Derry Tanti Wijaya

Understanding semantic change of words over centuries

In this paper, we propose to model and analyze changes that occur to an entity in terms of changes in the words that co-occur with the entity over time. We propose to do an in-depth analysis of how this co-occurrence changes over time, how the change influences the state (semantic, role) of the entity, and how the change may correspond to events occurring in the same period of time. We propose to identify clusters of topics surrounding the entity over time using Topics-Over-Time (TOT) and k-means clustering. We conduct this analysis on Google Books Ngram dataset. We show how clustering words that co-occur with an entity of interest in 5-grams can shed some lights to the nature of change that occurs to the entity and identify the period for which the change occurs. We find that the period identified by our model precisely coincides with events in the same period that correspond to the change that occurs.

Acquiring temporal constraints between relations

We consider the problem of automatically acquiring knowledge about the typical temporal orderings among relations (e.g., actedIn(person, film) typically occurs before wonPrize (film, award)), given only a database of known facts (relation instances) without time information, and a large document collection. Our approach is based on the conjecture that the narrative order of verb mentions within documents correlates with the temporal order of the relations they represent. We propose a family of algorithms based on this conjecture, utilizing a corpus of 890m dependency parsed sentences to obtain verbs that represent relations of interest, and utilizing Wikipedia documents to gather statistics on narrative order of verb mentions. Our proposed algorithm, GraphOrder, is a novel and scalable graph-based label propagation algorithm that takes transitivity of temporal order into account, as well as these statistics on narrative order of verb mentions. This algorithm achieves as high as 38.4% absolute improvement in F1 over a random baseline. Finally, we demonstrate the utility of this learned general knowledge about typical temporal orderings among relations, by showing that these temporal constraints can be successfully used by a joint inference framework to assign specific temporal scopes to individual facts.

PIDGIN: ontology alignment using web text as interlingua

The problem of aligning ontologies and database schemas across different knowledge bases and databases is fundamental to knowledge management problems, including the problem of integrating the disparate knowledge sources that form the semantic webs Linked Data [5]. We present a novel approach to this ontology alignment problem that employs a very large natural language text corpus as an interlingua to relate different knowledge bases (KBs). The result is a scalable and robust method (PIDGIN) that aligns relations and categories across different KBs by analyzing both (1) shared relation instances across these KBs, and (2) the verb phrases in the text instantiations of these relation instances. Experiments with PIDGIN demonstrate its superior performance when aligning ontologies across large existing KBs including NELL, Yago and Freebase. Furthermore, we show that in addition to aligning ontologies, PIDGIN can automatically learn from text, the verb phrases to identify relations, and can also type the arguments of relations of different KBs.

Clustering web documents using co‐citation, coupling, incoming, and outgoing hyperlinks: a comparative performance analysis of algorithms

Querying search engines with the keyword “jaguars” returns results as diverse as web sites about cars, computer games, attack planes, American football, and animals. More and more search engines offer options to organize query results by categories or, given a document, to return a list of links to topically related documents. While information retrieval traditionally defines similarity of documents in terms of contents, it seems natural to expect that the very structure of the Web carries important information about the topical similarity of documents. Here we study the role of a matrix constructed from weighted co‐citations (documents referenced by the same document), weighted couplings (documents referencing the same document), incoming, and outgoing links for the clustering of documents on the Web. We present and discuss three methods of clustering based on this matrix construction using three clustering algorithms, K‐means, Markov and Maximum Spanning Tree, respectively. Our main contribution is a cl...

Mapping Verbs in Different Languages to Knowledge Base Relations using Web Text as Interlingua.

In recent years many knowledge bases (KBs) have been constructed, yet there is not yet a verb resource that maps to these growing KB resources. A resource that maps verbs in different languages to KB relations would be useful for extracting facts from text into the KBs, and to aid alignment and integration of knowledge across different KBs and languages. Such a multi-lingual verb resource would also be useful for tasks such as machine translation and machine reading. In this paper, we present a scalable approach to automatically construct such a verb resource using a very large web text corpus as a kind of interlingua to relate verb phrases to KB relations. Given a text corpus in any language and any KB, it can produce a mapping of that language’s verb phrases to the KB relations. Experiments with the English NELL KB and ClueWeb corpus show that the learned English verb-to-relation mapping is effective for extracting relation instances from English text. When applied to a Portuguese NELL KB and a Portuguese text corpus, the same method automatically constructs a verb resource in Portuguese that is effective for extracting relation instances from Portuguese text.

A Spousal Relation Begins with a Deletion of engage and Ends with an Addition of divorce: Learning State Changing Verbs from Wikipedia Revision History

Learning to determine when the timevarying facts of a Knowledge Base (KB) have to be updated is a challenging task. We propose to learn state changing verbs from Wikipedia edit history. When a state-changing event, such as a marriage or death, happens to an entity, the infobox on the entity’s Wikipedia page usually gets updated. At the same time, the article text may be updated with verbs either being added or deleted to reflect the changes made to the infobox. We use Wikipedia edit history to distantly supervise a method for automatically learning verbs and state changes. Additionally, our method uses constraints to effectively map verbs to infobox changes. We observe in our experiments that when state-changing verbs are added or deleted from an entity’s Wikipedia page text, we can predict the entity’s infobox updates with 88% precision and 76% recall. One compelling application of our verbs is to incorporate them as triggers in methods for updating existing KBs, which are currently mostly static.

Journey to the centre of the star: various ways of finding star centers in star clustering

The Star algorithm is an effective and efficient algorithm for graph clustering. We propose a series of novel, yet simple, metrics for the selection of Star centers in the Star algorithm and its variants. We empirically study the performance of off-line, standard and extended, and on-line versions of the Star algorithm adapted to the various metrics and show that one of the proposed metrics outperforms all others in both effectiveness and efficiency of clustering. We empirically study the sensitivity of the metrics to the threshold value of the algorithm and show improvement with respect to this aspect too.

CTPs: Contextual Temporal Profiles for Time Scoping Facts using State Change Detection

Temporal scope adds a time dimension to facts in Knowledge Bases (KBs). These time scopes specify the time periods when a given fact was valid in real life. Without temporal scope, many facts are underspecified, reducing the usefulness of the data for upper level applications such as Question Answering. Existing methods for temporal scope inference and extraction still suffer from low accuracy. In this paper, we present a new method that leverages temporal profiles augmented with context— Contextual Temporal Profiles (CTPs) of entities. Through change patterns in an entity’s CTP, we model the entity’s state change brought about by real world events that happen to the entity (e.g, hired, fired, divorced, etc.). This leads to a new formulation of the temporal scoping problem as a state change detection problem. Our experiments show that this formulation of the problem, and the resulting solution are highly effective for inferring temporal scope of facts.

Clustering Vertices in Weighted Graphs

Clustering is the unsupervised process of discovering natural clusters so that objects within the same cluster are similar and objects from different clusters are dissimilar. In clustering, if similarity relations between objects are represented as a simple, weighted graph where objects are vertices and similarities between objects are weights of edges; clustering reduces to the problem of graph clustering. A natural notion of graph clustering is the separation of sparsely connected dense sub graphs from each other based on the notion of intra-cluster density vs. inter-cluster sparseness. In this chapter, we overview existing graph algorithms for clustering vertices in weighted graphs: Minimum Spanning Tree (MST) clustering, Markov clustering, and Star clustering. This includes the variants of Star clustering, MST clustering and Ricochet.