are familiar with the syntax of SPARQL and the underlying ontology. We have developed both an intuitive semantic graph notation or interface allowing one to pose a query by annotating a graph with natural language terms denoting entities and relations and a system that automatically translates the query into SPARQL to produce an answer. Our key contributions are the robust techniques, combining statistical association and semantic similarity, that map user terms to the most appropriate classes and properties used in the DBpedia Ontology.

Robust semantic text similarity using LSA, machine learning, and linguistic resources

Semantic textual similarity is a measure of the degree of semantic equivalence between two pieces of text. We describe the SemSim system and its performance in the *SEM 2013 and SemEval-2014 tasks on semantic textual similarity. At the core of our system lies a robust distributional word similarity component that combines latent semantic analysis and machine learning augmented with data from several linguistic resources. We used a simple term alignment algorithm to handle longer pieces of text. Additional wrappers and resources were used to handle task specific challenges that include processing Spanish text, comparing text sequences of different lengths, handling informal words and phrases, and matching words with sense definitions. In the *SEM 2013 task on Semantic Textual Similarity, our best performing system ranked first among the 89 submitted runs. In the SemEval-2014 task on Multilingual Semantic Textual Similarity, we ranked a close second in both the English and Spanish subtasks. In the SemEval-2014 task on Cross-Level Semantic Similarity, we ranked first in Sentence–Phrase, Phrase–Word, and Word–Sense subtasks and second in the Paragraph–Sentence subtask.

UMBC_Ebiquity-SFQ: Schema Free Querying System

Users need better ways to explore large complex linked data resources. Using SPARQL requires not only mastering its syntax and semantics but also understanding the RDF data model, the ontology and URIs for entities of interest. Natural language question answering systems solve the problem, but these are still subjects of research. The Schema agnostic SPARQL queries task defined in SAQ-2015 challenge consists of schema-agnostic queries following the syntax of the SPARQL standard, where the syntax and semantics of oper- ators are maintained, while users are free to choose words, phrases and entity names irrespective of the underlying schema or ontology. This combination of query skeleton with keywords helps to remove some of the ambiguity. We describe our framework for handling schema agnostic or schema free queries and discuss enhancements to handle the SAQ-2015 challenge queries. The key contributions are the robust methods that combine statistical association and semantic similarity to map user terms to the most appropriate classes and properties used in the underlying ontology and type inference for user input concepts based on concept linking.