BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

Supervised and Unsupervised ML algorithms are widely used over graphs. They use the structural properties of the data to deliver effective results. It is known that the same information can be represented under various graph structures. Thus, these algorithms may be effective on some structural variations of the data and ineffective on others. One would like to have an algorithm that is effective and generalizes to all structural variations of a data graph. We define the concept of structural generalizability for algorithms over graphs. We focus on the problem of similarity search, which is a popular task and the building block of many ML algorithms on graphs, and propose a structurally generalizable similarity search algorithm. As this algorithm may require users to specify features in a rather complex language, we modify this algorithm so that it requires only simple guidance from the user. Our extensive empirical study show that our algorithms are structurally generalizable while being efficient and more effective than current algorithms.

It is known that annotating entities in unstructured and semistructured datasets by their concepts improves the effectiveness of answering queries over these datasets. Ideally, one would like to annotate entities of all relevant concepts in a dataset. However, it takes substantial time and computational resources to annotate concepts in large datasets and an organization may have sufficient resources to annotate only a subset of relevant concepts. Clearly, it would like to annotate a subset of concepts that provides the most effective answers to queries over the dataset. We propose a formal framework that quantifies the amount by which annotating entities of concepts from a taxonomy in a dataset improves the effectiveness of answering queries over the dataset. Because the problem is NP-hard, we propose an efficient approximation for the problem. Our extensive empirical studies validate our framework and show the accuracy and efficiency of our algorithm.

Finding similar entities is a fundamental problem in graph data analysis. Similarity search algorithms usually leverage the structural properties of the database to quantify the degree of similarity between entities. However, the same information can be represented in different structures and the structural properties observed over particular representations may not hold for the alternatives. These algorithms are effective on some representations and ineffective on others. We define the property of representation independence for similarity search algorithms as their robustness against transformations that modify the structure of databases but preserve the information content. We introduce a widespread group of such transformations called relationship reorganizing. We propose an algorithm called R-PathSim, which is provably robust under relationship reorganizing. Our empirical results show that current algorithms except R-PathSim are highly sensitive to the data representation and R-PathSim is as efficient and effective as other algorithms.

Graph analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Because these algorithms tend to be highly effective over some choices of structure, such as that of the databases used to validate them, but not so effective with others, graph analytics has largely remained the province of experts who can find the desired forms for these algorithms. We argue that in order to make graph analytics usable, we should develop systems that are effective over a wide range of choices of structural organizations. We demonstrate Universal-DB an entity similarity and proximity search system that returns the same answers for a query over a wide range of choices to represent the input database.

It is well established that extracting and annotating occurrences of entities in a collection of unstructured text documents with their concepts improves the effectiveness of answering queries over the collection. However, it is very resource intensive to create and maintain large annotated collections. Since the available resources of an enterprise are limited and/or its users may have urgent information needs, it may have to select only a subset of relevant concepts for extraction and annotation. We call this subset a conceptual design for the annotated collection. In this article, we introduce and formally define the problem of cost-effective conceptual design where, given a collection, a set of relevant concepts, and a fixed budget, one likes to find a conceptual design that most improves the effectiveness of answering queries over the collection. We provide efficient algorithms for special cases of the problem and prove it is generally NP-hard in the number of relevant concepts. We propose three efficient approximations to solve the problem: a greedy algorithm, an approximate popularity maximization (APM for short), and approximate annotation-benefit maximization (AAM for short). We show that, if there are no constraints regrading the overlap of concepts, APM is a fully polynomial time approximation scheme. We also prove that if the relevant concepts are mutually exclusive, the greedy algorithm delivers a constant approximation ratio if the concepts are equally costly, APM has a constant approximation ratio, and AAM is a fully polynomial-time approximation scheme. Our empirical results using a Wikipedia collection and a search engine query log validate the proposed formalization of the problem and show that APM and AAM efficiently compute conceptual designs. They also indicate that, in general, APM delivers the optimal conceptual designs if the relevant concepts are not mutually exclusive. Also, if the relevant concepts are mutually exclusive, the conceptual designs delivered by AAM improve the effectiveness of answering queries over the collection more than the solutions provided by APM.

It is well established that extracting and annotating occurrences of entities in a collection of unstructured text documents with their concepts improve the effectiveness of answering queries over the collection. However, it is very resource intensive to create and maintain large annotated collections. Since the available resources of an enterprise are limited and/or its users may have urgent information needs, it may have to select only a subset of relevant concepts for extraction and annotation. We call this subset a conceptual design for the annotated collection. In this paper, we introduce the problem of cost effective conceptual design, where given a collection, a set of relevant concepts, and a fixed budget, one likes to find a conceptual design that improves the effectiveness of answering queries over the collection the most. We prove that the problem is generally NP-hard in the number of relevant concepts and propose two efficient approximation algorithms to solve the problem: Approximate Popularity Maximization (APM for short) and Approximate Annotation-benefit Maximization (AAM for short). We show that if there is not any constraints regrading the overlap of concepts, APM is a fully polynomial time approximation scheme. We also prove that if the relevant concepts are mutually exclusive, APM has a constant approximation ratio and AAM is a fully polynomial time approximation scheme. Our empirical results using Wikipedia collection and a search engine query log validate the proposed formalization of the problem and show that APM and AAM efficiently compute conceptual designs. They also indicate that in general APM delivers the optimal conceptual designs if the relevant concepts are not mutually exclusive. Also, if the relevant concepts are mutually exclusive, the conceptual designs delivered by AAM improve the effectiveness of answering queries over the collection more than the solutions provided by APM.

Real-world databases often have extremely complex schemas. With thousands of entity types and relationships, each with a hundred or so attributes, it is extremely difficult for new users to explore the data and formulate queries. Schema free query interfaces (SFQIs) address this problem by allowing users with no knowledge of the schema to submit queries. We postulate that SFQIs should deliver the same answers when given alternative designs for the same underlying data set. In this paper, we introduce and formally define design independence, which captures this property for SFQIs. We establish a theoretical framework to measure the amount of design independence provided by an SFQI. We show that most current SFQIs provide a very limited degree of design independence. We also show that SFQIs based on the statistical properties of data can provide design independence when the changes in the schema do not introduce or remove redundancy in the data. We propose a novel XML SFQI called Duplication Aware Coherency Ranking (DA-CR) based on information-theoretic relationships among the data items in the database, and prove that DA-CR is design independent. Our extensive empirical study using three real-world data sets shows that the average case design independence of current SFQIs is considerably lower than that of DA-CR. We also show that the ranking quality of DA-CR is better than or equal to that of current SFQI methods.

Real-world databases often have extremely complex schemas. With thousands of entity types and relationships, each with a hundred or so attributes, it is extremely difficult for new users to explore the data and formulate queries. Schema free query interfaces (SFQIs) address this problem by allowing users with no knowledge of the schema to submit queries. We postulate that SFQIs should deliver the same answers when given alternative but equivalent schemas for the same underlying information. In this paper, we introduce and formally define design independence, which captures this property for SFQIs. We establish a theoretical framework to measure the amount of design independence provided by an SFQI. We show that most current SFQIs provide a very limited degree of design independence. We also show that SFQIs based on the statistical properties of data can provide design independence when the changes in the schema do not introduce or remove redundancy in the data. We propose a novel XML SFQI called Duplication Aware Coherency Ranking (DA-CR) based on information-theoretic relationships among the data items in the database, and prove that DA-CR is design independent. Our extensive empirical study using three real-world data sets shows that the average case design independence of current SFQIs is considerably lower than that of DA-CR. We also show that the ranking quality of DA-CR is better than or equal to that of current SFQI methods.