Name Disambiguation Problem Research Articles

Network-based stochastic competitive learning approach to disambiguation in collaborative networks

Many patterns have been uncovered in complex systems through the application of concepts and methodologies of complex networks. Unfortunately, the validity and accuracy of the unveiled patterns are strongly dependent on the amount of unavoidable noise pervading the data, such as the presence of homonymous individuals in social networks. In the current paper, we investigate the problem of name disambiguation in collaborative networks, a task that plays a fundamental role on a myriad of scientific contexts. In special, we use an unsupervised technique which relies on a particle competition mechanism in a networked environment to detect the clusters. It has been shown that, in this kind of environment, the learning process can be improved because the network representation of data can capture topological features of the input data set. Specifically, in the proposed disambiguating model, a set of particles is randomly spawned into the nodes constituting the network. As time progresses, the particles employ a movement strategy composed of a probabilistic convex mixture of random and preferential walking policies. In the former, the walking rule exclusively depends on the topology of the network and is responsible for the exploratory behavior of the particles. In the latter, the walking rule depends both on the topology and the domination levels that the particles impose on the neighboring nodes. This type of behavior compels the particles to perform a defensive strategy, because it will force them to revisit nodes that are already dominated by them, rather than exploring rival territories. Computer simulations conducted on the networks extracted from the arXiv repository of preprint papers and also from other databases reveal the effectiveness of the model, which turned out to be more accurate than traditional clustering methods.

An ontological gazetteer and its application for place name disambiguation in text

AbstractThe volume of spatial information on the Web grows daily, both in the form of online maps and as references to places embedded in documents and pages. Considering the spatial information needs of users, it is often necessary to recognize, within a document’s text, the places to which it refers. This article presents a next-generation gazetteer, a toponymic dictionary which expands from the traditional cataloguing of place names and includes geographic elements such as spatial relationships, concepts and terms related to places. As such, we call it an OntoGazetteer, i.e., a gazetteer which also records semantic connections among places. The ontological gazetteer provides factual and semantic support to solving several common problems in geographic information retrieval. This paper presents the OntoGazetteer and demonstrates its applicability to a place name disambiguation problem. Along with other problem solutions to which the OntoGazetteer can contribute, we present a case study on recognizing and disambiguating place names within news sources.

Scalable clustering methods for the name disambiguation problem

When non-unique values are used as the identifier of entities, due to their homonym, confusion can occur. In particular, when (part of) “names” of entities are used as their identifier, the problem is often referred to as a name disambiguation problem, where goal is to sort out the erroneous entities due to name homonyms (e.g., If only last name is used as the identifier, one cannot distinguish “Masao Obama” from “Norio Obama”). In this paper, in particular, we study the scalability issue of the name disambiguation problem—when (1) a small number of entities with large contents or (2) a large number of entities get un-distinguishable due to homonyms. First, we carefully examine two of the state-of-the-art solutions to the name disambiguation problem and point out their limitations with respect to scalability. Then, we propose two scalable graph partitioning algorithms known as multi-level graph partitioning and multi-level graph partitioning and merging to solve the large-scale name disambiguation problem. Our claim is empirically validated via experimentation—our proposal shows orders of magnitude improvement in terms of performance while maintaining equivalent or reasonable accuracy compared to competing solutions.

Clustering web people search results using fuzzy ants

Person name queries often bring up web pages that correspond to individuals sharing the same name. The Web People Search (WePS) task consists of organizing search results for ambiguous person name queries into meaningful clusters, with each cluster referring to one individual. This paper presents a fuzzy ant based clustering approach for this multi-document person name disambiguation problem. The main advantage of fuzzy ant based clustering, a technique inspired by the behavior of ants clustering dead nestmates into piles, is that no specification of the number of output clusters is required. This makes the algorithm very well suited for the Web Person Disambiguation task, where we do not know in advance how many individuals each person name refers to. We compare our results with state-of-the-art partitional and hierarchical clustering approaches ( k-means and Agnes) and demonstrate favorable results. This is particularly interesting as the latter involve manual setting of a similarity threshold, or estimating the number of clusters in advance, while the fuzzy ant based clustering algorithm does not.

Utilization of external knowledge for personal name disambiguation

The amount of information on the World Wide Web (WWW) is increasing at an explosive rate, and the role of computer systems in processing such a huge amount of data has become crucial. In this paper, we focus on the name disambiguation problem when searching for people, because information about people is an important part of the web and improvements to personal information may benefit many web citizens. The name ambiguity problem occurs frequently when searching for people, because a name may be shared by several people. In this research, we use external knowledge while solving this problem, so that we can analyze information in web documents more easily. We collect web directories and use the latent Dirichlet allocation method to extract latent topics from web directories. The extracted topics are used to modify the search result documents so that important contexts that help to discriminate people can be recognized more easily. We carried out experiments with real web documents and verified the advantages of our approach over other disambiguation approaches that use the vector space model and named entity recognition methods.