Semantic Document Clustering Using Information from WordNet and DBPedia

Semantic document clustering is a type of unsupervised learning in which documents are grouped together based on their meaning. Unlike traditional approaches that cluster documents based on common keywords, this technique can group documents that share no words in common as long as they are on the same subject. We compute the similarity between two documents as a function of the semantic similarity between the words and phrases in the documents. We model information from WordNet and DBPedia as a probabilistic graph that can be used to compute the similarity between two terms. We experimentally validate our algorithm on the Reuters-21578 benchmark, which contains 11,362 newswire stories that are grouped in 82 categories using human judgment. We apply the k-means clustering algorithm to group the documents using a similarity metric that is based on keyword matching and one that uses the probabilistic graph. We show that the second approach produces higher precision and recall, which corresponds to better alignment with the classification that was done by human experts

Creating a Probabilistic Graph for WordNet using Markov Logic Network

The paper shows how to create a probabilistic graph for WordNet. A node is created for every word and phrase in WordNet. An edge between two nodes is labeled with the probability that a user that is interested in the source concept will also be interested in the destination concept. For example, an edge with weight 0.3 between “canine” and “dog” indicates that there is a 30% probability that a user who searches for “canine” will be interested in results that contain the word “dog”. We refer to the graph as probabilistic because we enforce the constraint that the sum of the weights of all the edges that go out of a node add up to one. Structural (e.g., the word “canine” is a hypernym (i.e., kind of) of the word “dog”) and textual (e.g., the word “canine” appears in the textual definition of the word “dog”) data from WordNet is used to create a Markov logic network, that is, a set of first order formulas with probabilities. The Markov logic network is then used to compute the weights of the edges in the probabilistic graph. We experimentally validate the quality of the data in the probabilistic graph on two independent benchmarks: Miller and Charles and WordSimilarity-353

Creating a Probabilistic Model for WordNet

We present a probabilistic model for extracting and storing information from WordNet and the British National Corpus. We map the data into a directed probabilistic graph that can be used to compute the conditional probability between a pair of words from the English language. For example, the graph can be used to deduce that there is a 10% probability that someone who is interested in dogs is also interested in the word “canine”. We propose three ways for computing this probability, where the best results are achieved when performing multiple random walks in the graph. Unlike existing approaches that only process the structured data in WordNet, we process all available information, including natural language descriptions. The available evidence is expressed as simple Horn clauses with probabilities. It is then aggregated using a Markov Logic Network model to create the probabilistic graph. We experimentally validate the quality of the data on five different benchmarks that contain collections of pairs of words and their semantic similarity as determined by humans. In the experimental section, we show that our random walk algorithm with logarithmic distance metric produces higher correlation with the results of the human judgment on three of the five benchmarks and better overall average correlation than the current state-of-the-art algorithms

Fine-Tuning an Algorithm for Semantic Search Using a Similarity Graph

Given a set of documents and an input query that is expressed in a natural language, the problem of document search is retrieving the most relevant documents. Unlike most existing systems that perform document search based on keyword matching, we propose a method that considers the meaning of the words in the queries and documents. As a result, our algorithm can return documents that have no words in common with the input query as long as the documents are relevant. For example, a document that contains the words Ford , Chrysler and General Motors multiple times is surely relevant for the query car even if the word car never appears in the document. Our information retrieval algorithm is based on a similarity graph that contains the degree of semantic closeness between terms, where a term can be a word or a phrase. Since the algorithms that constructs the similarity graph takes as input a myriad of parameters, in this paper we fine-tune the part of the algorithm that constructs the Wikipedia part of the graph. Specifically, we experimentally fine-tune the algorithm on the Miller and Charles study benchmark that contains 30 pairs of terms and their similarity score as determined by human users. We then evaluate the performance of the fine-tuned algorithm on the Cranfield benchmark that contains 1400 documents and 225 natural language queries. The benchmark also contains the relevant documents for every query as determined by human judgment. The results show that the fine-tuned algorithm produces higher mean average precision (MAP) score than traditional keyword-based search algorithms because our algorithm considers not only the words and phrases in the query and documents, but also their meaning